/ Introduction
WP3 will produce novel tools for computational journalism to produce quality generated content in terms of both trustworthiness and engagement as well as fact checking software. Central research questions are: How can we computationally produce unbiased, high-quality multi-modal content effectively? How can we analyse user-generated content accurately to generate more valuable insights?
Objective: We aim to develop solutions that produce verified and relevant content effectively while employing engaging narratives. We will collaborate closely with media production companies to integrate and test the methods and tools we develop in realistic production settings, thus increasing industry relevance. Our ultimate objective is to analyse user-generated and other media content with respect to quality and validity, extract data, information and knowledge from media content and provide this to algorithms that support (semi-)automated multi-modal content production.
/ Introduction
WP3 will produce novel tools for computational journalism to produce quality generated content in terms of both trustworthiness and engagement as well as fact checking software. Central research questions are: How can we computationally produce unbiased, high-quality multi-modal content effectively? How can we analyse user-generated content accurately to generate more valuable insights?
Objective: We aim to develop solutions that produce verified and relevant content effectively while employing engaging narratives. We will collaborate closely with media production companies to integrate and test the methods and tools we develop in realistic production settings, thus increasing industry relevance. Our ultimate objective is to analyse user-generated and other media content with respect to quality and validity, extract data, information and knowledge from media content and provide this to algorithms that support (semi-)automated multi-modal content production.
/ Introduction
WP3 will produce novel tools for computational journalism to produce quality generated content in terms of both trustworthiness and engagement as well as fact checking software. Central research questions are: How can we computationally produce unbiased, high-quality multi-modal content effectively? How can we analyse user-generated content accurately to generate more valuable insights?
Objective: We aim to develop solutions that produce verified and relevant content effectively while employing engaging narratives. We will collaborate closely with media production companies to integrate and test the methods and tools we develop in realistic production settings, thus increasing industry relevance. Our ultimate objective is to analyse user-generated and other media content with respect to quality and validity, extract data, information and knowledge from media content and provide this to algorithms that support (semi-)automated multi-modal content production.
/ People
Enrico Motta
Work Package Advisor & Key Researcher
The Open University
Read more
Eivind Throndsen
Work Package Industry leader
Schibsted
Yngve Lamo
Researcher
Høgskulen på Vestlandet
Read more
Sergej Stoppel
WolftTech
Lasse Lambrechts
BT
Morten Langfeldt Dahlback
Faktisk.no
Are Tverberg
Industry WP3 Co-leader
TV2 AS
/ Publications
2024
Sohail Ahmed Khan; Duc-Tien Dang-Nguyen
CLIPping the Deception: Adapting Vision-Language Models for Universal Deepfake Detection Conference
ACM International Conference on Multimedia Retrieval (ICMR), 2024.
@conference{CLIpingSohail24,
title = {CLIPping the Deception: Adapting Vision-Language Models for Universal Deepfake Detection},
author = {Sohail Ahmed Khan and Duc-Tien Dang-Nguyen},
url = {https://mediafutures.no/icmr24/},
year = {2024},
date = {2024-04-07},
booktitle = {ACM International Conference on Multimedia Retrieval (ICMR)},
journal = {ACM International Conference on Multimedia Retrieval (ICMR)},
abstract = {The recent advancements in Generative Adversarial Networks (GANs) and the emergence of Diffusion models have significantly streamlined the production of highly realistic and widely accessible synthetic content. As a result, there is a pressing need for effective general purpose detection mechanisms to mitigate the potential risks posed by deepfakes. In this paper, we explore the effectiveness of pre-trained vision-language models (VLMs) when paired with recent adaptation methods for universal deepfake detection. Following previous studies in this domain, we employ only a single dataset (ProGAN) in order to adapt CLIP for deepfake detection. However, in contrast to prior research, which rely solely on the visual part of CLIP while ignoring its textual component, our analysis reveals that retaining the text part is crucial. Consequently, the simple and lightweight Prompt Tuning based adaptation strategy that we employ outperforms the previous SOTA approach by 5.01% mAP and 6.61% accuracy while utilizing less than one third of the training data (200k images as compared to 720k). To assess the real-world applicability of our proposed models, we conduct a comprehensive evaluation across various scenarios. This involves rigorous testing on images sourced from 21 distinct datasets, including those generated by GANs-based, Diffusion-based and Commercial tools.},
keywords = {},
pubstate = {published},
tppubtype = {conference}
}
The recent advancements in Generative Adversarial Networks (GANs) and the emergence of Diffusion models have significantly streamlined the production of highly realistic and widely accessible synthetic content. As a result, there is a pressing need for effective general purpose detection mechanisms to mitigate the potential risks posed by deepfakes. In this paper, we explore the effectiveness of pre-trained vision-language models (VLMs) when paired with recent adaptation methods for universal deepfake detection. Following previous studies in this domain, we employ only a single dataset (ProGAN) in order to adapt CLIP for deepfake detection. However, in contrast to prior research, which rely solely on the visual part of CLIP while ignoring its textual component, our analysis reveals that retaining the text part is crucial. Consequently, the simple and lightweight Prompt Tuning based adaptation strategy that we employ outperforms the previous SOTA approach by 5.01% mAP and 6.61% accuracy while utilizing less than one third of the training data (200k images as compared to 720k). To assess the real-world applicability of our proposed models, we conduct a comprehensive evaluation across various scenarios. This involves rigorous testing on images sourced from 21 distinct datasets, including those generated by GANs-based, Diffusion-based and Commercial tools.
2023
Bahareh Fatemi; Fazle Rabbi; Andreas L. Opdahl
Evaluating the Effectiveness of GPT Large Language Model for News Classification in the IPTC News Ontology Journal Article
In: IEEE Access, 2023.
@article{GPTLangMo,
title = {Evaluating the Effectiveness of GPT Large Language Model for News Classification in the IPTC News Ontology},
author = {Bahareh Fatemi and Fazle Rabbi and Andreas L. Opdahl },
url = {https://mediafutures.no/evaluating_the_effectiveness_of_gpt_large_language_model_for_news_classification_in_the_iptc_news_ontology/},
year = {2023},
date = {2023-12-21},
journal = {IEEE Access},
abstract = {News classification plays a vital role in newsrooms, as it involves the time-consuming task
of categorizing news articles and requires domain knowledge. Effective news classification is essential
for categorizing and organizing a constant flow of information, serving as the foundation for subsequent
tasks, such as news aggregation, monitoring, filtering, and organization. The automation of this process can
significantly benefit newsrooms by saving time and resources. In this study, we explore the potential of the
GPT large language model in a zero-shot setting for multi-class classification of news articles within the
widely accepted International Press Telecommunications Council (IPTC) news ontology. The IPTC news
ontology provides a structured framework for categorizing news, facilitating the efficient organization and
retrieval of news content. By investigating the effectiveness of the GPT language model in this classification
task, we aimed to understand its capabilities and potential applications in the news domain. This study was
conducted as part of our ongoing research in the field of automated journalism.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
News classification plays a vital role in newsrooms, as it involves the time-consuming task
of categorizing news articles and requires domain knowledge. Effective news classification is essential
for categorizing and organizing a constant flow of information, serving as the foundation for subsequent
tasks, such as news aggregation, monitoring, filtering, and organization. The automation of this process can
significantly benefit newsrooms by saving time and resources. In this study, we explore the potential of the
GPT large language model in a zero-shot setting for multi-class classification of news articles within the
widely accepted International Press Telecommunications Council (IPTC) news ontology. The IPTC news
ontology provides a structured framework for categorizing news, facilitating the efficient organization and
retrieval of news content. By investigating the effectiveness of the GPT language model in this classification
task, we aimed to understand its capabilities and potential applications in the news domain. This study was
conducted as part of our ongoing research in the field of automated journalism.
of categorizing news articles and requires domain knowledge. Effective news classification is essential
for categorizing and organizing a constant flow of information, serving as the foundation for subsequent
tasks, such as news aggregation, monitoring, filtering, and organization. The automation of this process can
significantly benefit newsrooms by saving time and resources. In this study, we explore the potential of the
GPT large language model in a zero-shot setting for multi-class classification of news articles within the
widely accepted International Press Telecommunications Council (IPTC) news ontology. The IPTC news
ontology provides a structured framework for categorizing news, facilitating the efficient organization and
retrieval of news content. By investigating the effectiveness of the GPT language model in this classification
task, we aimed to understand its capabilities and potential applications in the news domain. This study was
conducted as part of our ongoing research in the field of automated journalism.
Sohail Ahmed Khan; Duc-Tien Dang-Nguyen
Deepfake Detection: Analysing Model Generalisation Across Architectures, Datasets and Pre-Training Paradigms Conference
IEEE Access, 2023.
@conference{deepfakede,
title = {Deepfake Detection: Analysing Model Generalisation Across Architectures, Datasets and Pre-Training Paradigms},
author = {Sohail Ahmed Khan and Duc-Tien Dang-Nguyen},
url = {https://mediafutures.no/ieee_access_2023___deepfake_comparative_analysis/},
year = {2023},
date = {2023-12-15},
booktitle = {IEEE Access},
abstract = {As deepfake technology gains traction, the need for reliable detection systems is crucial. Recent research has introduced various deep learning-based detection systems, yet they exhibit limitations in generalizing effectively across diverse data distributions that differ from the training data. Our study focuses on understanding the generalization challenges by exploring specific aspects such as deep learning model architecture, pre-training strategy and datasets. Through a comprehensive comparative analysis, we evaluate multiple supervised and self-supervised deep learning models for deepfake detection.
Specifically, we evaluate eight supervised deep learning architectures and two transformer-based models pre-trained using self-supervised strategies (DINO, CLIP) on four different deepfake detection benchmarks (FakeAVCeleb, CelebDF-V2, DFDC and FaceForensics++). Our analysis includes intra-dataset and inter-dataset evaluations, examining the best performing models, generalisation capabilities and impact of augmentations. We also investigate the trade-off between model size, efficiency and performance. Our main goal is to provide insights into the effectiveness of different deep learning architectures (transformers, CNNs), training strategies (supervised, self-supervised) and deepfake detection benchmarks. Through our extensive analysis, we established that Transformer models outperform CNN models in deepfake detection. Also, we show that FaceForensics++ and DFDC datasets equip models with comparably better generalisation capabilities, as compared to FakeAVCeleb and CelebDF-V2 datasets. Our analysis also show that image augmentations can be helpful in achieving better performance, at least for the Transformer models.},
keywords = {},
pubstate = {published},
tppubtype = {conference}
}
As deepfake technology gains traction, the need for reliable detection systems is crucial. Recent research has introduced various deep learning-based detection systems, yet they exhibit limitations in generalizing effectively across diverse data distributions that differ from the training data. Our study focuses on understanding the generalization challenges by exploring specific aspects such as deep learning model architecture, pre-training strategy and datasets. Through a comprehensive comparative analysis, we evaluate multiple supervised and self-supervised deep learning models for deepfake detection.
Specifically, we evaluate eight supervised deep learning architectures and two transformer-based models pre-trained using self-supervised strategies (DINO, CLIP) on four different deepfake detection benchmarks (FakeAVCeleb, CelebDF-V2, DFDC and FaceForensics++). Our analysis includes intra-dataset and inter-dataset evaluations, examining the best performing models, generalisation capabilities and impact of augmentations. We also investigate the trade-off between model size, efficiency and performance. Our main goal is to provide insights into the effectiveness of different deep learning architectures (transformers, CNNs), training strategies (supervised, self-supervised) and deepfake detection benchmarks. Through our extensive analysis, we established that Transformer models outperform CNN models in deepfake detection. Also, we show that FaceForensics++ and DFDC datasets equip models with comparably better generalisation capabilities, as compared to FakeAVCeleb and CelebDF-V2 datasets. Our analysis also show that image augmentations can be helpful in achieving better performance, at least for the Transformer models.
Specifically, we evaluate eight supervised deep learning architectures and two transformer-based models pre-trained using self-supervised strategies (DINO, CLIP) on four different deepfake detection benchmarks (FakeAVCeleb, CelebDF-V2, DFDC and FaceForensics++). Our analysis includes intra-dataset and inter-dataset evaluations, examining the best performing models, generalisation capabilities and impact of augmentations. We also investigate the trade-off between model size, efficiency and performance. Our main goal is to provide insights into the effectiveness of different deep learning architectures (transformers, CNNs), training strategies (supervised, self-supervised) and deepfake detection benchmarks. Through our extensive analysis, we established that Transformer models outperform CNN models in deepfake detection. Also, we show that FaceForensics++ and DFDC datasets equip models with comparably better generalisation capabilities, as compared to FakeAVCeleb and CelebDF-V2 datasets. Our analysis also show that image augmentations can be helpful in achieving better performance, at least for the Transformer models.
Fazle Rabbi; Bahareh Fatemi; Yngve Lamo; Andreas L. Opdahl
A model-based framework for NEWS content analysis Journal Article
In: 12th International Conference on Model-Based Software and Systems Engineering, 2023.
@article{modelBased23,
title = {A model-based framework for NEWS content analysis},
author = {Fazle Rabbi and Bahareh Fatemi and Yngve Lamo and Andreas L. Opdahl},
url = {https://mediafutures.no/news-content-analysis/},
year = {2023},
date = {2023-12-12},
urldate = {2023-12-12},
journal = {12th International Conference on Model-Based Software and Systems Engineering},
abstract = {News articles are published all over the world to cover important events. Journalists need to keep track of
ongoing events in a fair and accountable manner and analyze them for newsworthiness. It requires enormous
amount of time for journalists to process information coming from main stream news media, social media
from all over the world as well as policy and law circulated by governments and international organizations.
News articles published by different news providers may consist of subjectivity of the reporters due to the
influence of reporters’ backgrounds, world views and opinions. In today’s practice of journalism there is a
lack of computational methods to support journalists to investigate fairness and monitor and analyze large
massive information streams. In this paper we present a model based approach to analyze the perspectives of
news publishers and monitor the progression of news events from various perspective. The domain concepts
in the news domain such as the news events and their contextual information is represented across various
dimensions in a knowledge graph. We presented a multi dimensional comparative analysis method of news
events for analyzing news article variants and for uncovering underlying storylines. To show the applicability
of the proposed method in real life, we demonstrated a running example in this paper. The utilization of
a model-based approach ensures the adaptability of our proposed method for representing a wide array of
domain concepts within the news domain.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
News articles are published all over the world to cover important events. Journalists need to keep track of
ongoing events in a fair and accountable manner and analyze them for newsworthiness. It requires enormous
amount of time for journalists to process information coming from main stream news media, social media
from all over the world as well as policy and law circulated by governments and international organizations.
News articles published by different news providers may consist of subjectivity of the reporters due to the
influence of reporters’ backgrounds, world views and opinions. In today’s practice of journalism there is a
lack of computational methods to support journalists to investigate fairness and monitor and analyze large
massive information streams. In this paper we present a model based approach to analyze the perspectives of
news publishers and monitor the progression of news events from various perspective. The domain concepts
in the news domain such as the news events and their contextual information is represented across various
dimensions in a knowledge graph. We presented a multi dimensional comparative analysis method of news
events for analyzing news article variants and for uncovering underlying storylines. To show the applicability
of the proposed method in real life, we demonstrated a running example in this paper. The utilization of
a model-based approach ensures the adaptability of our proposed method for representing a wide array of
domain concepts within the news domain.
ongoing events in a fair and accountable manner and analyze them for newsworthiness. It requires enormous
amount of time for journalists to process information coming from main stream news media, social media
from all over the world as well as policy and law circulated by governments and international organizations.
News articles published by different news providers may consist of subjectivity of the reporters due to the
influence of reporters’ backgrounds, world views and opinions. In today’s practice of journalism there is a
lack of computational methods to support journalists to investigate fairness and monitor and analyze large
massive information streams. In this paper we present a model based approach to analyze the perspectives of
news publishers and monitor the progression of news events from various perspective. The domain concepts
in the news domain such as the news events and their contextual information is represented across various
dimensions in a knowledge graph. We presented a multi dimensional comparative analysis method of news
events for analyzing news article variants and for uncovering underlying storylines. To show the applicability
of the proposed method in real life, we demonstrated a running example in this paper. The utilization of
a model-based approach ensures the adaptability of our proposed method for representing a wide array of
domain concepts within the news domain.
Bjørnar Tessem; Are Tverberg; Njål Borch
The future technologies of journalism Journal Article
In: Procedia Computer Science, vol. 239, pp. 96-104, 2023.
@article{CENTERIS,
title = {The future technologies of journalism},
author = {Bjørnar Tessem and Are Tverberg and Njål Borch },
url = {https://mediafutures.no/centeris/},
year = {2023},
date = {2023-11-10},
urldate = {2023-11-10},
booktitle = {Centeris},
journal = {Procedia Computer Science},
volume = {239},
pages = {96-104},
abstract = {The practice of journalism has undergone many changes in the last few years, with changes in technology being the
main driver of these changes. We present a future study where we aim to get an understanding of what technologies
will become important for the journalist and further change the journalist’s workplace. The new technological
solutions will have to be implemented in the media houses’ information systems, and knowledge about what
technologies will have the greatest impact will influence IS strategies in the media house. In the study we
interviewed 16 experts on how they envision the future technologies of the journalist. We analyzed the interviews
with a qualitative research approach. Our analysis shows that technologies for multi-platform news production,
automated news content generation, cloud services for flexible production, content search, and content verification
are the most important in terms of needs and competitiveness.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
The practice of journalism has undergone many changes in the last few years, with changes in technology being the
main driver of these changes. We present a future study where we aim to get an understanding of what technologies
will become important for the journalist and further change the journalist’s workplace. The new technological
solutions will have to be implemented in the media houses’ information systems, and knowledge about what
technologies will have the greatest impact will influence IS strategies in the media house. In the study we
interviewed 16 experts on how they envision the future technologies of the journalist. We analyzed the interviews
with a qualitative research approach. Our analysis shows that technologies for multi-platform news production,
automated news content generation, cloud services for flexible production, content search, and content verification
are the most important in terms of needs and competitiveness.
main driver of these changes. We present a future study where we aim to get an understanding of what technologies
will become important for the journalist and further change the journalist’s workplace. The new technological
solutions will have to be implemented in the media houses’ information systems, and knowledge about what
technologies will have the greatest impact will influence IS strategies in the media house. In the study we
interviewed 16 experts on how they envision the future technologies of the journalist. We analyzed the interviews
with a qualitative research approach. Our analysis shows that technologies for multi-platform news production,
automated news content generation, cloud services for flexible production, content search, and content verification
are the most important in terms of needs and competitiveness.
Marc Gallofré Ocaña; Andreas L. Opdahl
A software reference architecture for journalistic knowledge Journal Article
In: Knowledge-based Systems, vol. 276, 2023.
@article{Ocana2023,
title = {A software reference architecture for journalistic knowledge },
author = {Marc Gallofré Ocaña and Andreas L. Opdahl },
url = {https://mediafutures.no/1-s2-0-s0950705123005002-main/},
year = {2023},
date = {2023-06-30},
urldate = {2023-06-30},
journal = {Knowledge-based Systems},
volume = {276},
abstract = {Newsrooms and journalists today rely on many different artificial-intelligence, big-data and knowledgebased systems to support efficient and high-quality journalism. However, making the different systems
work together remains a challenge, calling for new unified journalistic knowledge platforms. A software
reference architecture for journalistic knowledge platforms could help news organisations by capturing
tried-and-tested best practices and providing a generic blueprint for how their IT infrastructure should
evolve. To the best of our knowledge, no suitable architecture has been proposed in the literature.
Therefore, this article proposes a software reference architecture for integrating artificial intelligence
and knowledge bases to support journalists and newsrooms. The design of the proposed architecture
is grounded on the research literature and on our experiences with developing a series of prototypes
in collaboration with industry. Our aim is to make it easier for news organisations to evolve their
existing independent systems for news production towards integrated knowledge platforms and to
direct further research. Because journalists and newsrooms are early adopters of integrated knowledge
platforms, our proposal can hopefully also inform architectures in other domains with similar needs.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Newsrooms and journalists today rely on many different artificial-intelligence, big-data and knowledgebased systems to support efficient and high-quality journalism. However, making the different systems
work together remains a challenge, calling for new unified journalistic knowledge platforms. A software
reference architecture for journalistic knowledge platforms could help news organisations by capturing
tried-and-tested best practices and providing a generic blueprint for how their IT infrastructure should
evolve. To the best of our knowledge, no suitable architecture has been proposed in the literature.
Therefore, this article proposes a software reference architecture for integrating artificial intelligence
and knowledge bases to support journalists and newsrooms. The design of the proposed architecture
is grounded on the research literature and on our experiences with developing a series of prototypes
in collaboration with industry. Our aim is to make it easier for news organisations to evolve their
existing independent systems for news production towards integrated knowledge platforms and to
direct further research. Because journalists and newsrooms are early adopters of integrated knowledge
platforms, our proposal can hopefully also inform architectures in other domains with similar needs.
work together remains a challenge, calling for new unified journalistic knowledge platforms. A software
reference architecture for journalistic knowledge platforms could help news organisations by capturing
tried-and-tested best practices and providing a generic blueprint for how their IT infrastructure should
evolve. To the best of our knowledge, no suitable architecture has been proposed in the literature.
Therefore, this article proposes a software reference architecture for integrating artificial intelligence
and knowledge bases to support journalists and newsrooms. The design of the proposed architecture
is grounded on the research literature and on our experiences with developing a series of prototypes
in collaboration with industry. Our aim is to make it easier for news organisations to evolve their
existing independent systems for news production towards integrated knowledge platforms and to
direct further research. Because journalists and newsrooms are early adopters of integrated knowledge
platforms, our proposal can hopefully also inform architectures in other domains with similar needs.
Bjørnar Tessem; Marc Gallofré Ocaña; Andreas L. Opdahl
Construction of a relevance knowledge graph with application to the LOCAL news angle Proceedings Article
In: CEUR Workshop Proceedings (CEUR-WS.org) , 2023.
@inproceedings{Tessem2023,
title = {Construction of a relevance knowledge graph with application to the LOCAL news angle},
author = {Bjørnar Tessem and Marc Gallofré Ocaña and Andreas L. Opdahl},
url = {https://mediafutures.no/paper9/},
year = {2023},
date = {2023-06-15},
urldate = {2023-06-15},
booktitle = {CEUR Workshop Proceedings (CEUR-WS.org)
},
volume = {3431},
abstract = {News angles are approaches to journalism content often used to provide a way to present a new report
from an event. One particular type of news angle is the LOCAL news angle where a local news outlet
focuses on an event by emphasising a local connection. Knowledge graphs are most often used to
represent knowledge about a particular entity in the form of relationships to other entities. In this paper
we see how we can extract a knowledge sub graph containing entities and relevant relationships that are
connected to the locality of a news outlet. The purpose of this graph is to use it for automated journalism
or as an aid for the journalist to find local connections to an event, as well as how the local connection
relate to the event. We call such a graph a relevance knowledge graph. An algorithm for extracting such
a graph from a linked data source like DBpedia is presented and examples of the use of a relevance graph
in a LOCAL news angle context are provided.},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
News angles are approaches to journalism content often used to provide a way to present a new report
from an event. One particular type of news angle is the LOCAL news angle where a local news outlet
focuses on an event by emphasising a local connection. Knowledge graphs are most often used to
represent knowledge about a particular entity in the form of relationships to other entities. In this paper
we see how we can extract a knowledge sub graph containing entities and relevant relationships that are
connected to the locality of a news outlet. The purpose of this graph is to use it for automated journalism
or as an aid for the journalist to find local connections to an event, as well as how the local connection
relate to the event. We call such a graph a relevance knowledge graph. An algorithm for extracting such
a graph from a linked data source like DBpedia is presented and examples of the use of a relevance graph
in a LOCAL news angle context are provided.
from an event. One particular type of news angle is the LOCAL news angle where a local news outlet
focuses on an event by emphasising a local connection. Knowledge graphs are most often used to
represent knowledge about a particular entity in the form of relationships to other entities. In this paper
we see how we can extract a knowledge sub graph containing entities and relevant relationships that are
connected to the locality of a news outlet. The purpose of this graph is to use it for automated journalism
or as an aid for the journalist to find local connections to an event, as well as how the local connection
relate to the event. We call such a graph a relevance knowledge graph. An algorithm for extracting such
a graph from a linked data source like DBpedia is presented and examples of the use of a relevance graph
in a LOCAL news angle context are provided.
Bahareh Fatemi; Fazle Rabbi; Bjørnar Tessem
Fairness in automated data journalism systems Journal Article
In: NIKT: Norsk IKT-konferanse for forskning og utdanning, 2023.
@article{nokeyg,
title = {Fairness in automated data journalism systems},
author = {Bahareh Fatemi and Fazle Rabbi and Bjørnar Tessem},
url = {https://www.researchgate.net/publication/365127564_Fairness_in_automated_data_journalism_systems},
doi = {10.13140/RG.2.2.30374.19522},
year = {2023},
date = {2023-03-09},
urldate = {2023-03-09},
journal = {NIKT: Norsk IKT-konferanse for forskning og utdanning},
abstract = {Automated data journalism is an application of computing and artificial intelligence (AI) that aims to create stories from raw data, possibly in a variety of formats (such as visuals or text). Conventionally, a variety of methodologies and tools, including statistical software packages and data visualization tools have been used to generate stories from raw data. Artificial intelligence, and particularly machine learning techniques have recently been introduced because they can handle more complex data and scale more easily to larger datasets. However, AI techniques may raise a number of ethical concerns such as an unfair presentation which typically occurs due to bias. Stories that contains unfair presentation could be destructive at individual and societal levels; they could also damage the reputation of news providers. In this paper we study an existing framework of automated journalism and enhance the framework to make it aware of fairness concern. We present various steps of the framework where bias enters into the production of a story and address the causes and effects of different types of biases.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Automated data journalism is an application of computing and artificial intelligence (AI) that aims to create stories from raw data, possibly in a variety of formats (such as visuals or text). Conventionally, a variety of methodologies and tools, including statistical software packages and data visualization tools have been used to generate stories from raw data. Artificial intelligence, and particularly machine learning techniques have recently been introduced because they can handle more complex data and scale more easily to larger datasets. However, AI techniques may raise a number of ethical concerns such as an unfair presentation which typically occurs due to bias. Stories that contains unfair presentation could be destructive at individual and societal levels; they could also damage the reputation of news providers. In this paper we study an existing framework of automated journalism and enhance the framework to make it aware of fairness concern. We present various steps of the framework where bias enters into the production of a story and address the causes and effects of different types of biases.
Sohail Ahmed Khan; Ghazaal Sheikhi; Andreas L. Opdahl; Fazle Rabbi; Sergej Stoppel; Christoph Trattner; Duc-Tien Dang-Nguyen
Visual User-Generated Content Verification in Journalism: An Overview Journal Article
In: IEEE Access, 2023.
@article{KHAN2023,
title = {Visual User-Generated Content Verification in Journalism: An Overview},
author = {Sohail Ahmed Khan and Ghazaal Sheikhi and Andreas L. Opdahl and Fazle Rabbi and Sergej Stoppel and Christoph Trattner and Duc-Tien Dang-Nguyen},
url = {https://mediafutures.no/e0ret1-visual_user-generated_content_verification_in_journalism_an_overview/},
year = {2023},
date = {2023-01-16},
urldate = {2023-01-16},
journal = {IEEE Access},
abstract = {Over the past few years, social media has become an indispensable part of the news generation and dissemination cycle on the global stage. These digital channels along with the easy-to-use editing tools have unfortunately created a medium for spreading mis-/disinformation containing visual content. Media practitioners and fact-checkers continue to struggle with scrutinising and debunking visual user-generated content (UGC) quickly and thoroughly as verification of visual content requires a high level of expertise and could be exceedingly complex amid the existing computational tools employed in newsrooms. The aim of this study is to present a forward-looking perspective on how visual UGC verification in journalism can be transformed by multimedia forensics research. We elaborate on a comprehensive overview of the five elements of the UGC verification and propose multimedia forensics as the sixth element. In addition, different types of visual content forgeries and detection approaches proposed by the computer science research community are explained. Finally, a mapping of the available verification tools media practitioners rely on is created along with their limitations and future research directions to gain the confidence of media professionals in using multimedia forensics tools in their day-to-day routine.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Over the past few years, social media has become an indispensable part of the news generation and dissemination cycle on the global stage. These digital channels along with the easy-to-use editing tools have unfortunately created a medium for spreading mis-/disinformation containing visual content. Media practitioners and fact-checkers continue to struggle with scrutinising and debunking visual user-generated content (UGC) quickly and thoroughly as verification of visual content requires a high level of expertise and could be exceedingly complex amid the existing computational tools employed in newsrooms. The aim of this study is to present a forward-looking perspective on how visual UGC verification in journalism can be transformed by multimedia forensics research. We elaborate on a comprehensive overview of the five elements of the UGC verification and propose multimedia forensics as the sixth element. In addition, different types of visual content forgeries and detection approaches proposed by the computer science research community are explained. Finally, a mapping of the available verification tools media practitioners rely on is created along with their limitations and future research directions to gain the confidence of media professionals in using multimedia forensics tools in their day-to-day routine.
2021
Are Tverberg; Ingrid Agasøster; Mads Grønbæck; Marius Monsen; Robert Strand; Kristian Eikeland; Eivind Throndsen; Lars Westvang; Tove B. Knudsen; Eivind Fiskerud; Rune Skår; Sergej Stoppel; Arne Berven; Glenn Skare Pedersen; Paul Macklin; Kenneth Cuomo; Loek Vredenberg; Kristian Tolonen; Andreas L. Opdahl; Bjørnar Tessem; Csaba Veres; Duc-Tien Dang-Nguyen; Enrico Motta; Vinay Jayarama Setty
WP3 2021 M3.1 Report The industrial expectations to, needs from and wishes for the work package Technical Report
University of Bergen, MediaFutures 2021.
BibTeX | Links:
@techreport{Tverberg2021,
title = {WP3 2021 M3.1 Report The industrial expectations to, needs from and wishes for the work package},
author = {Are Tverberg and Ingrid Agasøster and Mads Grønbæck and Marius Monsen and Robert Strand and Kristian Eikeland and Eivind Throndsen and Lars Westvang and Tove B. Knudsen and Eivind Fiskerud and Rune Skår and Sergej Stoppel and Arne Berven and Glenn Skare Pedersen and Paul Macklin and Kenneth Cuomo and Loek Vredenberg and Kristian Tolonen and Andreas L. Opdahl and Bjørnar Tessem and Csaba Veres and Duc-Tien Dang-Nguyen and Enrico Motta and Vinay Jayarama Setty},
url = {https://mediafutures.no/wp3-q2-2021-m3-1-report-by-the-industrial-partners-final-2/},
year = {2021},
date = {2021-07-25},
urldate = {2021-07-25},
institution = {University of Bergen, MediaFutures},
keywords = {},
pubstate = {published},
tppubtype = {techreport}
}
Lars Nyre; Bjørnar Tessem
Automatiske nyhende Online
Dag og Tid 2021, visited: 26.03.2021.
BibTeX | Links:
@online{Tessem2021,
title = {Automatiske nyhende},
author = {Lars Nyre and Bjørnar Tessem},
url = {https://www.dagogtid.no/feature/automatiske-nyhende-6.3.20626.2396ffd8e5},
year = {2021},
date = {2021-03-26},
urldate = {2021-03-26},
journal = {Dag og Tid},
number = {12},
organization = {Dag og Tid},
keywords = {},
pubstate = {published},
tppubtype = {online}
}
2020
Arne Berven; Ole A. Christensen; Sindre Moldeklev; Andreas L. Opdahl; Kjetil A. Villanger
A knowledge-graph platform for newsrooms Journal Article
In: Computers in Industry, vol. 123, no. 103321, 2020, (Pre SFI).
@article{Berven2020,
title = {A knowledge-graph platform for newsrooms},
author = {Arne Berven and Ole A. Christensen and Sindre Moldeklev and Andreas L. Opdahl and Kjetil A. Villanger },
url = {https://reader.elsevier.com/reader/sd/pii/S0166361520305558?token=F8A21A513C97BFF598C2755575B3C89174B3D404E2EDDD23EC37966A2754ACA1700011EBBCF52ADE2845ADBC12D40041},
doi = {https://doi.org/10.1016/j.compind.2020.103321},
year = {2020},
date = {2020-12-01},
urldate = {2020-12-01},
journal = {Computers in Industry},
volume = {123},
number = {103321},
abstract = {Journalism is challenged by digitalisation and social media, resulting in lower subscription numbers and reduced advertising income. Information and communication techniques (ICT) offer new opportunities. Our research group is collaborating with a software developer of news production tools for the international market to explore how social, open, and other data sources can be leveraged for journalistic purposes. We have developed an architecture and prototype called News Hunter that uses knowledge graphs, natural-language processing (NLP), and machine learning (ML) together to support journalists. Our focus is on combining existing data sources and computation and storage techniques into a flexible architecture for news journalism. The paper presents News Hunter along with plans and possibilities for future work.},
note = {Pre SFI},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Journalism is challenged by digitalisation and social media, resulting in lower subscription numbers and reduced advertising income. Information and communication techniques (ICT) offer new opportunities. Our research group is collaborating with a software developer of news production tools for the international market to explore how social, open, and other data sources can be leveraged for journalistic purposes. We have developed an architecture and prototype called News Hunter that uses knowledge graphs, natural-language processing (NLP), and machine learning (ML) together to support journalists. Our focus is on combining existing data sources and computation and storage techniques into a flexible architecture for news journalism. The paper presents News Hunter along with plans and possibilities for future work.
Cathal Gurrin; Hideo Joho; Frank Hopfgartner; Liting Zhou; Rami Albatal; Graham Healy; Duc-Tien Dang-Nguyen
Experiments in Lifelog Organisation and Retrieval at NTCIR Book Chapter
In: Evaluating Information Retrieval and Access Tasks, Chapter 13, pp. 187-203, Springer, Singapore, 2020, (Pre SFI).
@inbook{Gurrin2020,
title = {Experiments in Lifelog Organisation and Retrieval at NTCIR},
author = {Cathal Gurrin and Hideo Joho and Frank Hopfgartner and Liting Zhou and Rami Albatal and Graham Healy and Duc-Tien Dang-Nguyen},
url = {https://www.researchgate.net/publication/344047066_Experiments_in_Lifelog_Organisation_and_Retrieval_at_NTCIR},
doi = {10.1007/978-981-15-5554-1_13},
year = {2020},
date = {2020-12-01},
urldate = {2020-12-01},
booktitle = {Evaluating Information Retrieval and Access Tasks},
pages = {187-203},
publisher = {Springer},
address = {Singapore},
chapter = {13},
abstract = {Lifelogging can be described as the process by which individuals use various software and hardware devices to gather large archives of multimodal personal data from multiple sources and store them in a personal data archive, called a lifelog. The Lifelog task at NTCIR was a comparative benchmarking exercise with the aim of encouraging research into the organisation and retrieval of data from multimodal lifelogs. The Lifelog task ran for over 4 years from NTCIR-12 until NTCIR-14 (2015.02–2019.06); it supported participants to submit to five subtasks, each tackling a different challenge related to lifelog retrieval. In this chapter, a motivation is given for the Lifelog task and a review of progress since NTCIR-12 is presented. Finally, the lessons learned and challenges within the domain of lifelog retrieval are presented.},
note = {Pre SFI},
keywords = {},
pubstate = {published},
tppubtype = {inbook}
}
Lifelogging can be described as the process by which individuals use various software and hardware devices to gather large archives of multimodal personal data from multiple sources and store them in a personal data archive, called a lifelog. The Lifelog task at NTCIR was a comparative benchmarking exercise with the aim of encouraging research into the organisation and retrieval of data from multimodal lifelogs. The Lifelog task ran for over 4 years from NTCIR-12 until NTCIR-14 (2015.02–2019.06); it supported participants to submit to five subtasks, each tackling a different challenge related to lifelog retrieval. In this chapter, a motivation is given for the Lifelog task and a review of progress since NTCIR-12 is presented. Finally, the lessons learned and challenges within the domain of lifelog retrieval are presented.
Danilo Dessì; Francesco Osborne; Diego Reforgiato Recupero; Davide Buscaldi; Enrico Motta; Harald Sack
AI-KG: an automatically generated knowledge graph of artificial intelligence Conference
nternational Semantic Web Conference, Springer, 2020, (Pre SFI).
@conference{Dessì2020,
title = {AI-KG: an automatically generated knowledge graph of artificial intelligence},
author = {Danilo Dessì and Francesco Osborne and Diego Reforgiato Recupero and Davide Buscaldi and Enrico Motta and Harald Sack},
url = {https://www.researchgate.net/publication/344991487_AI-KG_an_Automatically_Generated_Knowledge_Graph_of_Artificial_Intelligence},
year = {2020},
date = {2020-11-01},
booktitle = {nternational Semantic Web Conference},
pages = {127-143},
publisher = {Springer},
abstract = {Scientific knowledge has been traditionally disseminated and preserved through research articles published in journals, conference proceedings , and online archives. However, this article-centric paradigm has been often criticized for not allowing to automatically process, categorize , and reason on this knowledge. An alternative vision is to generate a semantically rich and interlinked description of the content of research publications. In this paper, we present the Artificial Intelligence Knowledge Graph (AI-KG), a large-scale automatically generated knowledge graph that describes 820K research entities. AI-KG includes about 14M RDF triples and 1.2M reified statements extracted from 333K research publications in the field of AI, and describes 5 types of entities (tasks, methods, metrics, materials, others) linked by 27 relations. AI-KG has been designed to support a variety of intelligent services for analyzing and making sense of research dynamics, supporting researchers in their daily job, and helping to inform decision-making in funding bodies and research policymakers. AI-KG has been generated by applying an automatic pipeline that extracts entities and relationships using three tools: DyGIE++, Stanford CoreNLP, and the CSO Classifier. It then integrates and filters the resulting triples using a combination of deep learning and semantic technologies in order to produce a high-quality knowledge graph. This pipeline was evaluated on a manually crafted gold standard, yielding competitive results. AI-KG is available under CC BY 4.0 and can be downloaded as a dump or queried via a SPARQL endpoint.},
note = {Pre SFI},
keywords = {},
pubstate = {published},
tppubtype = {conference}
}
Scientific knowledge has been traditionally disseminated and preserved through research articles published in journals, conference proceedings , and online archives. However, this article-centric paradigm has been often criticized for not allowing to automatically process, categorize , and reason on this knowledge. An alternative vision is to generate a semantically rich and interlinked description of the content of research publications. In this paper, we present the Artificial Intelligence Knowledge Graph (AI-KG), a large-scale automatically generated knowledge graph that describes 820K research entities. AI-KG includes about 14M RDF triples and 1.2M reified statements extracted from 333K research publications in the field of AI, and describes 5 types of entities (tasks, methods, metrics, materials, others) linked by 27 relations. AI-KG has been designed to support a variety of intelligent services for analyzing and making sense of research dynamics, supporting researchers in their daily job, and helping to inform decision-making in funding bodies and research policymakers. AI-KG has been generated by applying an automatic pipeline that extracts entities and relationships using three tools: DyGIE++, Stanford CoreNLP, and the CSO Classifier. It then integrates and filters the resulting triples using a combination of deep learning and semantic technologies in order to produce a high-quality knowledge graph. This pipeline was evaluated on a manually crafted gold standard, yielding competitive results. AI-KG is available under CC BY 4.0 and can be downloaded as a dump or queried via a SPARQL endpoint.
Vinay Setty; Erlend Rekve
Truth be told: Fake news detection using user reactions on reddit Journal Article
In: Proceedings of the 29th acm international conference on information knowledge management, pp. 3325–3328, 2020, (Pre SFI).
@article{Setty2020,
title = {Truth be told: Fake news detection using user reactions on reddit},
author = {Vinay Setty and Erlend Rekve},
url = {https://dl.acm.org/doi/pdf/10.1145/3340531.3417463},
doi = {https://doi.org/10.1145/3340531.3417463},
year = {2020},
date = {2020-10-01},
journal = {Proceedings of the 29th acm international conference on information knowledge management},
pages = {3325–3328},
abstract = {In this paper, we provide a large dataset for fake news detection using social media comments. The dataset consists of 12,597 claims (of which 63% are labelled as fake) from four different sources (Snopes, Poltifact, Emergent and Twitter). The novel part of the dataset is that it also includes over 662K social media discussion comments related to these claims from Reddit. We make this dataset public for the research community. In addition, for the task of fake news detection using social media comments, we provide a simple but strong baseline solution deep neural network model which beats several solutions in the literature.},
note = {Pre SFI},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
In this paper, we provide a large dataset for fake news detection using social media comments. The dataset consists of 12,597 claims (of which 63% are labelled as fake) from four different sources (Snopes, Poltifact, Emergent and Twitter). The novel part of the dataset is that it also includes over 662K social media discussion comments related to these claims from Reddit. We make this dataset public for the research community. In addition, for the task of fake news detection using social media comments, we provide a simple but strong baseline solution deep neural network model which beats several solutions in the literature.
Enrico Motta; Enrico Daga; Andreas L. Opdahl; Bjørnar Tessem
Analysis and design of computational news angles Journal Article
In: IEEE Access, vol. 8, pp. 120613-120626, 2020, (Pre SFI).
@article{Motta2020,
title = {Analysis and design of computational news angles},
author = {Enrico Motta and Enrico Daga and Andreas L. Opdahl and Bjørnar Tessem},
url = {https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9127417},
doi = {10.1109/ACCESS.2020.3005513},
year = {2020},
date = {2020-06-29},
urldate = {2020-06-29},
journal = {IEEE Access},
volume = {8},
pages = {120613-120626},
abstract = {A key skill for a journalist is the ability to assess the newsworthiness of an event or situation. To this purpose journalists often rely on news angles, conceptual criteria that are used both i) to assess whether something is newsworthy and also ii) to shape the structure of the resulting news item. As journalism becomes increasingly computer-supported, and more and more sources of potentially newsworthy data become available in real time, it makes sense to try and equip journalistic software tools with operational versions of news angles, so that, when searching this vast data space, these tools can both identify effectively the events most relevant to the target audience, and also link them to appropriate news angles. In this paper we analyse the notion of news angle and, in particular, we i) introduce a formal framework and data schema for representing news angles and related concepts and ii) carry out a preliminary analysis and characterization of a number of commonly used news angles, both in terms of our formal model and also in terms of the computational reasoning capabilities that are needed to apply them effectively to real-world scenarios. This study provides a stepping stone towards our ultimate goal of realizing a solution capable of exploiting a library of news angles to identify potentially newsworthy events in a large journalistic data space.},
note = {Pre SFI},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
A key skill for a journalist is the ability to assess the newsworthiness of an event or situation. To this purpose journalists often rely on news angles, conceptual criteria that are used both i) to assess whether something is newsworthy and also ii) to shape the structure of the resulting news item. As journalism becomes increasingly computer-supported, and more and more sources of potentially newsworthy data become available in real time, it makes sense to try and equip journalistic software tools with operational versions of news angles, so that, when searching this vast data space, these tools can both identify effectively the events most relevant to the target audience, and also link them to appropriate news angles. In this paper we analyse the notion of news angle and, in particular, we i) introduce a formal framework and data schema for representing news angles and related concepts and ii) carry out a preliminary analysis and characterization of a number of commonly used news angles, both in terms of our formal model and also in terms of the computational reasoning capabilities that are needed to apply them effectively to real-world scenarios. This study provides a stepping stone towards our ultimate goal of realizing a solution capable of exploiting a library of news angles to identify potentially newsworthy events in a large journalistic data space.
Andreas L. Opdahl; Bjørnar Tessem
Ontologies for finding journalistic angles Journal Article
In: Software and Systems Modeling, pp. 1-17, 2020, (Pre SFI).
@article{Ophdal2020,
title = {Ontologies for finding journalistic angles},
author = {Andreas L. Opdahl and Bjørnar Tessem},
url = {https://www.researchgate.net/publication/342132642_Ontologies_for_finding_journalistic_angles},
doi = {10.1007/s10270-020-00801-w},
year = {2020},
date = {2020-06-01},
urldate = {2020-06-01},
journal = {Software and Systems Modeling},
pages = {1-17},
abstract = {Journalism relies more and more on information and communication technology (ICT). ICT-based journalistic knowledge platforms continuously harvest potentially news-relevant information from the Internet and make it useful for journalists. Because information about the same event is available from different sources and formats vary widely, knowledge graphs are emerging as a preferred technology for integrating, enriching, and preparing information for journalistic use. The paper explores how journalistic knowledge graphs can be augmented with support for news angles, which can help journalists to detect newsworthy events and make them interesting for the intended audience. We argue that finding newsworthy angles on news-related information is an important example of a topical problem in information science: that of detecting interesting events and situations in big data sets and presenting those events and situations in interesting ways},
note = {Pre SFI},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Journalism relies more and more on information and communication technology (ICT). ICT-based journalistic knowledge platforms continuously harvest potentially news-relevant information from the Internet and make it useful for journalists. Because information about the same event is available from different sources and formats vary widely, knowledge graphs are emerging as a preferred technology for integrating, enriching, and preparing information for journalistic use. The paper explores how journalistic knowledge graphs can be augmented with support for news angles, which can help journalists to detect newsworthy events and make them interesting for the intended audience. We argue that finding newsworthy angles on news-related information is an important example of a topical problem in information science: that of detecting interesting events and situations in big data sets and presenting those events and situations in interesting ways
Bjarte Botnevik; Eirik Sakariassen; Vinay Setty
Brenda: Browser extension for fake news detection Journal Article
In: Proceedings of the 43rd international acm sigir conference on research and development in information retrieval, pp. 2117–2120, 2020, (Pre SFI).
@article{Botnevik2020,
title = {Brenda: Browser extension for fake news detection},
author = {Bjarte Botnevik and Eirik Sakariassen and Vinay Setty},
url = {https://arxiv.org/pdf/2005.13270.pdf},
doi = {10.1145/3397271.3401396},
year = {2020},
date = {2020-05-27},
journal = {Proceedings of the 43rd international acm sigir conference on research and development in information retrieval},
pages = { 2117–2120},
publisher = {Association for Computing Machinery},
abstract = {Misinformation such as fake news has drawn a lot of attention in recent years. It has serious consequences on society, politics and economy. This has lead to a rise of manually fact-checking websites such as Snopes and Politifact. However, the scale of misinformation limits their ability for verification. In this demonstration, we propose BRENDA a browser extension which can be used to automate the entire process of credibility assessments of false claims. Behind the scenes BRENDA uses a tested deep neural network architecture to automatically identify fact check worthy claims and classifies as well as presents the result along with evidence to the user. Since BRENDA is a browser extension, it facilities fast automated fact checking for the end user without having to leave the Webpage.},
note = {Pre SFI},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Misinformation such as fake news has drawn a lot of attention in recent years. It has serious consequences on society, politics and economy. This has lead to a rise of manually fact-checking websites such as Snopes and Politifact. However, the scale of misinformation limits their ability for verification. In this demonstration, we propose BRENDA a browser extension which can be used to automate the entire process of credibility assessments of false claims. Behind the scenes BRENDA uses a tested deep neural network architecture to automatically identify fact check worthy claims and classifies as well as presents the result along with evidence to the user. Since BRENDA is a browser extension, it facilities fast automated fact checking for the end user without having to leave the Webpage.
Agnese Chiatti; Enrico Motta; Enrico Daga
Towards a Framework for Visual Intelligence in Service Robotics: Epistemic Requirements and Gap Analysis Journal Article
In: Proceedings of the 17th International Conference on Principles of Knowledge Representation and Reasoning (KR 2020), pp. 905–916, 2020, (Pre SFI).
@article{Chiatti2020,
title = {Towards a Framework for Visual Intelligence in Service Robotics: Epistemic Requirements and Gap Analysis},
author = {Agnese Chiatti and Enrico Motta and Enrico Daga},
url = {https://arxiv.org/ftp/arxiv/papers/2003/2003.06171.pdf},
year = {2020},
date = {2020-03-13},
journal = {Proceedings of the 17th International Conference on Principles of Knowledge Representation and Reasoning (KR 2020)},
pages = {905–916},
abstract = {A key capability required by service robots operating in real-world, dynamic environments is that of Visual Intelligence, i.e., the ability to use their vision system, reasoning components and background knowledge to make sense of their environment. In this paper, we analyze the epistemic requirements for Visual Intelligence, both in a top-down fashion, using existing frameworks for human-like Visual Intelligence in the literature, and from the bottom up, based on the errors emerging from object recognition trials in a real-world robotic scenario. Finally, we use these requirements to evaluate current knowledge bases for Service Robotics and to identify gaps in the support they provide for Visual Intelligence. These gaps provide the basis of a research agenda for developing more effective knowledge representations for Visual Intelligence.},
note = {Pre SFI},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
A key capability required by service robots operating in real-world, dynamic environments is that of Visual Intelligence, i.e., the ability to use their vision system, reasoning components and background knowledge to make sense of their environment. In this paper, we analyze the epistemic requirements for Visual Intelligence, both in a top-down fashion, using existing frameworks for human-like Visual Intelligence in the literature, and from the bottom up, based on the errors emerging from object recognition trials in a real-world robotic scenario. Finally, we use these requirements to evaluate current knowledge bases for Service Robotics and to identify gaps in the support they provide for Visual Intelligence. These gaps provide the basis of a research agenda for developing more effective knowledge representations for Visual Intelligence.
Tareq Al-Moslmi; Marc Gallofré Ocaña; Andreas L. Opdahl; Csaba Veres
Named entity extraction for knowledge graphs: A literature overview Journal Article
In: IEEE Access, vol. 8, pp. 32862-32881, 2020, (Pre SFI).
@article{Al-Moslmi2020,
title = {Named entity extraction for knowledge graphs: A literature overview},
author = {Tareq Al-Moslmi and Marc Gallofré Ocaña and Andreas L. Opdahl and Csaba Veres
},
url = {https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8999622},
doi = {10.1109/ACCESS.2020.2973928},
year = {2020},
date = {2020-02-14},
urldate = {2020-02-14},
journal = {IEEE Access},
volume = {8},
pages = {32862-32881},
abstract = {An enormous amount of digital information is expressed as natural-language (NL) text that is not easily processable by computers. Knowledge Graphs (KG) offer a widely used format for representing information in computer-processable form. Natural Language Processing (NLP) is therefore needed for mining (or lifting) knowledge graphs from NL texts. A central part of the problem is to extract the named entities in the text. The paper presents an overview of recent advances in this area, covering: Named Entity Recognition (NER), Named Entity Disambiguation (NED), and Named Entity Linking (NEL). We comment that many approaches to NED and NEL are based on older approaches to NER and need to leverage the outputs of state-of-the-art NER systems. There is also a need for standard methods to evaluate and compare named-entity extraction approaches. We observe that NEL has recently moved from being stepwise and isolated into an integrated process along two dimensions: the first is that previously sequential steps are now being integrated into end-to-end processes, and the second is that entities that were previously analysed in isolation are now being lifted in each other's context. The current culmination of these trends are the deep-learning approaches that have recently reported promising results.},
note = {Pre SFI},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
An enormous amount of digital information is expressed as natural-language (NL) text that is not easily processable by computers. Knowledge Graphs (KG) offer a widely used format for representing information in computer-processable form. Natural Language Processing (NLP) is therefore needed for mining (or lifting) knowledge graphs from NL texts. A central part of the problem is to extract the named entities in the text. The paper presents an overview of recent advances in this area, covering: Named Entity Recognition (NER), Named Entity Disambiguation (NED), and Named Entity Linking (NEL). We comment that many approaches to NED and NEL are based on older approaches to NER and need to leverage the outputs of state-of-the-art NER systems. There is also a need for standard methods to evaluate and compare named-entity extraction approaches. We observe that NEL has recently moved from being stepwise and isolated into an integrated process along two dimensions: the first is that previously sequential steps are now being integrated into end-to-end processes, and the second is that entities that were previously analysed in isolation are now being lifted in each other's context. The current culmination of these trends are the deep-learning approaches that have recently reported promising results.
G. Boato; Duc-Tien Dang-Nguyen; F.G.B. De Natale
Morphological filter detector for image forensics applications Journal Article
In: IEEE Access, vol. 8, pp. 13549-13560, 2020, (Pre SFI).
@article{Boato2020,
title = {Morphological filter detector for image forensics applications},
author = {G. Boato and Duc-Tien Dang-Nguyen and F.G.B. De Natale},
url = {https://www.researchgate.net/publication/338524511_Morphological_Filter_Detector_for_Image_Forensics_Applications},
doi = {10.1109/ACCESS.2020.2965745},
year = {2020},
date = {2020-01-01},
urldate = {2020-01-01},
journal = {IEEE Access},
volume = {8},
pages = {13549-13560},
abstract = {Mathematical morphology provides a large set of powerful non-linear image operators, widely used for feature extraction, noise removal or image enhancement. Although morphological filters might be used to remove artifacts produced by image manipulations, both on binary and graylevel documents, little effort has been spent towards their forensic identification. In this paper we propose a non-trivial extension of a deterministic approach originally detecting erosion and dilation of binary images. The proposed approach operates on grayscale images and is robust to image compression and other typical attacks. When the image is attacked the method looses its deterministic nature and uses a properly trained SVM classifier, using the original detector as a feature extractor. Extensive tests demonstrate that the proposed method guarantees very high accuracy in filtering detection, providing 100% accuracy in discriminating the presence and the type of morphological filter in raw images of three different datasets. The achieved accuracy is also good after JPEG compression, equal or above 76.8% on all datasets for quality factors above 80. The proposed approach is also able to determine the adopted structuring element for moderate compression factors. Finally, it is robust against noise addition and it can distinguish morphological filter from other filters.},
note = {Pre SFI},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Mathematical morphology provides a large set of powerful non-linear image operators, widely used for feature extraction, noise removal or image enhancement. Although morphological filters might be used to remove artifacts produced by image manipulations, both on binary and graylevel documents, little effort has been spent towards their forensic identification. In this paper we propose a non-trivial extension of a deterministic approach originally detecting erosion and dilation of binary images. The proposed approach operates on grayscale images and is robust to image compression and other typical attacks. When the image is attacked the method looses its deterministic nature and uses a properly trained SVM classifier, using the original detector as a feature extractor. Extensive tests demonstrate that the proposed method guarantees very high accuracy in filtering detection, providing 100% accuracy in discriminating the presence and the type of morphological filter in raw images of three different datasets. The achieved accuracy is also good after JPEG compression, equal or above 76.8% on all datasets for quality factors above 80. The proposed approach is also able to determine the adopted structuring element for moderate compression factors. Finally, it is robust against noise addition and it can distinguish morphological filter from other filters.
2019
Bjørnar Tessem
Analogical News Angles from Text Similarity Conference
Artificial Intelligence XXXVI, no. 11927, Springer International Publishing, 2019, (Pre SFI).
@conference{Tessem2019b,
title = {Analogical News Angles from Text Similarity},
author = {Bjørnar Tessem},
editor = {Max Bramer and Miltos Petridis},
url = {https://bora.uib.no/bora-xmlui/bitstream/handle/1956/22473/SGAI_2019.pdf?sequence=4&isAllowed=y},
doi = {https://doi.org/10.1007/978-3-030-34885-4_35},
year = {2019},
date = {2019-11-19},
booktitle = {Artificial Intelligence XXXVI},
number = {11927},
pages = {449–455},
publisher = {Springer International Publishing},
abstract = {The paper presents an algorithm providing creativity support to journalists. It suggests analogical transfer of news angles from reports written about different events than the one the journalist is working on. The problem is formulated as a matching problem, where news reports with similar wordings from two events are matched, and unmatched reports from previous cases are selected as candidates for a news angle transfer. The approach is based on document similarity measures for matching and selection of transferable candidates. The algorithm has been tested on a small data set and show that the concept may be viable, but needs more exploration and evaluation in journalistic practice.},
note = {Pre SFI},
keywords = {},
pubstate = {published},
tppubtype = {conference}
}
The paper presents an algorithm providing creativity support to journalists. It suggests analogical transfer of news angles from reports written about different events than the one the journalist is working on. The problem is formulated as a matching problem, where news reports with similar wordings from two events are matched, and unmatched reports from previous cases are selected as candidates for a news angle transfer. The approach is based on document similarity measures for matching and selection of transferable candidates. The algorithm has been tested on a small data set and show that the concept may be viable, but needs more exploration and evaluation in journalistic practice.
Enrico Daga; Enrico Motta
Capturing themed evidence, a hybrid approach Conference
roceedings of the 10th International Conference on Knowledge Capture, 2019, (Pre SFI).
@conference{Daga2019,
title = {Capturing themed evidence, a hybrid approach},
author = {Enrico Daga and Enrico Motta},
url = {https://dl.acm.org/doi/pdf/10.1145/3360901.3364415},
year = {2019},
date = {2019-09-01},
booktitle = {roceedings of the 10th International Conference on Knowledge Capture},
pages = {93-100},
abstract = {The task of identifying pieces of evidence in texts is of fundamental importance in supporting qualitative studies in various domains, especially in the humanities. In this paper, we coin the expression themed evidence, to refer to (direct or indirect) traces of a fact or situation relevant to a theme of interest and study the problem of identifying them in texts. We devise a generic framework aimed at capturing themed evidence in texts based on a hybrid approach, combining statistical natural language processing, background knowledge, and Semantic Web technologies. The effectiveness of the method is demonstrated on a case study of a digital humanities database aimed at collecting and curating a repository of evidence of experiences of listening to music. Extensive experiments demonstrate that our hybrid approach outperforms alternative solutions. We also evidence its generality by testing it on a different use case in the digital humanities.},
note = {Pre SFI},
keywords = {},
pubstate = {published},
tppubtype = {conference}
}
The task of identifying pieces of evidence in texts is of fundamental importance in supporting qualitative studies in various domains, especially in the humanities. In this paper, we coin the expression themed evidence, to refer to (direct or indirect) traces of a fact or situation relevant to a theme of interest and study the problem of identifying them in texts. We devise a generic framework aimed at capturing themed evidence in texts based on a hybrid approach, combining statistical natural language processing, background knowledge, and Semantic Web technologies. The effectiveness of the method is demonstrated on a case study of a digital humanities database aimed at collecting and curating a repository of evidence of experiences of listening to music. Extensive experiments demonstrate that our hybrid approach outperforms alternative solutions. We also evidence its generality by testing it on a different use case in the digital humanities.
Rahul Mishra; Vinay Setty
Hierarchical attention networks to learn latent aspect embeddings for fake news detection Conference
Proceedings of the 2019 acm sigir international conference on theory of information retrieval, Association for Computing Machinery, New York, 2019, (Pre SFI).
@conference{Mishra2019,
title = {Hierarchical attention networks to learn latent aspect embeddings for fake news detection},
author = {Rahul Mishra and Vinay Setty},
url = {https://dl.acm.org/doi/pdf/10.1145/3341981.3344229},
doi = {10.1145/3341981.3344229},
year = {2019},
date = {2019-09-01},
booktitle = {Proceedings of the 2019 acm sigir international conference on theory of information retrieval},
pages = {197–204},
publisher = {Association for Computing Machinery},
address = {New York},
abstract = {Recently false claims and misinformation have become rampant in the web, affecting election outcomes, societies and economies. Consequently, fact checking websites such as snopes.com and politifact.com are becoming popular. However, these websites require expert analysis which is slow and not scalable. Many recent works try to solve these challenges using machine learning models trained on a variety of features and a rich lexicon or more recently, deep neural networks to avoid feature engineering. In this paper, we propose hierarchical deep attention networks to learn embeddings for various latent aspects of news. Contrary to existing solutions which only apply word-level self-attention, our model jointly learns the latent aspect embeddings for classifying false claims by applying hierarchical attention. Using several manually annotated high quality datasets such as Politifact, Snopes and Fever we show that these learned aspect embeddings are strong predictors of false claims. We show that latent aspect embeddings learned from attention mechanisms improve the accuracy of false claim detection by up to 13.5% in terms of Macro F1 compared to a state-of-the-art attention mechanism guided by claim-text DeClarE. We also extract and visualize the evidence from the external articles which supports or disproves the claims},
note = {Pre SFI},
keywords = {},
pubstate = {published},
tppubtype = {conference}
}
Recently false claims and misinformation have become rampant in the web, affecting election outcomes, societies and economies. Consequently, fact checking websites such as snopes.com and politifact.com are becoming popular. However, these websites require expert analysis which is slow and not scalable. Many recent works try to solve these challenges using machine learning models trained on a variety of features and a rich lexicon or more recently, deep neural networks to avoid feature engineering. In this paper, we propose hierarchical deep attention networks to learn embeddings for various latent aspects of news. Contrary to existing solutions which only apply word-level self-attention, our model jointly learns the latent aspect embeddings for classifying false claims by applying hierarchical attention. Using several manually annotated high quality datasets such as Politifact, Snopes and Fever we show that these learned aspect embeddings are strong predictors of false claims. We show that latent aspect embeddings learned from attention mechanisms improve the accuracy of false claim detection by up to 13.5% in terms of Macro F1 compared to a state-of-the-art attention mechanism guided by claim-text DeClarE. We also extract and visualize the evidence from the external articles which supports or disproves the claims
Bjørnar Tessem; Andreas L. Opdahl
Supporting Journalistic News Angles with Models and Analogies Conference
2019 13th International Conference on Research Challenges in Information Science (RCIS), 2019, (Pre SFI).
@conference{Tessem2019,
title = {Supporting Journalistic News Angles with Models and Analogies},
author = {Bjørnar Tessem and Andreas L. Opdahl},
url = {https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8877058},
doi = {10.1109/RCIS.2019.8877058},
year = {2019},
date = {2019-05-31},
urldate = {2019-05-31},
booktitle = { 2019 13th International Conference on Research Challenges in Information Science (RCIS)},
pages = {1-7},
abstract = {News angles are approaches to content presentation in journalism, where the journalist chooses which facts of an event to present. The News Angler project investigates how to computationally support the creation and selection of original news angles for a news event based on information from big data sources. At least two creative approaches are possible. One is to maintain a library of well-known news angles represented in a suitable modeling language, matching published reports on a current event to news angles in order to identify possible angles that have not yet been used. A second approach is not to represent news angles explicitly, instead matching the current event with previous events, and transferring angles from past to present reports by similarity and analogy. Both approaches are described and technologies needed to proceed in either direction are discussed.},
note = {Pre SFI},
keywords = {},
pubstate = {published},
tppubtype = {conference}
}
News angles are approaches to content presentation in journalism, where the journalist chooses which facts of an event to present. The News Angler project investigates how to computationally support the creation and selection of original news angles for a news event based on information from big data sources. At least two creative approaches are possible. One is to maintain a library of well-known news angles represented in a suitable modeling language, matching published reports on a current event to news angles in order to identify possible angles that have not yet been used. A second approach is not to represent news angles explicitly, instead matching the current event with previous events, and transferring angles from past to present reports by similarity and analogy. Both approaches are described and technologies needed to proceed in either direction are discussed.
2018
Marc Gallofré Ocaña; Lars Nyre; Andreas L. Opdahl; Bjørnar Tessem; Christoph Trattner; Csaba Veres
Towards a big data platform for news angles Workshop
Norwegian Big Data Symposium 2018, 2018, (Pre SFI).
@workshop{Ocaña2018,
title = {Towards a big data platform for news angles},
author = {Marc Gallofré Ocaña and Lars Nyre and Andreas L. Opdahl and Bjørnar Tessem and Christoph Trattner and Csaba Veres},
url = {https://www.researchgate.net/publication/332274562_Towards_a_Big_Data_Platform_for_News_Angles},
year = {2018},
date = {2018-11-01},
urldate = {2018-11-01},
booktitle = {Norwegian Big Data Symposium 2018},
abstract = {Finding good angles on news events is a central journalistic and editorial skill. As news work becomes increasingly computer-assisted and big-data based, journalistic tools therefore need to become better able to support news angles too. This paper outlines a big-data platform that is able to suggest appropriate angles on news events to journalists. We first clarify and discuss the central characteristics of news angles. We then proceed to outline a big-data architecture that can propose news angles. Important areas for further work include: representing news angles formally; identifying interesting and unexpected angles on unfolding events; and designing a big-data architecture that works on a global scale.
},
note = {Pre SFI},
keywords = {},
pubstate = {published},
tppubtype = {workshop}
}
Finding good angles on news events is a central journalistic and editorial skill. As news work becomes increasingly computer-assisted and big-data based, journalistic tools therefore need to become better able to support news angles too. This paper outlines a big-data platform that is able to suggest appropriate angles on news events to journalists. We first clarify and discuss the central characteristics of news angles. We then proceed to outline a big-data architecture that can propose news angles. Important areas for further work include: representing news angles formally; identifying interesting and unexpected angles on unfolding events; and designing a big-data architecture that works on a global scale.
Vinay Setty; Katja Hose
Neural embeddings for news events Conference
The 41st international acm sigir conference on research development in information retrieval, Association for Computing Machinery Association for Computing Machinery, New York, 2018, (Pre SFI).
@conference{Setty2018,
title = {Neural embeddings for news events},
author = {Vinay Setty and Katja Hose},
url = {https://dl.acm.org/doi/pdf/10.1145/3209978.3210136},
doi = {10.1145/3209978.3210136},
year = {2018},
date = {2018-06-01},
booktitle = {The 41st international acm sigir conference on research development in information retrieval},
pages = {1013–1016},
publisher = {Association for Computing Machinery},
address = {New York},
organization = {Association for Computing Machinery},
abstract = {Representation of news events as latent feature vectors is essential for several tasks, such as news recommendation, news event linking, etc. However, representations proposed in the past fail to capture the complex network structure of news events. In this paper we propose Event2Vec, a novel way to learn latent feature vectors for news events using a network. We use recently proposed network embedding techniques, which are proven to be very effective for various prediction tasks in networks. As events involve different classes of nodes, such as named entities, temporal information, etc, general purpose network embeddings are agnostic to event semantics. To address this problem, we propose biased random walks that are tailored to capture the neighborhoods of news events in event networks. We then show that these learned embeddings are effective for news event recommendation and news event linking tasks using strong baselines, such as vanilla Node2Vec, and other state-of-the-art graph-based event ranking techniques.},
note = {Pre SFI},
keywords = {},
pubstate = {published},
tppubtype = {conference}
}
Representation of news events as latent feature vectors is essential for several tasks, such as news recommendation, news event linking, etc. However, representations proposed in the past fail to capture the complex network structure of news events. In this paper we propose Event2Vec, a novel way to learn latent feature vectors for news events using a network. We use recently proposed network embedding techniques, which are proven to be very effective for various prediction tasks in networks. As events involve different classes of nodes, such as named entities, temporal information, etc, general purpose network embeddings are agnostic to event semantics. To address this problem, we propose biased random walks that are tailored to capture the neighborhoods of news events in event networks. We then show that these learned embeddings are effective for news event recommendation and news event linking tasks using strong baselines, such as vanilla Node2Vec, and other state-of-the-art graph-based event ranking techniques.
Duc-Tien Dang-Nguyen; Michael Alexander Riegler; Liting Zhou; Cathal Gurrin
Challenges and opportunities within personal life archives Conference
Proceedings of the 2018 ACM on International Conference on Multimedia Retrieval, 2018, (Pre SFI).
@conference{Nguyen2018,
title = {Challenges and opportunities within personal life archives},
author = {Duc-Tien Dang-Nguyen and Michael Alexander Riegler and Liting Zhou and Cathal Gurrin},
url = {https://www.researchgate.net/publication/325706506_Challenges_and_Opportunities_within_Personal_Life_Archives},
doi = {10.1145/3206025.3206040},
year = {2018},
date = {2018-06-01},
urldate = {2018-06-01},
booktitle = {Proceedings of the 2018 ACM on International Conference on Multimedia Retrieval},
pages = {335-343},
abstract = {Nowadays, almost everyone holds some form or other of a personal life archive. Automatically maintaining such an archive is an activity that is becoming increasingly common, however without automatic support the users will quickly be overwhelmed by the volume of data and will miss out on the potential benefits that lifelogs provide. In this paper we give an overview of the current status of lifelog research and propose a concept for exploring these archives. We motivate the need for new methodologies for indexing data, organizing content and supporting information access. Finally we will describe challenges to be addressed and give an overview of initial steps that have to be taken, to address the challenges of organising and searching personal life archives.},
note = {Pre SFI},
keywords = {},
pubstate = {published},
tppubtype = {conference}
}
Nowadays, almost everyone holds some form or other of a personal life archive. Automatically maintaining such an archive is an activity that is becoming increasingly common, however without automatic support the users will quickly be overwhelmed by the volume of data and will miss out on the potential benefits that lifelogs provide. In this paper we give an overview of the current status of lifelog research and propose a concept for exploring these archives. We motivate the need for new methodologies for indexing data, organizing content and supporting information access. Finally we will describe challenges to be addressed and give an overview of initial steps that have to be taken, to address the challenges of organising and searching personal life archives.
Angelo Antonio Salatino; Francesco Osborne; Enrico Motta
AUGUR: forecasting the emergence of new research topics Conference
Proceedings of the 18th ACM/IEEE on Joint Conference on Digital Libraries, 2018, (Pre SFI).
@conference{Salatino2018,
title = {AUGUR: forecasting the emergence of new research topics},
author = {Angelo Antonio Salatino and Francesco Osborne and Enrico Motta},
url = {https://www.researchgate.net/publication/325492541_AUGUR_Forecasting_the_Emergence_of_New_Research_Topics},
year = {2018},
date = {2018-05-01},
booktitle = {Proceedings of the 18th ACM/IEEE on Joint Conference on Digital Libraries},
pages = {303-312},
abstract = {Being able to rapidly recognise new research trends is strategic for many stakeholders, including universities, institutional funding bodies, academic publishers and companies. The literature presents several approaches to identifying the emergence of new research topics, which rely on the assumption that the topic is already exhibiting a certain degree of popularity and consistently referred to by a community of researchers. However, detecting the emergence of a new research area at an embryonic stage, i.e., before the topic has been consistently labelled by a community of researchers and associated with a number of publications, is still an open challenge. We address this issue by introducing Augur, a novel approach to the early detection of research topics. Augur analyses the diachronic relationships between research areas and is able to detect clusters of topics that exhibit dynamics correlated with the emergence of new research topics. Here we also present the Advanced Clique Percolation Method (ACPM), a new community detection algorithm developed specifically for supporting this task. Augur was evaluated on a gold standard of 1,408 debutant topics in the 2000-2011 interval and outperformed four alternative approaches in terms of both precision and recall.},
note = {Pre SFI},
keywords = {},
pubstate = {published},
tppubtype = {conference}
}
Being able to rapidly recognise new research trends is strategic for many stakeholders, including universities, institutional funding bodies, academic publishers and companies. The literature presents several approaches to identifying the emergence of new research topics, which rely on the assumption that the topic is already exhibiting a certain degree of popularity and consistently referred to by a community of researchers. However, detecting the emergence of a new research area at an embryonic stage, i.e., before the topic has been consistently labelled by a community of researchers and associated with a number of publications, is still an open challenge. We address this issue by introducing Augur, a novel approach to the early detection of research topics. Augur analyses the diachronic relationships between research areas and is able to detect clusters of topics that exhibit dynamics correlated with the emergence of new research topics. Here we also present the Advanced Clique Percolation Method (ACPM), a new community detection algorithm developed specifically for supporting this task. Augur was evaluated on a gold standard of 1,408 debutant topics in the 2000-2011 interval and outperformed four alternative approaches in terms of both precision and recall.
2017
Christina Boididou; Stuart Middleton; Zhiwei Jin; Symeon Papadopoulos; Duc-Tien Dang-Nguyen; G. Boato; Ioannis (Yiannis) Kompatsiaris
Verifying information with multimedia content on twitter: A comparative study of automated approaches Journal Article
In: Multimedia Tools and Applications, vol. 77, no. 12, pp. 15545-15571, 2017, (Pre SFI).
@article{Boididou2017,
title = {Verifying information with multimedia content on twitter: A comparative study of automated approaches},
author = {Christina Boididou and Stuart Middleton and Zhiwei Jin and Symeon Papadopoulos and Duc-Tien Dang-Nguyen and G. Boato and Ioannis (Yiannis) Kompatsiaris},
url = {https://www.researchgate.net/publication/319859894_Verifying_information_with_multimedia_content_on_twitter_A_comparative_study_of_automated_approaches},
doi = {10.1007/s11042-017-5132-9},
year = {2017},
date = {2017-09-01},
urldate = {2017-09-01},
journal = {Multimedia Tools and Applications},
volume = {77},
number = {12},
pages = {15545-15571},
abstract = {An increasing amount of posts on social media are used for dissem- inating news information and are accompanied by multimedia content. Such content may often be misleading or be digitally manipulated. More often than not, such pieces of content reach the front pages of major news outlets, having a detrimental eect on their credibility. To avoid such eects, there is profound need for automated methods that can help debunk and verify online content in very short time. To this end, we present a comparative study of three such methods that are catered for Twitter, a major social media platform used for news sharing. Those include: a) a method that uses textual patterns to extract
},
note = {Pre SFI},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
An increasing amount of posts on social media are used for dissem- inating news information and are accompanied by multimedia content. Such content may often be misleading or be digitally manipulated. More often than not, such pieces of content reach the front pages of major news outlets, having a detrimental eect on their credibility. To avoid such eects, there is profound need for automated methods that can help debunk and verify online content in very short time. To this end, we present a comparative study of three such methods that are catered for Twitter, a major social media platform used for news sharing. Those include: a) a method that uses textual patterns to extract
Duc-Tien Dang-Nguyen; Luca Piras; Giorgio Giacinto; G. Boato; Francesco G. B. DE Natale
Multimodal Retrieval with Diversification and Relevance Feedback for Tourist Attraction Images Journal Article
In: vol. 14, no. 4, pp. 1-24, 2017, (Pre SFI).
@article{Nguyen2017,
title = {Multimodal Retrieval with Diversification and Relevance Feedback for Tourist Attraction Images},
author = {Duc-Tien Dang-Nguyen and Luca Piras and Giorgio Giacinto and G. Boato and Francesco G. B. DE Natale
},
url = {https://www.researchgate.net/publication/319114515_Multimodal_Retrieval_with_Diversification_and_Relevance_Feedback_for_Tourist_Attraction_Images},
doi = {10.1145/3103613},
year = {2017},
date = {2017-08-01},
urldate = {2017-08-01},
booktitle = {ACM Transactions on Multimedia Computing, Communications, and Applications (TOMM)},
volume = {14},
number = {4},
pages = {1-24},
abstract = {In this article, we present a novel framework that can produce a visual description of a tourist attraction by choosing the most diverse pictures from community-contributed datasets, which describe different details of the queried location. The main strength of the proposed approach is its flexibility that permits us to filter out non-relevant images and to obtain a reliable set of diverse and relevant images by first clustering similar images according to their textual descriptions and their visual content and then extracting images from different clusters according to a measure of the user’s credibility. Clustering is based on a two-step process, where textual descriptions are used first and the clusters are then refined according to the visual features. The degree of diversification can be further increased by exploiting users’ judgments on the results produced by the proposed algorithm through a novel approach, where users not only provide a relevance feedback but also a diversity feedback. Experimental results performed on the MediaEval 2015 “Retrieving Diverse Social Images” dataset show that the proposed framework can achieve very good performance both in the case of automatic retrieval of diverse images and in the case of the exploitation of the users’ feedback. The effectiveness of the proposed approach has been also confirmed by a small case study involving a number of real users.},
note = {Pre SFI},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
In this article, we present a novel framework that can produce a visual description of a tourist attraction by choosing the most diverse pictures from community-contributed datasets, which describe different details of the queried location. The main strength of the proposed approach is its flexibility that permits us to filter out non-relevant images and to obtain a reliable set of diverse and relevant images by first clustering similar images according to their textual descriptions and their visual content and then extracting images from different clusters according to a measure of the user’s credibility. Clustering is based on a two-step process, where textual descriptions are used first and the clusters are then refined according to the visual features. The degree of diversification can be further increased by exploiting users’ judgments on the results produced by the proposed algorithm through a novel approach, where users not only provide a relevance feedback but also a diversity feedback. Experimental results performed on the MediaEval 2015 “Retrieving Diverse Social Images” dataset show that the proposed framework can achieve very good performance both in the case of automatic retrieval of diverse images and in the case of the exploitation of the users’ feedback. The effectiveness of the proposed approach has been also confirmed by a small case study involving a number of real users.
Vinay Setty; Abhijit Anand; Arunav Mishra; Avishek Anand
Modeling event importance for ranking daily news events Conference
Proceedings of the tenth acm international conference on web search and data mining, Association for Computing Machinery New York, 2017, (Pre SFI).
@conference{Setty2017,
title = {Modeling event importance for ranking daily news events},
author = {Vinay Setty and Abhijit Anand and Arunav Mishra and Avishek Anand},
url = {https://dl.acm.org/doi/pdf/10.1145/3018661.3018728},
doi = {10.1145/3018661.3018728},
year = {2017},
date = {2017-02-01},
booktitle = {Proceedings of the tenth acm international conference on web search and data mining},
pages = {231–240},
address = {New York},
organization = {Association for Computing Machinery},
abstract = {We deal with the problem of ranking news events on a daily basis for large news corpora, an essential building block for news aggregation. News ranking has been addressed in the literature before but with individual news articles as the unit of ranking. However, estimating event importance accurately requires models to quantify current day event importance as well as its significance in the historical context. Consequently, in this paper we show that a cluster of news articles representing an event is a better unit of ranking as it provides an improved estimation of popularity, source diversity and authority cues. In addition, events facilitate quantifying their historical significance by linking them with long-running topics and recent chain of events. Our main contribution in this paper is to provide effective models for improved news event ranking.
To this end, we propose novel event mining and feature generation approaches for improving estimates of event importance. Finally, we conduct extensive evaluation of our approaches on two large real-world news corpora each of which span for more than a year with a large volume of up to tens of thousands of daily news articles. Our evaluations are large-scale and based on a clean human curated ground-truth from Wikipedia Current Events Portal. Experimental comparison with a state-of-the-art news ranking technique based on language models demonstrates the effectiveness of our approach.},
note = {Pre SFI},
keywords = {},
pubstate = {published},
tppubtype = {conference}
}
We deal with the problem of ranking news events on a daily basis for large news corpora, an essential building block for news aggregation. News ranking has been addressed in the literature before but with individual news articles as the unit of ranking. However, estimating event importance accurately requires models to quantify current day event importance as well as its significance in the historical context. Consequently, in this paper we show that a cluster of news articles representing an event is a better unit of ranking as it provides an improved estimation of popularity, source diversity and authority cues. In addition, events facilitate quantifying their historical significance by linking them with long-running topics and recent chain of events. Our main contribution in this paper is to provide effective models for improved news event ranking.
To this end, we propose novel event mining and feature generation approaches for improving estimates of event importance. Finally, we conduct extensive evaluation of our approaches on two large real-world news corpora each of which span for more than a year with a large volume of up to tens of thousands of daily news articles. Our evaluations are large-scale and based on a clean human curated ground-truth from Wikipedia Current Events Portal. Experimental comparison with a state-of-the-art news ranking technique based on language models demonstrates the effectiveness of our approach.
To this end, we propose novel event mining and feature generation approaches for improving estimates of event importance. Finally, we conduct extensive evaluation of our approaches on two large real-world news corpora each of which span for more than a year with a large volume of up to tens of thousands of daily news articles. Our evaluations are large-scale and based on a clean human curated ground-truth from Wikipedia Current Events Portal. Experimental comparison with a state-of-the-art news ranking technique based on language models demonstrates the effectiveness of our approach.
Lars Nyre; Joao Ribeiro; Bjørnar Tessem
Business models for academic prototypes: A new approach to media innovation Journal Article
In: he Journal of Media Innovations, vol. 4, no. 2, pp. 4-19, 2017, (Pre SFI).
@article{Nyre2017,
title = {Business models for academic prototypes: A new approach to media innovation},
author = {Lars Nyre and Joao Ribeiro and Bjørnar Tessem},
url = {https://journals.uio.no/TJMI/article/view/2616/5101},
doi = {https://doi.org/10.5617/jomi.v4i2.2616},
year = {2017},
date = {2017-01-18},
journal = {he Journal of Media Innovations},
volume = {4},
number = {2},
pages = {4-19},
abstract = {This article introduces the concept of academic prototypes, and shows how they can lead to technological innovation in journalism. We propose an innovation method that transforms a value-oriented academic prototype into a market-oriented journalistic service. The principles for product development presented here are based on the lean startup method as well as business model canvassing. A prototype scenario shows how the locative information app PediaCloud could be transformed into a locative news service for a regional newspaper in Western Norway. Ideally, the academic prototype will be transformed into a novel and engaging way of reading news stories, and a profitable solution for the newspaper. Realistically, the team will have acquired empirical validation of the business model's strong and weak points. In the conclusion, we summarize the utility of the approach for validated learning, and make recommendations for further research on innovation with academic prototypes.},
note = {Pre SFI},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
This article introduces the concept of academic prototypes, and shows how they can lead to technological innovation in journalism. We propose an innovation method that transforms a value-oriented academic prototype into a market-oriented journalistic service. The principles for product development presented here are based on the lean startup method as well as business model canvassing. A prototype scenario shows how the locative information app PediaCloud could be transformed into a locative news service for a regional newspaper in Western Norway. Ideally, the academic prototype will be transformed into a novel and engaging way of reading news stories, and a profitable solution for the newspaper. Realistically, the team will have acquired empirical validation of the business model's strong and weak points. In the conclusion, we summarize the utility of the approach for validated learning, and make recommendations for further research on innovation with academic prototypes.
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