Kenan Alkiek


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Exploring Linguistic Style Matching in Online Communities: The Role of Social Context and Conversation Dynamics
Aparna Ananthasubramaniam | Hong Chen | Jason Yan | Kenan Alkiek | Jiaxin Pei | Agrima Seth | Lavinia Dunagan | Minje Choi | Benjamin Litterer | David Jurgens
Proceedings of the First Workshop on Social Influence in Conversations (SICon 2023)

Linguistic style matching (LSM) in conversations can be reflective of several aspects of social influence such as power or persuasion. However, how LSM relates to the outcomes of online communication on platforms such as Reddit is an unknown question. In this study, we analyze a large corpus of two-party conversation threads in Reddit where we identify all occurrences of LSM using two types of style: the use of function words and formality. Using this framework, we examine how levels of LSM differ in conversations depending on several social factors within Reddit: post and subreddit features, conversation depth, user tenure, and the controversiality of a comment. Finally, we measure the change of LSM following loss of status after community banning. Our findings reveal the interplay of LSM in Reddit conversations with several community metrics, suggesting the importance of understanding conversation engagement when understanding community dynamics.


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Classification without (Proper) Representation: Political Heterogeneity in Social Media and Its Implications for Classification and Behavioral Analysis
Kenan Alkiek | Bohan Zhang | David Jurgens
Findings of the Association for Computational Linguistics: ACL 2022

Reddit is home to a broad spectrum of political activity, and users signal their political affiliations in multiple ways—from self-declarations to community participation. Frequently, computational studies have treated political users as a single bloc, both in developing models to infer political leaning and in studying political behavior. Here, we test this assumption of political users and show that commonly-used political-inference models do not generalize, indicating heterogeneous types of political users. The models remain imprecise at best for most users, regardless of which sources of data or methods are used. Across a 14-year longitudinal analysis, we demonstrate that the choice in definition of a political user has significant implications for behavioral analysis. Controlling for multiple factors, political users are more toxic on the platform and inter-party interactions are even more toxic—but not all political users behave this way. Last, we identify a subset of political users who repeatedly flip affiliations, showing that these users are the most controversial of all, acting as provocateurs by more frequently bringing up politics, and are more likely to be banned, suspended, or deleted.