Understanding the Contribution of Recommendation Algorithms on Misinformation Recommendation and Misinformation Dissemination on Social Networks

Understanding the Contribution of Recommendation Algorithms on Misinformation Recommendation and Misinformation Dissemination on Social Networks

Pathak, Royal (Boise State University)
Spezzano, Francesca (Boise State University)
Pera, M.S. (TU Delft Web Information Systems)

2023

Social networks are a platform for individuals and organizations to connect with each other and inform, advertise, spread ideas, and ultimately influence opinions. These platforms have been known to propel misinformation. We argue that this could be compounded by the recommender algorithms that these platforms use to suggest items potentially of interest to their users, given the known biases and filter bubbles issues affecting recommender systems. While much has been studied about misinformation on social networks, the potential exacerbation that could result from recommender algorithms in this environment is in its infancy. In this manuscript, we present the result of an in-depth analysis conducted on two datasets (Politifact FakeNewsNet dataset and HealthStory FakeHealth dataset) in order to deepen our understanding of the interconnection between recommender algorithms and misinformation spread on Twitter. In particular, we explore the degree to which well-known recommendation algorithms are prone to be impacted by misinformation. Via simulation, we also study misinformation diffusion on social networks, as triggered by suggestions produced by these recommendation algorithms. Outcomes from this work evidence that misinformation does not equally affect all recommendation algorithms. Popularity-based and network-based recommender algorithms contribute the most to misinformation diffusion. Users who are known to be superspreaders are known to directly impact algorithmic performance and misinformation spread in specific scenarios. Findings emerging from our exploration result in a number of implications for researchers and practitioners to consider when designing and deploying recommender algorithms in social networks.

Additional Key Words and PhrasesMisinformation
diffusion
news
recommendation algorithms
social networks
Twitter

http://resolver.tudelft.nl/uuid:91eb9569-7b7f-4860-bdff-611f480769e4

https://doi.org/10.1145/3616088

1559-1131

ACM Transactions on the Web, 17 (4)

Institutional Repository

journal article

© 2023 Royal Pathak, Francesca Spezzano, M.S. Pera