Prof. Andreas Lothe Opdahl

@article{aiandnewa,

title = {Guest Editorial: AI and the news},

author = {Andreas L. Opdahl and Natali Helberger and Nicholas Diakopoulos},

url = {https://mediafutures.no/guest_editorial_ai_and_the_news-2/},

year  = {2024},

date = {2024-05-15},

journal = {AI Magazine},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@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.},

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pubstate = {published},

tppubtype = {article}

}

@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.},

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pubstate = {published},

tppubtype = {article}

}

@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.},

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pubstate = {published},

tppubtype = {article}

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@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},

tppubtype = {article}

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@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.},

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pubstate = {published},

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@article{Opdahl2022,

title = {Semantic Knowledge Graphs for the News: A Review},

author = {Andreas L. Opdahl and Tareq Al-Moslmi and Duc-Tien Dang-Nguyen and Marc Gallofré Ocaña},

url = {https://mediafutures.no/3543508/},

year  = {2022},

date = {2022-12-15},

urldate = {2022-12-15},

journal = {ACM Computing Surveys},

volume = {55},

issue = {7},

pages = {1-38},

abstract = {ICT platforms for news production, distribution, and consumption must exploit the ever-growing availability of digital data. These data originate from different sources and in different formats; they arrive at different velocities and in different volumes. Semantic knowledge graphs (KGs) is an established technique for integrating such heterogeneous information. It is therefore well-aligned with the needs of news producers and distributors, and it is likely to become increasingly important for the news industry. This article reviews the research on using semantic knowledge graphs for production, distribution, and consumption of news. The purpose is to present an overview of the field; to investigate what it means; and to suggest opportunities and needs for further research and development.},

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@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}

}

@article{cristin1949655,

title = {Developing a Software Reference Architecture for Journalistic Knowledge Platforms},

author = {Marc Gallofré Ocaña and Andreas L. Opdahl},

url = {https://app.cristin.no/results/show.jsf?id=1949655, Cristin

http://ceur-ws.org/Vol-2978/saml-paper2.pdf},

year  = {2021},

date = {2021-01-01},

urldate = {2021-01-01},

journal = {CEUR Workshop Proceedings},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@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}

}

@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}

}

@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}

}

@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},

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}