research-article

Rewiring What-to-Watch-Next Recommendations to Reduce Radicalization Pathways

Authors:

Francesco Fabbri,

Francesco Bonchi,

Carlos Castillo,

Michael MathioudakisAuthors Info & Claims

WWW '22: Proceedings of the ACM Web Conference 2022

Pages 2719 - 2728

https://doi.org/10.1145/3485447.3512143

Published: 25 April 2022 Publication History

Abstract

Recommender systems typically suggest to users content similar to what they consumed in the past. If a user happens to be exposed to strongly polarized content, she might subsequently receive recommendations which may steer her towards more and more radicalized content, eventually being trapped in what we call a “radicalization pathway”. In this paper, we study the problem of mitigating radicalization pathways using a graph-based approach. Specifically, we model the set of recommendations of a “what-to-watch-next” recommender as a d-regular directed graph where nodes correspond to content items, links to recommendations, and paths to possible user sessions.

We measure the “segregation” score of a node representing radicalized content as the expected length of a random walk from that node to any node representing non-radicalized content. High segregation scores are associated to larger chances to get users trapped in radicalization pathways. Hence, we define the problem of reducing the prevalence of radicalization pathways by selecting a small number of edges to “rewire”, so to minimize the maximum of segregation scores among all radicalized nodes, while maintaining the relevance of the recommendations.

We prove that the problem of finding the optimal set of recommendations to rewire is NP-hard and NP-hard to approximate within any factor. Therefore, we turn our attention to heuristics, and propose an efficient yet effective greedy algorithm based on the absorbing random walk theory. Our experiments on real-world datasets in the context of video and news recommendations confirm the effectiveness of our proposal.

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Miyauchi ASeverini LBonchi FChua TNgo CKa-Wei Lee RKumar RLauw H(2024)Local Centrality Minimization with Quality GuaranteesProceedings of the ACM on Web Conference 202410.1145/3589334.3645382(410-421)Online publication date: 13-May-2024
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Index Terms

Rewiring What-to-Watch-Next Recommendations to Reduce Radicalization Pathways
1. Information systems
  1. Information retrieval
    1. Retrieval tasks and goals
      1. Document filtering
      2. Information extraction
  2. Information systems applications
2. Mathematics of computing
  1. Discrete mathematics
    1. Graph theory

Index terms have been assigned to the content through auto-classification.

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cover image ACM Conferences

WWW '22: Proceedings of the ACM Web Conference 2022

April 2022

3764 pages

ISBN:9781450390965

DOI:10.1145/3485447

Editors:
Frédérique Laforest
INSA Lyon, France
,
Raphaël Troncy
EURECOM, France
,
Elena Simperl
King’s College London, UK
,
Deepak Agarwal
Pinterest, USA
,
Aristides Gionis
KTH Royal Institute of Technology, Sweden
,
Ivan Herman
W3C / retired
,
Lionel Médini
Université Lyon 1, France

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Published: 25 April 2022

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Porcaro LGómez ECastillo C(2024)Assessing the Impact of Music Recommendation Diversity on Listeners: A Longitudinal StudyACM Transactions on Recommender Systems10.1145/36084872:1(1-47)Online publication date: 7-Mar-2024
https://dl.acm.org/doi/10.1145/3608487
Miyauchi ASeverini LBonchi FChua TNgo CKa-Wei Lee RKumar RLauw H(2024)Local Centrality Minimization with Quality GuaranteesProceedings of the ACM on Web Conference 202410.1145/3589334.3645382(410-421)Online publication date: 13-May-2024
https://dl.acm.org/doi/10.1145/3589334.3645382
Fabbri FWang YBonchi FCastillo CMathioudakis MElkind E(2023)Rewiring what-to-watch-next recommendations to reduce radicalization pathways (extended abstract)Proceedings of the Thirty-Second International Joint Conference on Artificial Intelligence10.24963/ijcai.2023/715(6431-6435)Online publication date: 19-Aug-2023
https://dl.acm.org/doi/10.24963/ijcai.2023/715
Antonini A(2023)Positive by DesignProceedings of the 34th ACM Conference on Hypertext and Social Media10.1145/3603163.3609068(1-4)Online publication date: 4-Sep-2023
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Gatta VLuceri LFabbri FFerrara E(2023)The Interconnected Nature of Online Harm and ModerationProceedings of the 34th ACM Conference on Hypertext and Social Media10.1145/3603163.3609058(1-10)Online publication date: 4-Sep-2023
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Pandey RCarter JHill JMohler GSingh ASun YAkoglu LGunopulos DYan XKumar ROzcan FYe J(2023)Rewiring Police Officer Training Networks to Reduce Forecasted Use of ForceProceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining10.1145/3580305.3599899(4696-4704)Online publication date: 6-Aug-2023
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Coupette CNeumann SGionis ASingh ASun YAkoglu LGunopulos DYan XKumar ROzcan FYe J(2023)Reducing Exposure to Harmful Content via Graph RewiringProceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining10.1145/3580305.3599489(323-334)Online publication date: 6-Aug-2023
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Adriaens FWang HGionis ASingh ASun YAkoglu LGunopulos DYan XKumar ROzcan FYe J(2023)Minimizing Hitting Time between Disparate Groups with Shortcut EdgesProceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining10.1145/3580305.3599434(1-10)Online publication date: 6-Aug-2023
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Berjawi OFenza GLoia V(2023)A Comprehensive Survey of Detection and Prevention Approaches for Online Radicalization: Identifying Gaps and Future DirectionsIEEE Access10.1109/ACCESS.2023.332699511(120463-120491)Online publication date: 2023
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