Platform-Invariant Topic Modeling via Contrastive Learning to Mitigate Platform-Induced Bias

Minseo Koo, Doeun Kim, Sungwon Han, Sungkyu Shaun Park


Abstract
Cross-platform topic dissemination is one of the research subjects that delved into media analysis; sometimes it fails to grasp the authentic topics due to platform-induced biases, which may be caused by aggregating documents from multiple platforms and running them on an existing topic model. This work deals with the impact of unique platform characteristics on the performance of topic models and proposes a new approach to enhance the effectiveness of topic modeling. The data utilized in this study consisted of a total of 1.5 million posts collected using the keyword ”ChatGPT” on the three social media platforms. The devised model reduces platform influence in topic models by developing a platform-invariant contrastive learning algorithm and removing platform-specific jargon word sets. The proposed approach was thoroughly validated through quantitative and qualitative experiments alongside standard and state-of-the-art topic models and showed its supremacy. This method can mitigate biases arising from platform influences when modeling topics from texts collected across various platforms.
Anthology ID:
2024.findings-emnlp.650
Volume:
Findings of the Association for Computational Linguistics: EMNLP 2024
Month:
November
Year:
2024
Address:
Miami, Florida, USA
Editors:
Yaser Al-Onaizan, Mohit Bansal, Yun-Nung Chen
Venue:
Findings
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
11123–11139
Language:
URL:
https://aclanthology.org/2024.findings-emnlp.650
DOI:
10.18653/v1/2024.findings-emnlp.650
Bibkey:
Cite (ACL):
Minseo Koo, Doeun Kim, Sungwon Han, and Sungkyu Shaun Park. 2024. Platform-Invariant Topic Modeling via Contrastive Learning to Mitigate Platform-Induced Bias. In Findings of the Association for Computational Linguistics: EMNLP 2024, pages 11123–11139, Miami, Florida, USA. Association for Computational Linguistics.
Cite (Informal):
Platform-Invariant Topic Modeling via Contrastive Learning to Mitigate Platform-Induced Bias (Koo et al., Findings 2024)
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PDF:
https://aclanthology.org/2024.findings-emnlp.650.pdf