Aparna Ananthasubramaniam


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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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POTATO: The Portable Text Annotation Tool
Jiaxin Pei | Aparna Ananthasubramaniam | Xingyao Wang | Naitian Zhou | Apostolos Dedeloudis | Jackson Sargent | David Jurgens
Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing: System Demonstrations

We present POTATO, the Portable text annotation tool, a free, fully open-sourced annotation system that 1) supports labeling many types of text and multimodal data; 2) offers easy-to-configure features to maximize the productivity of both deployers and annotators (convenient templates for common ML/NLP tasks, active learning, keypress shortcuts, keyword highlights, tooltips); and 3) supports a high degree of customization (editable UI, inserting pre-screening questions, attention and qualification tests). Experiments over two annotation tasks suggest that POTATO improves labeling speed through its specially-designed productivity features, especially for long documents and complex tasks. POTATO is available at https://github.com/davidjurgens/potato and will continue to be updated.