Bjørnar Tessem
Work Package Leader & Task Leader
University of Bergen
2024
Tarekegn, Adane Nega; Rabbi, Fazle; Tessem, Bjørnar
Large Language Model Enhanced Clustering for News Event Detection Conference Forthcoming
AIMEDIA : AI-based Media Disruption and Transformation, Forthcoming.
@conference{newseventdec24,
title = {Large Language Model Enhanced Clustering for News Event Detection},
author = {Adane Nega Tarekegn and Fazle Rabbi and Bjørnar Tessem},
url = {https://mediafutures.no/1_aimedia_paper_cr_version-1/},
year = {2024},
date = {2024-09-02},
booktitle = {AIMEDIA : AI-based Media Disruption and Transformation},
abstract = {The news landscape is continuously evolving, with an ever-increasing volume of information from around the world. Automated event detection within this vast data repository is crucial for monitoring, identifying, and categorizing significant news occurrences across diverse platforms. This paper presents an event detection framework that leverages Large Language Models (LLMs) combined with clustering analysis to detect news events from the Global Database of Events, Language, and Tone (GDELT). The framework enhances event clustering through both pre-event detection tasks (keyword extraction and text embedding) and post-event detection tasks (event summarization and topic labeling). We also evaluate the impact of various textual embeddings on the quality of clustering outcomes, ensuring robust news categorization. Additionally, we introduce a novel Cluster Stability Assessment Index (CSAI) to assess the validity and robustness of clustering results. CSAI utilizes latent feature vectors to provide a new way of measuring clustering quality. Our experiments indicate that combining LLM embeddings with clustering algorithms yields the best results, demonstrating greater robustness in terms of CSAI scores. Moreover, post-event detection tasks generate meaningful insights, facilitating effective interpretation of event clustering results. Overall, our findings indicate that the proposed framework offers valuable insights and could enhance the accuracy and depth of news reporting.},
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The news landscape is continuously evolving, with an ever-increasing volume of information from around the world. Automated event detection within this vast data repository is crucial for monitoring, identifying, and categorizing significant news occurrences across diverse platforms. This paper presents an event detection framework that leverages Large Language Models (LLMs) combined with clustering analysis to detect news events from the Global Database of Events, Language, and Tone (GDELT). The framework enhances event clustering through both pre-event detection tasks (keyword extraction and text embedding) and post-event detection tasks (event summarization and topic labeling). We also evaluate the impact of various textual embeddings on the quality of clustering outcomes, ensuring robust news categorization. Additionally, we introduce a novel Cluster Stability Assessment Index (CSAI) to assess the validity and robustness of clustering results. CSAI utilizes latent feature vectors to provide a new way of measuring clustering quality. Our experiments indicate that combining LLM embeddings with clustering algorithms yields the best results, demonstrating greater robustness in terms of CSAI scores. Moreover, post-event detection tasks generate meaningful insights, facilitating effective interpretation of event clustering results. Overall, our findings indicate that the proposed framework offers valuable insights and could enhance the accuracy and depth of news reporting.
Ashrafi, Aida; Tessem, Bjørnar; Enberg, Katja
Analysing Unlabeled Data with Randomness and Noise: The Case of Fishery Catch Reports Conference
14th Scandinavian Conference on Artificial Intelligence SCAI 2024, 2024.
@conference{fisherycatch24,
title = {Analysing Unlabeled Data with Randomness and Noise: The Case of Fishery Catch Reports},
author = {Aida Ashrafi and Bjørnar Tessem and Katja Enberg},
url = {https://mediafutures.no/analysing_unlabeled_data_with_randomness_and_noise/},
year = {2024},
date = {2024-06-10},
booktitle = {14th Scandinavian Conference on Artificial Intelligence SCAI 2024},
abstract = {Detecting violations within fishing activity reports is crucial for ensuring the sustainable utilization of fish resources, and employing machine learning methods holds promise for uncovering hidden patterns within this complex dataset. Given that these violations are infrequent occurrences, as fishermen generally adhere to regulations, identifying them becomes akin to an anomaly outlier detection task. Since labeled data distinguishing between normal and anomalous instances is not available for catch reports from Norwegian waters, we have opted for more conventional approaches, such as clustering methods, to identify potential clusters and outliers. Moreover, the catch reports inherently exhibit randomness and noise due to environmental factors and potential errors made by fishermen during report registration which complicates the processes of scaling, clustering, and anomaly detection. Through experimentation with various scaling and clustering techniques, we have observed that many of these methods tend to group the data based on the species caught, exhibiting a high level of agreement in cluster formation, indicating the stability of the clusters. Anomaly detection methods, however, yield varying potential outliers as it is a more challenging task.},
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Detecting violations within fishing activity reports is crucial for ensuring the sustainable utilization of fish resources, and employing machine learning methods holds promise for uncovering hidden patterns within this complex dataset. Given that these violations are infrequent occurrences, as fishermen generally adhere to regulations, identifying them becomes akin to an anomaly outlier detection task. Since labeled data distinguishing between normal and anomalous instances is not available for catch reports from Norwegian waters, we have opted for more conventional approaches, such as clustering methods, to identify potential clusters and outliers. Moreover, the catch reports inherently exhibit randomness and noise due to environmental factors and potential errors made by fishermen during report registration which complicates the processes of scaling, clustering, and anomaly detection. Through experimentation with various scaling and clustering techniques, we have observed that many of these methods tend to group the data based on the species caught, exhibiting a high level of agreement in cluster formation, indicating the stability of the clusters. Anomaly detection methods, however, yield varying potential outliers as it is a more challenging task.
2023
Tessem, Bjørnar; Tverberg, Are; Borch, Njål
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.
Tessem, Bjørnar; Ocaña, Marc Gallofré; Opdahl, Andreas L.
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.},
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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.
Opdahl, Andreas L.; Tessem, Bjørnar; Dang-Nguyen, Duc-Tien; Motta, Enrico; Setty, Vinay; Throndsen, Eivind; Tverberg, Are; Trattner, Christoph
Trustworthy Journalism Through AI Journal Article
In: Data & Knowledge Engineering (DKE), Elsevier, 2023.
@article{Opdahl2023,
title = {Trustworthy Journalism Through AI},
author = {Andreas L. Opdahl and Bjørnar Tessem and Duc-Tien Dang-Nguyen and Enrico Motta and Vinay Setty and Eivind Throndsen and Are Tverberg and Christoph Trattner},
url = {https://mediafutures.no/1-s2-0-s0169023x23000423-main/},
year = {2023},
date = {2023-04-29},
urldate = {2023-04-29},
journal = {Data & Knowledge Engineering (DKE), Elsevier},
abstract = {Quality journalism has become more important than ever due to the need for quality and trustworthy media outlets that can provide accurate information to the public and help to address and counterbalance the wide and rapid spread of disinformation. At the same time, quality journalism is under pressure due to loss of revenue and competition from alternative information providers. This vision paper discusses how recent advances in Artificial Intelligence (AI), and in Machine Learning (ML) in particular, can be harnessed to support efficient production of high-quality journalism. From a news consumer perspective, the key parameter here concerns the degree of trust that is engendered by quality news production. For this reason, the paper will discuss how AI techniques can be applied to all aspects of news, at all stages of its production cycle, to increase trust.},
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pubstate = {published},
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Quality journalism has become more important than ever due to the need for quality and trustworthy media outlets that can provide accurate information to the public and help to address and counterbalance the wide and rapid spread of disinformation. At the same time, quality journalism is under pressure due to loss of revenue and competition from alternative information providers. This vision paper discusses how recent advances in Artificial Intelligence (AI), and in Machine Learning (ML) in particular, can be harnessed to support efficient production of high-quality journalism. From a news consumer perspective, the key parameter here concerns the degree of trust that is engendered by quality news production. For this reason, the paper will discuss how AI techniques can be applied to all aspects of news, at all stages of its production cycle, to increase trust.
Fatemi, Bahareh; Rabbi, Fazle; Tessem, Bjørnar
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}
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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.
2022
Tessem, Bjørnar; Nyre, Lars; Mulholland, Paul
Deep Learning to Encourage Citizen Involvement in Local Journalism Book Chapter
In: Mari K. Niemi Ville J. E. Manninen, Anthony Ridge-Newman (Ed.): Chapter 3, pp. 211-226, Palgrave Macmillan Cham, 2022.
@inbook{Tessem2022,
title = {Deep Learning to Encourage Citizen Involvement in Local Journalism},
author = {Bjørnar Tessem and Lars Nyre and Paul Mulholland},
editor = {Ville J. E. Manninen, Mari K. Niemi, Anthony Ridge-Newman},
url = {https://link.springer.com/chapter/10.1007/978-3-030-95073-6_14},
doi = {https://doi.org/10.1007/978-3-030-95073-6_14},
year = {2022},
date = {2022-05-05},
urldate = {2022-05-05},
pages = {211-226},
publisher = {Palgrave Macmillan Cham},
chapter = {3},
abstract = {We discuss the potential of a mobile app for news tips to local newspapers to be augmented with artificial intelligence. It can be designed to encourage deliberative, consensus-oriented contributions from citizens. We presume that such an app will generate news stories from multi-modal data in the form of photos, videos, text elements, location information, and the identity of the contributor. Three scenarios are presented to show how image recognition, natural language processing, narrative construction, and other AI technologies can be applied. The scenarios address three interrelated challenges for local journalism. First, text and photos in tips are often of low quality for journalism purposes. Second, peer-to-peer dialogue about local news takes place in social media instead of in the newspaper. Third, readers lack news literacy and are prone to confrontational debates and trolling. We show how advances in deep learning technology makes it possible to propose solutions to these problems.},
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We discuss the potential of a mobile app for news tips to local newspapers to be augmented with artificial intelligence. It can be designed to encourage deliberative, consensus-oriented contributions from citizens. We presume that such an app will generate news stories from multi-modal data in the form of photos, videos, text elements, location information, and the identity of the contributor. Three scenarios are presented to show how image recognition, natural language processing, narrative construction, and other AI technologies can be applied. The scenarios address three interrelated challenges for local journalism. First, text and photos in tips are often of low quality for journalism purposes. Second, peer-to-peer dialogue about local news takes place in social media instead of in the newspaper. Third, readers lack news literacy and are prone to confrontational debates and trolling. We show how advances in deep learning technology makes it possible to propose solutions to these problems.
Tessem, Bjørnar
The Future Technologies in Journalism Presentation
EBU Metadata Network 2022 Online Conference, 01.01.2022.
@misc{cristin2028464,
title = {The Future Technologies in Journalism},
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Tessem, Bjørnar; Nyre, Lars; dos Santos Mesquita, Michel; Mulholland, Paul
Deep Learning to Encourage Citizen Involvement in Local Journalism Proceedings Article
In: Palgrave Macmillan, 2022.
@inproceedings{cristin2023207,
title = {Deep Learning to Encourage Citizen Involvement in Local Journalism},
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doi = {https://doi.org/10.1007/978-3-030-95073-6_14},
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2021
Tessem, Bjørnar; Nyre, Lars; dos Santos Mesquita, Michel; Mulholland, Paul
Deep Learning to Encourage Citizen Involvement in Local Journalism Journal Article
In: Futures of Journalism. Palgrave Macmillan. , 2021.
@article{dosMulholland2021,
title = {Deep Learning to Encourage Citizen Involvement in Local Journalism},
author = {Bjørnar Tessem and Lars Nyre and Michel dos Santos Mesquita and Paul Mulholland},
editor = {In Ville Manninen et al.},
year = {2021},
date = {2021-12-31},
urldate = {2021-12-31},
journal = {Futures of Journalism. Palgrave Macmillan. },
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Trattner, Christoph; Jannach, Dietmar; Motta, Enrico; Meijer, Irene Costera; Diakopoulos, Nicholas; Elahi, Mehdi; Opdahl, Andreas L.; Tessem, Bjørnar; Borch, Njål; Fjeld, Morten; Øvrelid, Lilja; Smedt, Koenraad De; Moe, Hallvard
Responsible media technology and AI: challenges and research directions Journal Article
In: AI and Ethics, 2021.
@article{cristin2000622,
title = {Responsible media technology and AI: challenges and research directions},
author = {Christoph Trattner and Dietmar Jannach and Enrico Motta and Irene Costera Meijer and Nicholas Diakopoulos and Mehdi Elahi and Andreas L. Opdahl and Bjørnar Tessem and Njål Borch and Morten Fjeld and Lilja Øvrelid and Koenraad De Smedt and Hallvard Moe},
url = {https://app.cristin.no/results/show.jsf?id=2000622, Cristin
https://link.springer.com/content/pdf/10.1007/s43681-021-00126-4.pdf},
doi = {https://doi.org/10.1007/s43681-021-00126-4},
year = {2021},
date = {2021-12-20},
urldate = {2021-12-20},
journal = {AI and Ethics},
keywords = {},
pubstate = {published},
tppubtype = {article}
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Tessem, Bjørnar
Når kunstig intelligens inntar redaksjonen Medium
2021.
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title = {Når kunstig intelligens inntar redaksjonen},
author = {Bjørnar Tessem},
url = {https://app.cristin.no/results/show.jsf?id=1942282, Cristin},
year = {2021},
date = {2021-10-01},
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Tverberg, Are; Agasøster, Ingrid; Grønbæck, Mads; Monsen, Marius; Strand, Robert; Eikeland, Kristian; Throndsen, Eivind; Westvang, Lars; Knudsen, Tove B.; Fiskerud, Eivind; Skår, Rune; Stoppel, Sergej; Berven, Arne; Pedersen, Glenn Skare; Macklin, Paul; Cuomo, Kenneth; Vredenberg, Loek; Tolonen, Kristian; Opdahl, Andreas L.; Tessem, Bjørnar; Veres, Csaba; Dang-Nguyen, Duc-Tien; Motta, Enrico; Setty, Vinay Jayarama
WP3 2021 M3.1 Report The industrial expectations to, needs from and wishes for the work package Technical Report
University of Bergen, MediaFutures 2021.
@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},
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tppubtype = {techreport}
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Nyre, Lars; Tessem, Bjørnar
Automatiske nyhende Online
Dag og Tid 2021, visited: 26.03.2021.
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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},
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Nyre, Lars; Tessem, Bjørnar
Automatiske nyhende Medium
Dag og Tid, 2021.
@media{cristin1905817,
title = {Automatiske nyhende},
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Nyre, Lars; Tessem, Bjørnar
Auka røynd (AR) Medium
Dag og Tid, 2021.
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title = {Auka røynd (AR)},
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Nyre, Lars; Tessem, Bjørnar
Ting i internettet Medium
Dag og Tid, 2021.
@media{cristin1931531,
title = {Ting i internettet},
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year = {2021},
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Tessem, Bjørnar; Opdahl, Andreas L.
Content Analysis and Production Presentation
MediaFutures Annual Meeting 2021, 01.01.2021.
@misc{cristin1942264,
title = {Content Analysis and Production},
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Nyre, Lars; Tessem, Bjørnar
Kvifor Google-briller vart ein fiasko Medium
Dag og Tid, 2021.
@media{cristin1942262,
title = {Kvifor Google-briller vart ein fiasko},
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year = {2021},
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Nyre, Lars; Tessem, Bjørnar
Kva datamaskiner kan gjere Medium
Dag og Tid, 2021.
@media{cristin1957728,
title = {Kva datamaskiner kan gjere},
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year = {2021},
date = {2021-01-01},
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2020
Motta, Enrico; Daga, Enrico; Opdahl, Andreas L.; Tessem, Bjørnar
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.
Opdahl, Andreas L.; Tessem, Bjørnar
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
2019
Tessem, Bjørnar
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.
Tessem, Bjørnar; Opdahl, Andreas L.
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
Ocaña, Marc Gallofré; Nyre, Lars; Opdahl, Andreas L.; Tessem, Bjørnar; Trattner, Christoph; Veres, Csaba
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.
2017
Nyre, Lars; Ribeiro, Joao; Tessem, Bjørnar
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.