CommunityLM: Probing Partisan Worldviews from Language Models

Hang Jiang, Doug Beeferman, Brandon Roy, Deb Roy


Abstract
As political attitudes have diverged ideologically in the United States, political speech has diverged lingusitically. The ever-widening polarization between the US political parties is accelerated by an erosion of mutual understanding between them. We aim to make these communities more comprehensible to each other with a framework that probes community-specific responses to the same survey questions using community language models CommunityLM. In our framework we identify committed partisan members for each community on Twitter and fine-tune LMs on the tweets authored by them. We then assess the worldviews of the two groups using prompt-based probing of their corresponding LMs, with prompts that elicit opinions about public figures and groups surveyed by the American National Election Studies (ANES) 2020 Exploratory Testing Survey. We compare the responses generated by the LMs to the ANES survey results, and find a level of alignment that greatly exceeds several baseline methods. Our work aims to show that we can use community LMs to query the worldview of any group of people given a sufficiently large sample of their social media discussions or media diet.
Anthology ID:
2022.coling-1.593
Volume:
Proceedings of the 29th International Conference on Computational Linguistics
Month:
October
Year:
2022
Address:
Gyeongju, Republic of Korea
Venue:
COLING
SIG:
Publisher:
International Committee on Computational Linguistics
Note:
Pages:
6818–6826
Language:
URL:
https://aclanthology.org/2022.coling-1.593
DOI:
Bibkey:
Cite (ACL):
Hang Jiang, Doug Beeferman, Brandon Roy, and Deb Roy. 2022. CommunityLM: Probing Partisan Worldviews from Language Models. In Proceedings of the 29th International Conference on Computational Linguistics, pages 6818–6826, Gyeongju, Republic of Korea. International Committee on Computational Linguistics.
Cite (Informal):
CommunityLM: Probing Partisan Worldviews from Language Models (Jiang et al., COLING 2022)
Copy Citation:
PDF:
https://aclanthology.org/2022.coling-1.593.pdf
Code
 hjian42/communitylm