extended-abstract

OHARS: Second Workshop on Online Misinformation- and Harm-Aware Recommender Systems

Authors:

Antonela Tommasel,

Daniela Godoy,

Arkaitz ZubiagaAuthors Info & Claims

RecSys '21: Proceedings of the 15th ACM Conference on Recommender Systems

Pages 789 - 791

https://doi.org/10.1145/3460231.3470941

Published: 13 September 2021 Publication History

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Abstract

Recommender systems play a central role in online information consumption and user decision-making by leveraging user-generated information at scale to assist users in finding relevant information and establishing new social relationships. Just as recommendation techniques have become powerful tools that are inserted in most social platforms, they could also involuntarily spread unwanted content and other types of online harms. The same fundamental concepts on which these techniques rely make them facilitators of such unwanted diffusion. To increase the user-perceived quality of recommender systems and mitigating the negative effects of the multiple forms of online harms, it is essential to provide recommender systems with harm-aware mechanisms. To further research in this direction, this Second edition of the Workshop on Online Misinformation- and Harm-Aware Recommender Systems (OHARS 2021) aimed at fostering research in recommender systems that can mitigate the negative effects of online harms by fostering the recommendation of safe content and trustworthy users, with a special interest in research tackling the negative effects of the propagation of harmful content referring to the COVID-19 crisis.

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Omidosu JKyobe M(2024)View No Evil And Listen To No Evil: The Need For Harm Metrics To Evaluate Online Hate On Multimedia Communication Channels2024 IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS)10.1109/AVSS61716.2024.10672585(1-8)Online publication date: 15-Jul-2024
https://doi.org/10.1109/AVSS61716.2024.10672585
Pathak RSpezzano FPera M(2023)Understanding the Contribution of Recommendation Algorithms on Misinformation Recommendation and Misinformation Dissemination on Social NetworksACM Transactions on the Web10.1145/361608817:4(1-26)Online publication date: 10-Oct-2023
https://dl.acm.org/doi/10.1145/3616088
Shen JV. R. Oliveira DCao JKnott BGu GVijaya Raghavan SJin YSudan NMonarch R(2023)Identifying Controversial Pairs in Item-to-Item RecommendationsProceedings of the 17th ACM Conference on Recommender Systems10.1145/3604915.3608871(671-674)Online publication date: 14-Sep-2023
https://dl.acm.org/doi/10.1145/3604915.3608871
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RecSys '21: Proceedings of the 15th ACM Conference on Recommender Systems

September 2021

883 pages

ISBN:9781450384582

DOI:10.1145/3460231

Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the Owner/Author.

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Association for Computing Machinery

New York, NY, United States

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Published: 13 September 2021

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Extended-abstract
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Royal Society of London
Consejo Nacional de Investigaciones Científicas y Técnicas

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RecSys '21

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RecSys '21: Fifteenth ACM Conference on Recommender Systems

September 27 - October 1, 2021

Amsterdam, Netherlands

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Overall Acceptance Rate 254 of 1,295 submissions, 20%

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RecSys '24

Sponsor:
sigchi

18th ACM Conference on Recommender Systems

October 14 - 18, 2024

Bari , Italy

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Omidosu JKyobe M(2024)View No Evil And Listen To No Evil: The Need For Harm Metrics To Evaluate Online Hate On Multimedia Communication Channels2024 IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS)10.1109/AVSS61716.2024.10672585(1-8)Online publication date: 15-Jul-2024
https://doi.org/10.1109/AVSS61716.2024.10672585
Pathak RSpezzano FPera M(2023)Understanding the Contribution of Recommendation Algorithms on Misinformation Recommendation and Misinformation Dissemination on Social NetworksACM Transactions on the Web10.1145/361608817:4(1-26)Online publication date: 10-Oct-2023
https://dl.acm.org/doi/10.1145/3616088
Shen JV. R. Oliveira DCao JKnott BGu GVijaya Raghavan SJin YSudan NMonarch R(2023)Identifying Controversial Pairs in Item-to-Item RecommendationsProceedings of the 17th ACM Conference on Recommender Systems10.1145/3604915.3608871(671-674)Online publication date: 14-Sep-2023
https://dl.acm.org/doi/10.1145/3604915.3608871
Godoy DTommasel AZubiaga A(2022)Special issue on intelligent systems for tackling online harmsPersonal and Ubiquitous Computing10.1007/s00779-022-01682-027:1(1-3)Online publication date: 17-Mar-2022
https://dl.acm.org/doi/10.1007/s00779-022-01682-0
Le BSpina DScholer FChia H(2022)A Crowdsourcing Methodology to Measure Algorithmic Bias in Black-Box Systems: A Case Study with COVID-Related SearchesAdvances in Bias and Fairness in Information Retrieval10.1007/978-3-031-09316-6_5(43-55)Online publication date: 19-Jun-2022
https://doi.org/10.1007/978-3-031-09316-6_5

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Workshop on Online Misinformation- and Harm-Aware Recommender Systems

Tutorial: Sequence-Aware Recommender Systems

Acquiring User Information Needs for Recommender Systems

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