Enabling Cross-Platform Comparison of Online Communities Using Content and Opinion Similarity

Prasanna Lakkur Subramanyam, Jeng-Yu Chou, Kevin K. Nam, Brian Levine


Abstract
With the continuous growth of online communities, understanding their similarities and dissimilarities is more crucial than ever for enhancing digital interactions, maintaining healthy interactions, and improving content recommendation and moderation systems. In this work, we present two novel techniques: BOTS for finding similarity between online communities based on their opinion, and Emb-PSR for finding similarity in the content they post. To facilitate finding the similarity based on opinion, we model the opinions on online communities using upvotes and downvotes as an indicator for community approval. Our results demonstrate that BOTS and Emb-PSR outperform existing techniques at their individual tasks while also being flexible enough to allow for cross-platform comparison of online communities. We demonstrate this novel cross-platform capability by comparing GAB with various subreddits.
Anthology ID:
2024.findings-emnlp.586
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:
10017–10028
Language:
URL:
https://preview.aclanthology.org/build-pipeline-with-new-library/2024.findings-emnlp.586/
DOI:
10.18653/v1/2024.findings-emnlp.586
Bibkey:
Cite (ACL):
Prasanna Lakkur Subramanyam, Jeng-Yu Chou, Kevin K. Nam, and Brian Levine. 2024. Enabling Cross-Platform Comparison of Online Communities Using Content and Opinion Similarity. In Findings of the Association for Computational Linguistics: EMNLP 2024, pages 10017–10028, Miami, Florida, USA. Association for Computational Linguistics.
Cite (Informal):
Enabling Cross-Platform Comparison of Online Communities Using Content and Opinion Similarity (Subramanyam et al., Findings 2024)
Copy Citation:
PDF:
https://preview.aclanthology.org/build-pipeline-with-new-library/2024.findings-emnlp.586.pdf
Software:
 2024.findings-emnlp.586.software.zip