Circumventing Misinformation Controls: Assessing the Robustness of Intervention Strategies in Recommender Systems. Pathak, R. & Spezzano, F. In Proceedings of the 33rd ACM Conference on User Modeling, Adaptation and Personalization, of UMAP '25, pages 279–284, New York, NY, USA, June, 2025. Association for Computing Machinery. ![Circumventing Misinformation Controls: Assessing the Robustness of Intervention Strategies in Recommender Systems [link]](https://bibbase.org/img/filetypes/link.svg "link")
Paper doi abstract bibtex 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.
@inproceedings{pathak_circumventing_2025,
address = {New York, NY, USA},
series = {{UMAP} '25},
title = {Circumventing {Misinformation} {Controls}: {Assessing} the {Robustness} of {Intervention} {Strategies} in {Recommender} {Systems}},
isbn = {979-8-4007-1313-2},
shorttitle = {Circumventing {Misinformation} {Controls}},
url = {https://dl.acm.org/doi/10.1145/3699682.3728350},
doi = {10.1145/3699682.3728350},
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.},
urldate = {2025-06-22},
booktitle = {Proceedings of the 33rd {ACM} {Conference} on {User} {Modeling}, {Adaptation} and {Personalization}},
publisher = {Association for Computing Machinery},
author = {Pathak, Royal and Spezzano, Francesca},
month = jun,
year = {2025},
pages = {279--284},
}
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