Advancing Misinformation Awareness in Recommender Systems for Social Media Information Integrity. Pathak, R. In Proceedings of the 33rd ACM International Conference on Information and Knowledge Management, of CIKM '24, pages 5471–5474, New York, NY, USA, October, 2024. Association for Computing Machinery. ![Advancing Misinformation Awareness in Recommender Systems for Social Media Information Integrity [link]](https://bibbase.org/img/filetypes/link.svg "link")
Paper doi abstract bibtex 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.
@inproceedings{pathak_advancing_2024,
address = {New York, NY, USA},
series = {{CIKM} '24},
title = {Advancing {Misinformation} {Awareness} in {Recommender} {Systems} for {Social} {Media} {Information} {Integrity}},
isbn = {9798400704369},
url = {https://dl.acm.org/doi/10.1145/3627673.3680259},
doi = {10.1145/3627673.3680259},
abstract = {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.},
urldate = {2024-11-04},
booktitle = {Proceedings of the 33rd {ACM} {International} {Conference} on {Information} and {Knowledge} {Management}},
publisher = {Association for Computing Machinery},
author = {Pathak, Royal},
month = oct,
year = {2024},
pages = {5471--5474},
}
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