Royal Pathak – Computer Science
Title: The Role of News Recommendation System in Social Networks and Its Contribution to Misinformation
Misinformation spread in social networks has drastically increased from the 2016 U.S. Presidential Election trough the current Covid-19 pandemic, with consequences that have impacted health, politics, the economy, and response to natural disasters. Misinformation spread has compromised people’s ability to access correct information and have informed opinions. It has also led to people reprimanding individuals and corporations for broadcasting, amplifying, and disseminating untrustworthy, inaccurate, and misleading information. The reach of misinformation remains prevalent, and the impact of misinformation spread, particularly on social networks are non-trivial. Recent research endeavors have addressed bot detection and features that characterize users, content, and context as means to identify misinformation in social networks. However, there is a dearth of work centered on investigating and understanding the impact that recommendation algorithms (RAs) can have towards misinformation spread in social networks. We first discuss the characteristics of the news domain, RAs used in the news domain, metrics used in this domain, and the connection between recommender systems and misinformation. Thereafter, we bring our attention to fundamental work in the context of social networks pertaining misinformation diffusion.