Work

Circumventing Misinformation Controls: Assessing the Robustness of Intervention Strategies in Recommender Systems

Year: 2025

Type: conference-paper

Abstract: Recommender systems are essential on social media platforms, shaping the order of information users encounter and facilitating news discovery.However, these systems can inadvertently contribute to the spread of misinformation by reinforcing algorithmic biases, fostering excessive personalization, creating filter bubbles, and amplifying false narratives.Recent studies have demonstrated that intervention strategies, such as Virality Circuit Breakers and accuracy nudges, can effectively mitigate misinformation when implemented on top of recommender systems.Despite this, existing literature has yet to explore the robustness of these interventions against circumvention-where individuals or groups intentionally evade or resist efforts to counter misinformation.This research aims to address this gap, examining how well these interventions hold up in the face of circumvention tactics.Our findings highlight that these intervention strategies are generally robust against misinformation circumvention threats when applied on top of recommender systems.

Source: ACM Conference on User Modeling, Adaptation and Personalization (UMAP)

DOI: 10.1145/3699682.3728350

Authors: Royal Pathak, Francesca Spezzano

Institution: Boise State University

Language: English

Topic: Misinformation and Its Impacts

Subfield: Sociology and Political Science

Field: Social Sciences

Domain: Social Sciences

Open Access status: hybrid

Funder: National Science Foundation

Award: CAREER: Enhanced Analysis & Algorithms to Minimize the Spread of Misinformation in Social Networks