@article{cristin1933059,

title = {Changing Salty Food Preferences with Visual and Textual

Explanations in a Search Interface},

author = {Arngeir Berge and Vegard Velle Sjøen and Alain Starke and Christoph Trattner},

url = {https://app.cristin.no/results/show.jsf?id=1933059, Cristin

http://ceur-ws.org/Vol-2903/IUI21WS-HEALTHI-2.pdf},

year  = {2021},

date = {2021-01-01},

journal = {CEUR Workshop Proceedings},

abstract = {Salt is consumed at too high levels in the general population, causing high blood pressure and related health problems. In this paper, we present results of ongoing research that tries to reduce salt intake via technology and in particular from an interface perspective. In detail, this paper features results of a study that examines the extent to which visual and textual explanations in a search interface can change salty food preferences. An online user study with 200 participants demonstrates that this is possible in food search results by accompanying recipes with a visual taste map that includes salt-replacer herbs and spices in the calculation of salty taste.},

keywords = {Cristin},

pubstate = {published},

tppubtype = {article}

}

@article{cristin1902285,

title = {How the public understands news media trust: An open-ended approach},

author = {Erik Knudsen and Stefan Dahlberg and Magnus Hoem Iversen and Mikael Poul Johannesson and Silje Nygaard},

url = {https://app.cristin.no/results/show.jsf?id=1902285, Cristin},

doi = {https://doi.org/10.1177/14648849211005892},

year  = {2021},

date = {2021-01-01},

journal = {Journalism - Theory, Practice & Criticism},

keywords = {Cristin},

pubstate = {published},

tppubtype = {article}

}

@article{cristin1949655,

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

author = {Marc Gallofré Ocaña and Andreas Lothe 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},

journal = {CEUR Workshop Proceedings},

keywords = {Cristin},

pubstate = {published},

tppubtype = {article}

}

@article{cristin1953363,

title = {Investigating the impact of recommender systems on user-based and item-based popularity bias},

author = {Mehdi Elahi and Danial Khosh Kholgh and Mohammad Sina Kiarostami and Sorush Saghari and Shiva Parsa Rad and Marko Tkalcic},

url = {https://app.cristin.no/results/show.jsf?id=1953363, Cristin

https://www.sciencedirect.com/science/article/pii/S0306457321001436},

doi = {https://doi.org/10.1016/j.ipm.2021.102655},

year  = {2021},

date = {2021-01-01},

journal = {Information Processing & Management},

abstract = {Recommender Systems are decision support tools that adopt advanced algorithms in order to help users to find less-explored items that can be interesting for them. While recommender systems may offer a range of attractive benefits, they may also intensify undesired effects, such as the Popularity Bias, where a few popular users/items get more popular and many unpopular users/items get more unpopular.



In this paper, we study the impact of different recommender algorithms on the popularity bias in different application domains and recommendation scenarios. We have designed a comprehensive evaluation methodology by considering two different recommendation scenarios, i.e., the user-based scenario (e.g., recommending users to users to follow), and the item-based scenario (e.g., recommending items to users to consume). We have used two large datasets, Twitter and Movielens, and compared a wide range of classical and modern recommender algorithms by considering a diverse range of metrics, such as PR-AUC, RCE, Gini index, and Entropy Score.



The results have shown a substantial difference between different scenarios and different recommendation domains. According to our observations, while the recommendation of users to users may increase the popularity bias in the system, the recommendation of items to users may indeed decrease it. Moreover, while we have measured a different level of popularity bias in different languages (i.e., English, Spanish, Portuguese, and Japaneses), the above-noted phenomena has been consistently observed in all of these languages.},

keywords = {Cristin},

pubstate = {published},

tppubtype = {article}

}

@article{Arne2020,

title = {Folk theories of algorithms: Understanding digital irritation},

author = {Brita Ytre-Arne and Hallvard Moe},

year  = {2020},

date = {2020-12-31},

journal = {Media, Culture & Society},

series = {TEST},

note = {Pre SFI},

keywords = {WP1: Understanding Media Experiences},

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 Lothe 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},

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 = {Computational journalism, Journalistic knowledge platforms, Knowledge graphs, Machine learning (ML), Natural-language processing (NLP), Newsroom systems, Ontology, OWL, RDF, Semantic technologies, WP3: Media Content Production and Analysis},

pubstate = {published},

tppubtype = {article}

}

@article{Touileb2020,

title = {Gender and sentiment, critics and authors: a dataset of Norwegian book reviews},

author = {Samia Touileb and Lilja Øvrelid and Erik Velldal},

url = {https://www.aclweb.org/anthology/2020.gebnlp-1.11.pdf},

year  = {2020},

date = {2020-12-01},

journal = {Gender Bias in Natural Language Processing. Association for Computational Linguistics},

abstract = {Gender bias in models and datasets is widely studied in NLP. The focus has usually been on analysing how females and males express themselves, or how females and males are described. However, a less studied aspect is the combination of these two perspectives, how female and male describe the same or opposite gender. In this paper, we present a new gender annotated sentiment dataset of critics reviewing the works of female and male authors. We investigate if this newly annotated dataset contains differences in how the works of male and female authors are critiqued, in particular in terms of positive and negative sentiment. We also explore the differences in how this is done by male and female critics. We show that there are differences in how critics assess the works of authors of the same or opposite gender. For example, male critics rate crime novels written by females, and romantic and sentimental works written by males, more negatively.},

note = {Pre SFI},

keywords = {WP5: Norwegian Language Technologies},

pubstate = {published},

tppubtype = {article}

}

@article{Barnes2020,

title = {Improving sentiment analysis with multi-task learning of negation},

author = {J Barnes and Erik Velldal and Lilja Øvrelid},

url = {https://www.cambridge.org/core/journals/natural-language-engineering/article/abs/improving-sentiment-analysis-with-multitask-learning-of-negation/14EF2B829EC4B8EC29E7C0C5C77B95B0},

year  = {2020},

date = {2020-11-11},

note = {Pre SFI},

keywords = {WP5: Norwegian Language Technologies},

pubstate = {published},

tppubtype = {article}

}

@article{Endriss2020,

title = {The complexity landscape of outcome determination in judgment aggregation},

author = {Ulle Endriss and Ronald de Haan and Jerôme Lang and Marija Slavkovik },

url = {https://www.jair.org/index.php/jair/article/view/11970/26619},

doi = {10.1613/jair.1.11970},

year  = {2020},

date = {2020-11-04},

journal = {Journal of Artificial Intelligence Research},

volume = {69},

pages = {687–731},

abstract = {We provide a comprehensive analysis of the computational complexity of the outcome determinationproblem for the most important aggregation rules proposed in the literature on logic-based judgmentaggregation. Judgment aggregation is a powerful and flexible framework for studying problems ofcollective decision making that has attracted interest in a range of disciplines, including Legal Theory,Philosophy, Economics, Political Science, and Artificial Intelligence. The problem of computing theoutcome for a given list of individual judgments to be aggregated into a single collective judgment isthe most fundamental algorithmic challenge arising in this context. Our analysis applies to severaldifferent variants of the basic framework of judgment aggregation that have been discussed in theliterature, as well as to a new framework that encompasses all existing such frameworks in terms ofexpressive power and representational succinctness.},

note = {Pre SFI},

keywords = {WP2: User Modeling Personalization and Engagement},

pubstate = {published},

tppubtype = {article}

}

@article{Moe2020,

title = {Operationalizing exposure diversity.},

author = {Hallvard Moe and Jan Fredrik Hovden and Kari Karppinen},

url = {https://journals.sagepub.com/doi/pdf/10.1177/0267323120966849},

doi = {10.1177/0267323120966849},

year  = {2020},

date = {2020-10-29},

journal = {European Journal of Communication},

pages = {1-2},

abstract = {The concept of exposure diversity, the diversity of information that people actually access and use, has recently gained prominence in media policy debates. This aspect of media diversity, however, remains difficult to define, measure or implement in actual policy. In this article, we propose an empirical approach that operationalizes exposure diversity in terms of news and current affairs providers in the media repertoire of different social groups. This can be studied through cluster analysis of survey data on respondents’ combinations of use of different media providers and outlets. The article first discusses exposure diversity as a media policy aim. We then outline our proposal on how to take the debate a step further through empirical analysis of media repertoires, with an illustration of how such an analysis may be conducted using survey data from Norway.},

note = {Pre SFI},

keywords = {Exposure Diversity, Media Diversity, Media Policy, Media Repertoires, Survey, WP1: Understanding Media Experiences},

pubstate = {published},

tppubtype = {article}

}

@article{Hazrati2020,

title = { Addressing the New Item problem in video recommender systems by incorporation of visual features with restricted Boltzmann machines.},

author = {Naieme Hazrati and Mehdi Elahi},

url = {https://onlinelibrary.wiley.com/doi/epdf/10.1111/exsy.12645},

doi = {https://doi.org/10.1111/exsy.12645},

year  = {2020},

date = {2020-10-19},

journal = {Expert Systems},

volume = {e12645},

pages = {1-20},

abstract = {Over the past years, the research of video recommender systems (RSs) has been mainly focussed on the development of novel algorithms. Although beneficial, still any algorithm may fail to recommend video items that the system has no form of data associated to them (New Item Cold Start). This problem occurs when a new item is added to the catalogue of the system and no data are available for that item. In content‐based RSs, the video items are typically represented by semantic attributes, when generating recommendations. These attributes require a group of experts or users for annotation, and still, the generated recommendations might not capture a complete picture of the users' preferences, for example, the visual tastes of users on video style. This article addresses this problem by proposing recommendation based on novel visual features that do not require human annotation and can represent visual aspects of video items. We have designed a novel evaluation methodology considering three realistic scenarios, that is, (a) extreme cold start, (b) moderate cold start and (c) warm‐start scenario. We have conducted a set of comprehensive experiments, and our results have shown the superior performance of recommendations based on visual features, in all of the evaluation scenarios.},

note = {Pre SFI},

keywords = {Cold Start, Multimedia, New Item, Recommender systems, Visually Aware, WP2: User Modeling Personalization and Engagement},

pubstate = {published},

tppubtype = {article}

}

@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 = {Deep neural networks, Fake news detection, Reddit comments, WP3: Media Content Production and Analysis},

pubstate = {published},

tppubtype = {article}

}

@article{Arne2020b,

title = {Temporal ambivalences in smartphone use: Conflicting flows, conflicting responsibilities.},

author = {Brita Ytre-Arne and Trine Syvertsen and Hallvard Moe and Faltin Karlsen },

url = {https://journals.sagepub.com/doi/pdf/10.1177/1461444820913561},

doi = {10.1177/1461444820913561},

year  = {2020},

date = {2020-09-03},

journal = {New Media and Society},

volume = {22},

number = {9},

pages = {1715–1732},

abstract = {This article explores implications of the central position of the smartphone in an age of constant connectivity. Based on a qualitative study of 50 informants, we ask how users experience and handle temporal ambivalences in everyday smartphone use, drawing on the concepts flow and responsibilization to conceptualize central dimensions of such ambivalences. The notion of conflicting flows illuminates how brief checking cycles expand at the expense of other activities, resulting in a temporal conflict experienced by users. Responsibilization points to how users take individual responsibility for managing such conflicting flows, and to how this practice is difficult and conflict-ridden. We conclude that while individual time management is often framed as the solution to temporal conflicts, such attempts at regulating smartphone use appear inadequate. Our conceptualization of temporal ambivalence offers a more nuanced understanding of why this is the case.},

note = {Pre SFI},

keywords = {Ambivalence, Flow, Media Use, Responsibilization, Smartphone, Time Management, WP1: Understanding Media Experiences},

pubstate = {published},

tppubtype = {article}

}

@article{Arne2020c,

title = {Audiences’ Communicative Agency in a Datafied Age: Interpretative, Relational and Increasingly Prospective.},

author = {Brita Ytre-Arne and Ranjana Das},

url = {https://watermark.silverchair.com/qtaa018.pdf?token=AQECAHi208BE49Ooan9kkhW_Ercy7Dm3ZL_9Cf3qfKAc485ysgAAAtAwggLMBgkqhkiG9w0BBwagggK9MIICuQIBADCCArIGCSqGSIb3DQEHATAeBglghkgBZQMEAS4wEQQM-cFhCl8ql-5yUsJoAgEQgIICg0JoOiOVaCIdOstGjiiVpYuTKzqRfP7Hb1L0JBUB7TMpOQ5ya4v7afXtJvvTasi126A8qHSxK2rDZfeWFajUD34XXIbXVfimKI-a7-dZNYNjF6xn9p5OzsBABo10PuVtS5bHE4B3RSURKpRgGKXIyem7o-HzoTgKWKxJjRuOEVJNX4XjUC9-D9C7f8n3BItvYkMJqiX8NRSmuM3MI1MmhCjjtrUEOaURe-mCNKonobiYtkdoywElD9W7SG0ZQg9nigzcJmEH36Rbf3jzaGMMQhOsTv0NCAFwm52wxxeqt1jlSX6GmjMPmwTTmhZNGPE-sD0j3VZEfzqZNb5RqOBv2tih20z3kz2mBAU_25OlUThhSQ_G9-dNMVfVsuKMbo6TnS8EUotKdlWDmmenYUmQdHFx6KHA2mFn3j0AYyqz9Kolc2HeSSu4JJhZMZGHIUkNVC5c-GPPCzpO4D5pCT3pNw2d4mK037ziOWPOKcr29Ak2sJlQ05rcn4NovqBWrNIeRQRuhlVyNI7nr7bIXXFMwogvhFttUt3IxH68cYD6nNQ1Gbtl2BwygLseOwrwZA_6irZcqueVtAwqzMchowERFcKR8gMPKyDiDGPfbbKMNvqeHeIvemaQSLnfLM7Lfj9-OYtxOgF6CxZTrbOcCTUVpnvwTIZO1Oq2amSRBEb2lCORDiegqrxTitstUZ141VXW5bId94vwLuZqPoYA58nNcF0_2WzAOB69owF5B7D_ofDUTA2tBAhuvcvwKpAB5t_bhLDscuVfVGcYi5azd06oQ9PJWPV5EigzleuyHR57_cVyPpYkXe-PimZJ6iZzEhnmYZE0v9A_9F2LYm9dwfEryznsCog},

doi = {10.1093/ct/qtaa018},

issn = {1050–3293},

year  = {2020},

date = {2020-08-24},

journal = {Communication Theory},

volume = {0},

number = {C},

pages = {1-19},

abstract = {This article develops a conceptualization of audience agency in the face of datafication. We consider how people, as audiences and users of media and technologies, face transforming communicative conditions, and how these conditions challenge the power potentials of audiences in processes of communication—that is, their communicative agency. To develop our conceptualization, we unpack the concept of audiences’ communicative agency by examining its foundations in communication scholarship, in reception theory and sociology, arguing that agency is understood as interpretative and relational, and applied to make important normative assessments. We further draw on emerging scholarship on encounters with data in the everyday to discuss how audience agency is now challenged by datafication, arguing that communicative agency is increasingly prospective in a datafied age. Thereby, we provide a theoretical conceptualization for further analysis of audiences in transforming communicative conditions.},

note = {Pre SFI},

keywords = {Agency, Audiences, Datafication, Everyday, Interpretation, Prospection, Reception Theory, Sociological Theory, Structure, Technology, WP1: Understanding Media Experiences},

pubstate = {published},

tppubtype = {article}

}

@article{Motta2020,

title = {Analysis and design of computational news angles},

author = {Enrico Motta and Enrico Daga and Andreas Lothe 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},

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 = {Computational journalism, Data schema, Knowledge representation, News angles, Ontology, Reasoning components., WP3: Media Content Production and Analysis},

pubstate = {published},

tppubtype = {article}

}

@article{Nunes2020,

title = {A Systematic Review and Taxonomy of Explanations in Decision Support and Recommender Systems},

author = {Ingrid Nunes and Dietmar Jannach},

url = {https://arxiv.org/pdf/2006.08672.pdf},

doi = {10.1007/s11257-017-9195-0},

year  = {2020},

date = {2020-06-15},

journal = {User-Modeling and User-Adapted Interaction},

volume = {27},

number = {3-5},

pages = {393-444},

abstract = {With the recent advances in the field of artificial intelligence, an increasing number of decision-making tasks are delegated to software systems. A key requirement for the success and adoption of such systems is that users must trust system choices or even fully automated decisions. To achieve this, explanation facilities have been widely investigated as a means of establishing trust in these systems since the early years of expert systems. With today's increasingly sophisticated machine learning algorithms, new challenges in the context of explanations, accountability, and trust towards such systems constantly arise. In this work, we systematically review the literature on explanations in advice-giving systems. This is a family of systems that includes recommender systems, which is one of the most successful classes of advice-giving software in practice. We investigate the purposes of explanations as well as how they are generated, presented to users, and evaluated. As a result, we derive a novel comprehensive taxonomy of aspects to be considered when designing explanation facilities for current and future decision support systems. The taxonomy includes a variety of different facets, such as explanation objective, responsiveness, content and presentation. Moreover, we identified several challenges that remain unaddressed so far, for example related to fine-grained issues associated with the presentation of explanations and how explanation facilities are evaluated.},

note = {Pre SFI},

keywords = {Artificial Intelligence, Decision Support System, Expert System, Explanation, Knowledge-based system, Machine Learning, Recommender systems, Systematic review, Trust, WP2: User Modeling Personalization and Engagement},

pubstate = {published},

tppubtype = {article}

}

@article{Ophdal2020,

title = {Ontologies for finding journalistic angles},

author = {Andreas Lothe 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},

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 = {WP3: Media Content Production and Analysis},

pubstate = {published},

tppubtype = {article}

}

@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 = {Fake news detection, Hierarchical attention, Neural networks, WP3: Media Content Production and Analysis},

pubstate = {published},

tppubtype = {article}

}

@article{Sjøvaag2020,

title = {Strategic and Organisational fit in Corporate News Markets: A Principal-agent Approach to Studying Newspaper Mergers},

author = {Helle Sjøvaag and Thomas Owren and Turid Borgen},

url = {https://www.tandfonline.com/doi/epub/10.1080/17512786.2020.1772097?needAccess=true},

doi = {https://doi.org/10.1080/17512786.2020.1772097},

year  = {2020},

date = {2020-05-20},

journal = {Journalism Practice},

pages = {1-18},

abstract = {This article analyses strategic and organisational fit in corporate newspaper mergers in the context of the digitalisation of local newspaper markets. Using the 2019 acquisition of Nordsjø Media by Amedia in Norway as case, we analyse how eight editors-in-chief perceive the process of incorporating small, low-frequency, print-oriented monopolistic newspapers into one of Scandinavia’s largest newspaper chains. The semi-structured interviews were analysed in light of perceived strategic and organisational fit in a principal-agent theoretical framework, the aim of which is to shed light on corporate ownership effects in consolidated newspaper markets. The analysis reveals the precarity of independent ownership in digitising news markets, to which corporatisation emerges as a necessary and welcomed solution. We find the strategic fit as perceived by editors to be tied to technological resources and scale economics, while organisational fit is hampered by the speed and pressure of corporatisation processes. While these results largely support findings from previous acquisition studies in the news industries, the contribution of this analysis lies primarily with the necessity of scale required by the technological transformation that forces independent newspapers to submit to larger chain operations and how it influences considerations of fit in disruptive digital news markets.},

note = {Pre SFI},

keywords = {Agency Theory, Editor in chief, Local journalism, Media ownership, Mergers and acqusitions, News markets, News organisations, Newspaper chains, WP2: User Modeling Personalization and Engagement},

pubstate = {published},

tppubtype = {article}

}

@article{Lomborg2020,

title = {Methods for datafication, datafication of methods: Introduction to the Special Issue.},

author = {Stine Lomborg and Lina Dencik and Hallvard Moe },

url = {https://journals.sagepub.com/doi/pdf/10.1177/0267323120922045},

doi = {10.1177/0267323120922045 Article information },

year  = {2020},

date = {2020-05-05},

journal = {European Journal of Communication},

volume = {35},

number = {3},

pages = {203-212},

abstract = {Digital media enable processes of datafication: users' online activities leave digital traces that are transformed into data points in databases, kept by service providers and other private and public organisations, and repurposed for commercial exploitation, business innovation, surveillance -- and research. Increasingly, this also extends to sensors and recognition technologies that turn homes and cities, as well as our own bodies, into data points to be collected and analysed So-called ‘traditional’ media industries, too, including public service broadcasting, have been datafied, tracking and profiling audiences, algorithmically processing data for greater personalisation as a way to compete with new players and streaming services. Datafication both raises new research questions and brings about new avenues, and an array of tools, for empirical research. This special issue is dedicated to exploring these, linking them to broader historical trajectories of social science methodologies as well as to central concerns and perspectives in media and communication research. As such, this special issue grapples with approaches to empirical research that interlink questions of methods and tools with epistemology and practice. It discusses the datafication of methods, as well as methods for studying datafication. With this we hope to enable reflection of what research questions media and communication scholars should ask of datafication, and how new and existing methods enable us to answer them.},

note = {Pre SFI},

keywords = {Datafication, Media and Communication Research, Methods, WP1: Understanding Media Experiences},

pubstate = {published},

tppubtype = {article}

}

@article{Lison2020,

title = {Named Entity Recognition without Labelled Data: A Weak Supervision Approach},

author = {Pierre Lison and Aliaksandr Hubin and Jeremy Barnes and Samia Touileb},

url = {https://arxiv.org/pdf/2004.14723.pdf},

year  = {2020},

date = {2020-04-30},

journal = {Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics},

pages = {1518–1533},

abstract = {Named Entity Recognition (NER) performance often degrades rapidly when applied to target domains that differ from the texts observed during training. When in-domain labelled data is available, transfer learning techniques can be used to adapt existing NER models to the target domain. But what should one do when there is no hand-labelled data for the target domain? This paper presents a simple but powerful approach to learn NER models in the absence of labelled data through weak supervision. The approach relies on a broad spectrum of labelling functions to automatically annotate texts from the target domain. These annotations are then merged together using a hidden Markov model which captures the varying accuracies and confusions of the labelling functions. A sequence labelling model can finally be trained on the basis of this unified annotation. We evaluate the approach on two English datasets (CoNLL 2003 and news articles from Reuters and Bloomberg) and demonstrate an improvement of about 7 percentage points in entity-level F1 scores compared to an out-of-domain neural NER model.},

note = {Pre SFI},

keywords = {WP5: Norwegian Language Technologies},

pubstate = {published},

tppubtype = {article}

}

@article{Holst2020,

title = {Deliberative systems theory and citizens’ use of online media: testing a critical theory of democracy on a high achiever.},

author = {Cathrine Holst and Hallvard Moe },

url = {https://journals.sagepub.com/doi/pdf/10.1177/0032321719890809},

doi = {10.1177/0032321719890809 Article information },

year  = {2020},

date = {2020-04-06},

journal = {Political Studies},

pages = {1-18},

abstract = {Deliberative systems theory is a promising candidate for a normative theory of democracy that combines ideal requirements with feasibility. Yet, recent theoretical elaborations and studies of citizens’ online media use inspired by the theory suffer from an incomplete account of the public sphere’s epistemic function, too rough interpretations of participatory levels, shortcomings in the understanding of online media, and a context-insensitive notion of policy reform. Addressing these weaknesses, the article argues for a refined version of deliberative systems theory. Particular attention is given to feasibility considerations. Reviewing studies of online democracy in Norway, the article shows that the theoretical critique has practical significance. It is also argued that the amended version of the deliberative systems approach produces a diagnosis of Norwegian online democracy more in line with reasonable expectations to a high achiever. This is taken as a prima facie indicator of feasibility.},

note = {Pre SFI},

keywords = {Deliberative Democracy, Experts, Non-ideal theory, Online Media, Public Sphere, WP1: Understanding Media Experiences},

pubstate = {published},

tppubtype = {article}

}

@article{Salzmann2020,

title = {“We in the Mojo Community” – Exploring a Global Network of Mobile Journalists},

author = {Anja Salzmann and Frode Guribye and Astrid Gynnild},

url = {https://www.tandfonline.com/doi/epub/10.1080/17512786.2020.1742772?needAccess=true},

doi = {https://doi.org/10.1080/17512786.2020.1742772},

year  = {2020},

date = {2020-04-03},

journal = {Journalism Practice},

pages = {1-18},

abstract = {Mobile journalism is a fast-growing area of journalistic innovation that requires new skills and work practices. Thus, a major challenge for journalists is learning not only how to keep up with new gadgets but how to advance and develop a mojo mindset to pursue their interests and solidify future work options. This paper investigates a globally pioneering network of mojo journalism, the Mojo Community, that consists of journalists and practitioners dedicated to creating multimedia content using mobile technologies. The study is based on empirical data from interviews with and the observation of the participants of the community over a two-year period. The analysis draws on Wenger’s concept of “communities of practice” to explore the domain, structure, and role of this communal formation for innovation and change in journalistic practices. The community’s core group is comprised of journalists mainly affiliated with legacy broadcast organizations and with a particular interest in and extensive knowledge of mobile technologies. The participants perceive their engagement with the community as a way of meeting the challenges of organizational reluctance to change, fast-evolving technological advancements, and uncertain job prospects.},

note = {Pre SFI},

keywords = {community of practice, digital culture, mobile content creation, Mobile journalism, mobile technologies, mojo, mojo community, smartphone reporting, WP4: Media Content Interaction and Accessibility},

pubstate = {published},

tppubtype = {article}

}

@article{Fjeld2020,

title = {Learn with Haptics: Improving Vocabulary Recall with Free-form Digital Annotation on Touchscreen Mobiles},

author = {Morten Fjeld and Smitha Sheshadri and Shendong Zhao and Yang Cheng},

url = {https://dl.acm.org/doi/pdf/10.1145/3313831.3376272

https://www.youtube.com/watch?v=WY_T0fK5gCQ&ab_channel=ACMSIGCHI},

year  = {2020},

date = {2020-04-01},

journal = {CHI 2020 Paper},

pages = {1-13},

abstract = {Mobile vocabulary learning interfaces typically present material only in auditory and visual channels, underutilizing the haptic modality. We explored haptic-integrated learning by adding free-form digital annotation to mobile vocabulary learning interfaces. Through a series of pilot studies, we identified three design factors: annotation mode, presentation sequence, and vibrotactile feedback, that influence recall in haptic-integrated vocabulary interfaces. These factors were then evaluated in a within-subject comparative study using a digital flashcard interface as baseline. Results using a 84-item vocabulary showed that the 'whole word' annotation mode is highly effective, yielding a 24.21% increase in immediate recall scores and a 30.36% increase in the 7-day delayed scores. Effects of presentation sequence and vibrotactile feedback were more transient; they affected the results of immediate tests, but not the delayed tests. We discuss the implications of these factors for designing future mobile learning applications.},

note = {Pre SFI},

keywords = {Haptics for Learning, Intersensory reinforced learning, Mobile Vocabulary Learning, Motoric Engagement, Multimodal Learning, WP4: Media Content Interaction and Accessibility},

pubstate = {published},

tppubtype = {article}

}

@article{deSmedt2020,

title = {FAIR Digital Objects for Science: From Data Pieces to Actionable Knowledge Units},

author = {Koenraad de Smedt and D Koureas and P Wittenberg},

url = {https://ideas.repec.org/a/gam/jpubli/v8y2020i2p21-d344422.html},

year  = {2020},

date = {2020-04-01},

note = {Pre SFI},

keywords = {WP5: Norwegian Language Technologies},

pubstate = {published},

tppubtype = {article}

}

@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 = {WP3: Media Content Production and Analysis},

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 Lothe 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},

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 = {Knowledge graphs, Named-entity disambiguation, Named-entity extraction, Named-entity linking, Named-entity recognition, Natural-language processing, WP3: Media Content Production and Analysis},

pubstate = {published},

tppubtype = {article}

}