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Hackathon-Challenge-Pitch_221124.pdf

Sina
November 25, 2022
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 Hackathon-Challenge-Pitch_221124.pdf

Sina

November 25, 2022
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Transcript

  1. Who?
    Research Project on «The Role of News Recommender Systems in Digital Democracies»
    Sina Blassnig1, Edina Strikovic2, Eliza Mitova1, Aleksandra Urman3,
    Anikó Hannák3, Frank Esser1, & Claes de Vreese2,
    1 Department of Communication and Media Research, University of Zürich
    2 Amsterdam School of Communication Research, University of Amsterdam
    3 Department of Informatics, University of Zürich
    Contact: [email protected]
    25.11.2022 Page 1

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  2. Why?
    25.11.2022 Page 2
    – Changing user behavior – Increasing automation and personalization of
    news
    Source: Digital News Report 2022, Data for Switzerland

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  3. What are News Recommender Systems (NRS)?
    – Algorithms that can make automated
    and/or personalized recommendations
    based on metadata, past behavior,
    ratings of similar users, popularity,
    and/or content-specific features (Ricci et
    al., 2011)
    – NRS can be used on the frontpage of
    news media websites, below articles,
    in special sections, for newsletters,
    etc.
    25.11.2022 Page 3

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  4. What is the problem?
    – Good NRS are expensive, laborious
    and complicated.
    – That’s why media still often focus only
    on:
    – Topic similarity
    – Popularity
    25.11.2022 Page 4

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  5. What is the Challenge?
    1) Analyze the problem:
    – What gets left behind if we focus on
    popularity-based recommenders?
    what categories, e.g., topics, regions, actors,
    formats etc. are over- or underrepresented
    2) Provide a solution:
    – What would need to be pushed in a
    diversity-maximizing recommender?
    what categories, e.g., topics, regions, actors,
    formats etc.
    – Can this be done in a transparent
    way? E.g., by showing why something is
    recommended
    25.11.2022 Page 5

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  6. Potential Resources & Needed Data
    Needed:
    − Content archive (articles, videos, audio)
    − Aggregated user data (e.g., likes, clicks,
    views, time spent)
    Available:
    - SRG APIs:
    https://developer.srgssr.ch/apis/
    - Others?
    25.11.2022 Page 6

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  7. Contact
    Sina BlassnigDepartment of Communication and Media Research, University of Zürich
    Contact: [email protected]
     Slack
    25.11.2022 Page 7

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