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.
BibTeX
@inproceedings{subramanyam-etal-2024-enabling,
title = "Enabling Cross-Platform Comparison of Online Communities Using Content and Opinion Similarity",
author = "Subramanyam, Prasanna Lakkur and
Chou, Jeng-Yu and
Nam, Kevin K. and
Levine, Brian",
editor = "Al-Onaizan, Yaser and
Bansal, Mohit and
Chen, Yun-Nung",
booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2024",
month = nov,
year = "2024",
address = "Miami, Florida, USA",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2024.findings-emnlp.586/",
doi = "10.18653/v1/2024.findings-emnlp.586",
pages = "10017--10028"
}