Reading Between the Tweets: Deciphering Ideological Stances of Interconnected Mixed-Ideology Communities (2024)

Abstract: Recent advances in NLP have improved our ability to understand the nuanced worldviews of online communities. Existing research focused on probing ideological stances treats liberals and conservatives as separate groups. However, this fails to account for the nuanced views of the organically formed online communities and the connections between them. In this paper, we study discussions of the 2020 U.S. election on Twitter to identify complex interacting communities. Capitalizing on this interconnectedness, we introduce a novel approach that harnesses message passing when finetuning language models (LMs) to probe the nuanced ideologies of these communities. By comparing the responses generated by LMs and real-world survey results, our method shows higher alignment than existing baselines, highlighting the potential of using LMs in revealing complex ideologies within and across interconnected mixed-ideology communities.

## introduction social media platforms connect people worldwide within digital town squares, transforming how they share information and exchange ideas. however, mass connectivity, has created new vulnerabilities, including rampant misinformation, the formation of echo chambers that confirm people's pre-existing beliefs @xcite , and the fragmentation of society into polarized factions that disagree with and distrust each other @xcite . these developments intensify societal conflicts and undermine trust in democratic institutions @xcite . given these challenges, understanding the ideological nuances within online communities is essential. existing works provide insights into political ideologies of online groups @xcite ; however, they treat ideology as a liberal/conservative binary (figure 1a ) and fail to capture the spectrum of ideologies that may organically emerge in interconnected online communities. to bridge this gap, we propose a methodology to uncover interacting communities in political discourse on twitter that are not merely liberal or conservative, but possess a complex mixture of political ideologies (figure 1b ). to reveal communities' ideological stances, we align gpt-2 language models (lms) to the language and mindsets of communities by finetuning the models on tweets that the communities generate. this finetuning, enriched by message passing techniques inspired by graph convolutional networks @xcite , leverages the interconnected nature of these communities, allowing for a more robust representation of their ideological stances. with the finetuned lms, we then probe the stances of the communities towards various targets, including different political figures and social groups, by looking at the sentiment of generated responses. this way we can measure 1) for each target, which communities are more in favor of or against it (target-specific community ranking), and 2) for each community, which targets it favors more and which it is against @xcite . by comparing the model predicted stances to that from the american national election studies (anes) 2020 exploratory testing survey, our method, when benchmarked against existing baselines, outperforms them on these tasks, validating its effectiveness in capturing the political ideology of interconnected online communities. our work highlights the potential of leveraging social media data to reveal the nuanced ideological stances of organically-formed, interconnected online communities. such insights pave the way for a more informed understanding of the dynamics and shifts in digital attitudes. ## probing stances of online communities to study the different opinions and stances of different communities, we delineate each community with a large language model finetuned on this community's corpus. during finetuning, we use the message passing technique to account for the information and opinion shared between communities. finally, to verify that our models indeed capture communities' political ideology, we test it against multiple baselines on stance prediction toward 30 politically salient entities or groups. the results show the outstanding performance of our method. ## conclusion we explore the complex ideologies of ad-hoc online communities towards different political figures and social groups. our approach probes these ideological stances by finetuning language models on community-authored tweets and exchanging community information through message passing. our method aligns with real-world survey data and outperforms existing baselines. our work underscores the potential of leveraging social media data to monitor and understand societal dynamics in the digital age. our method offers a promising pathway for future research. potential avenues include expanding the study to other social media platforms, analyzing how ideological stances of online communities evolve over time, and finetuning one single language model for different communities to enhance scalability when the number of communities increases. our approach also holds the promise of providing an in-depth exploration of intricate ideological postures of the communities, facilitating a broader array of applications, including the examination of community emotional reaction to wedge issues @xcite and affective polarization @xcite .