TY - GEN
T1 - Advancing Misinformation Awareness in Recommender Systems for Social Media Information Integrity
AU - Pathak, Royal
N1 - Publisher Copyright:
© 2024 Owner/Author.
PY - 2024/10/21
Y1 - 2024/10/21
N2 - Recommender systems play an essential role in determining the content users encounter on social media platforms and in uncovering relevant news. However, they also present significant risks, such as reinforcing biases, over-personalizing content, fostering filter bubbles, and inadvertently promoting misinformation. The spread of false information is rampant across various online platforms, such as Twitter (now X), Meta, and TikTok, especially noticeable during events like the COVID-19 pandemic and the US Presidential elections. These instances underscore the critical necessity for transparency and regulatory oversight in the development of recommender systems. Given the challenge of balancing free speech with the risks of outright removal of fake news, this paper aims to address the spread of misinformation from algorithmic biases in recommender systems using a social science perspective.
AB - Recommender systems play an essential role in determining the content users encounter on social media platforms and in uncovering relevant news. However, they also present significant risks, such as reinforcing biases, over-personalizing content, fostering filter bubbles, and inadvertently promoting misinformation. The spread of false information is rampant across various online platforms, such as Twitter (now X), Meta, and TikTok, especially noticeable during events like the COVID-19 pandemic and the US Presidential elections. These instances underscore the critical necessity for transparency and regulatory oversight in the development of recommender systems. Given the challenge of balancing free speech with the risks of outright removal of fake news, this paper aims to address the spread of misinformation from algorithmic biases in recommender systems using a social science perspective.
KW - accuracy nudges
KW - echo chambers
KW - fake news
KW - intervention strategies
KW - misinformation
KW - recommender systems
KW - virality circuit breakers
UR - https://www.scopus.com/pages/publications/85210018572
U2 - 10.1145/3627673.3680259
DO - 10.1145/3627673.3680259
M3 - Conference contribution
AN - SCOPUS:85210018572
T3 - International Conference on Information and Knowledge Management, Proceedings
SP - 5471
EP - 5474
BT - CIKM 2024 - Proceedings of the 33rd ACM International Conference on Information and Knowledge Management
PB - Association for Computing Machinery
T2 - 33rd ACM International Conference on Information and Knowledge Management, CIKM 2024
Y2 - 21 October 2024 through 25 October 2024
ER -