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News diversity and recommendation systems : setting the interdisciplinary scene

Glen Joris (UGent) , Camiel Colruyt, Judith Vermeulen (UGent) , Stefaan Vercoutere (UGent) , Frederik De Grove, Kristin Van Damme (UGent) , Orphée De Clercq (UGent) , Cynthia Van Hee (UGent) , Lieven De Marez (UGent) , Veronique Hoste (UGent) , et al.
(2020) Privacy and identity management : data for better living : AI and privacy. In IFIP Advances in Information and Communication Technology 576. p.90-105
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Abstract
Concerns about selective exposure and filter bubbles in the digital news environment trigger questions regarding how news recommender systems can become more citizen-oriented and facilitate – rather than limit – normative aims of journalism. Accordingly, this chapter presents building blocks for the construction of such a news algorithm as they are being developed by the Ghent University interdisciplinary research project #NewsDNA, of which the primary aim is to actually build, evaluate and test a diversity-enhancing news recommender. As such, the deployment of artificial intelligence could support the media in providing people with information and stimulating public debate, rather than undermine their role in that respect. To do so, it combines insights from computer sciences (news recommender systems), law (right to receive information), communication sciences (conceptualisations of news diversity), and computational linguistics (automated content extraction from text). To gather feedback from scholars of different backgrounds, this research has been presented and discussed during the 2019 IFIP summer school workshop on ‘co-designing a personalised news diversity algorithmic model based on news consumers’ agency and fine-grained content modelling’. This contribution also reflects the results of that dialogue.
Keywords
lt3, Algorithms, News content extraction, News diversity, NewsDNA, News personalisation, News recommendation systems, Right to receive diverse information

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MLA
Joris, Glen, et al. “News Diversity and Recommendation Systems : Setting the Interdisciplinary Scene.” Privacy and Identity Management : Data for Better Living : AI and Privacy, edited by Michael Friedewald et al., vol. 576, Springer, 2020, pp. 90–105, doi:10.1007/978-3-030-42504-3_7.
APA
Joris, G., Colruyt, C., Vermeulen, J., Vercoutere, S., De Grove, F., Van Damme, K., … Martens, L. (2020). News diversity and recommendation systems : setting the interdisciplinary scene. In M. Friedewald, M. Önen, E. Lievens, S. Krenn, & S. Fricker (Eds.), Privacy and identity management : data for better living : AI and privacy (Vol. 576, pp. 90–105). https://doi.org/10.1007/978-3-030-42504-3_7
Chicago author-date
Joris, Glen, Camiel Colruyt, Judith Vermeulen, Stefaan Vercoutere, Frederik De Grove, Kristin Van Damme, Orphée De Clercq, et al. 2020. “News Diversity and Recommendation Systems : Setting the Interdisciplinary Scene.” In Privacy and Identity Management : Data for Better Living : AI and Privacy, edited by Michael Friedewald, Melek Önen, Eva Lievens, Stephan Krenn, and Samuel Fricker, 576:90–105. Springer. https://doi.org/10.1007/978-3-030-42504-3_7.
Chicago author-date (all authors)
Joris, Glen, Camiel Colruyt, Judith Vermeulen, Stefaan Vercoutere, Frederik De Grove, Kristin Van Damme, Orphée De Clercq, Cynthia Van Hee, Lieven De Marez, Veronique Hoste, Eva Lievens, Toon De Pessemier, and Luc Martens. 2020. “News Diversity and Recommendation Systems : Setting the Interdisciplinary Scene.” In Privacy and Identity Management : Data for Better Living : AI and Privacy, ed by. Michael Friedewald, Melek Önen, Eva Lievens, Stephan Krenn, and Samuel Fricker, 576:90–105. Springer. doi:10.1007/978-3-030-42504-3_7.
Vancouver
1.
Joris G, Colruyt C, Vermeulen J, Vercoutere S, De Grove F, Van Damme K, et al. News diversity and recommendation systems : setting the interdisciplinary scene. In: Friedewald M, Önen M, Lievens E, Krenn S, Fricker S, editors. Privacy and identity management : data for better living : AI and privacy. Springer; 2020. p. 90–105.
IEEE
[1]
G. Joris et al., “News diversity and recommendation systems : setting the interdisciplinary scene,” in Privacy and identity management : data for better living : AI and privacy, vol. 576, M. Friedewald, M. Önen, E. Lievens, S. Krenn, and S. Fricker, Eds. Springer, 2020, pp. 90–105.
@incollection{8625324,
  abstract     = {{Concerns about selective exposure and filter bubbles in the digital news environment trigger questions regarding how news recommender systems can become more citizen-oriented and facilitate – rather than limit – normative aims of journalism. Accordingly, this chapter presents building blocks for the construction of such a news algorithm as they are being developed by the Ghent University interdisciplinary research project #NewsDNA, of which the primary aim is to actually build, evaluate and test a diversity-enhancing news recommender. As such, the deployment of artificial intelligence could support the media in providing people with information and stimulating public debate, rather than undermine their role in that respect. To do so, it combines insights from computer sciences (news recommender systems), law (right to receive information), communication sciences (conceptualisations of news diversity), and computational linguistics (automated content extraction from text). To gather feedback from scholars of different backgrounds, this research has been presented and discussed during the 2019 IFIP summer school workshop on ‘co-designing a personalised news diversity algorithmic model based on news consumers’ agency and fine-grained content modelling’. This contribution also reflects the results of that dialogue.}},
  author       = {{Joris, Glen and Colruyt, Camiel and Vermeulen, Judith and Vercoutere, Stefaan and De Grove, Frederik and Van Damme, Kristin and De Clercq, Orphée and Van Hee, Cynthia and De Marez, Lieven and Hoste, Veronique and Lievens, Eva and De Pessemier, Toon and Martens, Luc}},
  booktitle    = {{Privacy and identity management : data for better living : AI and privacy}},
  editor       = {{Friedewald, Michael and Önen, Melek and Lievens, Eva and Krenn, Stephan and Fricker, Samuel}},
  isbn         = {{9783030425036}},
  issn         = {{1868-4238}},
  keywords     = {{lt3,Algorithms,News content extraction,News diversity,NewsDNA,News personalisation,News recommendation systems,Right to receive diverse information}},
  language     = {{eng}},
  location     = {{Windisch, Switzerland}},
  pages        = {{90--105}},
  publisher    = {{Springer}},
  series       = {{IFIP Advances in Information and Communication Technology}},
  title        = {{News diversity and recommendation systems : setting the interdisciplinary scene}},
  url          = {{http://doi.org/10.1007/978-3-030-42504-3_7}},
  volume       = {{576}},
  year         = {{2020}},
}
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