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Browsing by Author "Rho, Eugenia"

Now showing 1 - 3 of 3
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    Leveraging Prompt-Based Large Language Models: Predicting Pandemic Health Decisions and Outcomes Through Social Media Language
    Ding, Xiaohan; Carik, Buse; Gunturi, Uma Sushmitha; Reyna, Valerie; Rho, Eugenia (ACM, 2024-05-11)
    We introduce a multi-step reasoning framework using prompt-based LLMs to examine the relationship between social media lan guage patterns and trends in national health outcomes. Grounded in fuzzy-trace theory, which emphasizes the importance of “gists” of causal coherence in effective health communication, we introduce Role-Based Incremental Coaching (RBIC), a prompt-based LLM framework, to identify gists at-scale. Using RBIC, we systematically extract gists from subreddit discussions opposing COVID-19 health measures (Study 1). We then track how these gists evolve across key events (Study 2) and assess their influence on online engage ment (Study 3). Finally, we investigate how the volume of gists is associated with national health trends like vaccine uptake and hospitalizations (Study 4). Our work is the first to empirically link social media linguistic patterns to real-world public health trends, highlighting the potential of prompt-based LLMs in identifying critical online discussion patterns that can form the basis of public health communication strategies.
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    Linguistically Differentiating Acts and Recalls of Racial Microaggressions on Social Media
    Gunturi, Uma Sushmitha; Kumar, Anisha; Ding, Xiaohan; Rho, Eugenia (ACM, 2024-04-23)
    In this work, we examine the linguistic signature of online racial microaggressions (acts) and how it differs from that of personal narratives recalling experiences of such aggressions (recalls) by Black social media users. We manually curate and annotate a corpus of acts and recalls from in-the-wild social media discussions, and verify labels with Black workshop participants. We leverage Natural Language Processing (NLP) and qualitative analysis on this data to classify (RQ1), interpret (RQ2), and characterize (RQ3) the language underlying acts and recalls of racial microaggressions in the context of racism in the U.S. Our findings show that neural language models (LMs) can classify acts and recalls with high accuracy (RQ1) with contextual words revealing themes that associate Blacks with objects that reify negative stereotypes (RQ2). Furthermore, overlapping linguistic signatures between acts and recalls serve functionally different purposes (RQ3), providing broader implications to the current challenges in content moderation systems on social media.
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    Understanding the Relationship Between Social Identity and Self-Expression Through Animated GIFs on Social Media
    Wang, Marx; Bhuiyan, Md Momen; Rho, Eugenia; Luther, Kurt; Lee, Sang Won (ACM, 2024-04-23)
    GIFs afford a high degree of personalization, as they are often created from popular movie and video clips with diverse and realistic characters, each expressing a nuanced emotional state through a combination of characters' own unique bodily gestures and distinctive visual backgrounds. These properties of high personalization and embodiment provide a unique window for exploring how individuals represent and express themselves on social media through the lens of the GIFs they use. In this study, we explore how Twitter users express their gender and racial identities through characters in GIFs. We conducted a behavioral study ($n=398$) to simulate a series of tweeting and GIF-picking scenarios. We annotated the gender and race identities of GIF characters, and we found that gender and race identities have significant impacts on users' GIF choices: men chose more gender-matching GIFs than women, and White participants chose more race-matching GIFs than Black participants. We also found that users' prior familiarity with the source of a GIF and perceptions about the composition of the audience (viz., having a matching identity) have significant effects on whether a user will choose race- and gender-matching GIFs. This work has implications for practitioners supporting personalized social identity construction and impression management mechanisms online.