Bright and Dark Side of Social Media in Marginalized Contexts

Permanent URI for this collectionhttps://hdl.handle.net/10125/112487

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    Commenting from Afar: Psychological Distance and Empathy in Reddit Responses to School Shootings
    (2026-01-06) Ren, Jie; Mattson, Tom; Weng, Qin
    This study investigates the social media discourse surrounding school shootings, with a focus on how empathy is expressed in response to traumatic events. Using Reddit data related to school shootings, we identify a non-linear (inverted U-shaped) relationship between the frequency of death-related mentions in posts and the empathy expressed in subsequent comments. This pattern is particularly pronounced when the event discussed is temporally proximate (i.e., low temporal distance) or when commenters use more first-person pronouns (i.e., low social distance). Grounded in Terror Management Theory and Construal Level Theory, this research empirically examines how death-related language in posts influences online grieving and empathic response. Notably, our findings suggest that psychological distance functions as a buffering mechanism moderating rather than impeding the expression of empathy in digital spaces.
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    Social Media Use as a Contributor to Marginalization in India: A Five-Year Field Study in the US and India
    (2026-01-06) Venkatesh, Viswanath; Raman, Raji
    The COVID-19 pandemic accelerated a global shift to technology driven remote work, intensifying the blurring of work-life boundaries. This study investigates the differential impact on employee well-being in developed versus emerging economies. Using a five-year longitudinal study with data collected pre-COVID-19 (2019), during-COVID-19 (2020), and three years of post-COVID-19 (2021, 2022, 2023) in a single firm’s US and India offices, we examine job outcomes through the lens of the job characteristics model. Our findings reveal a stark divergence in post-pandemic recovery. While US employees’ job strain and job satisfaction returned to pre-pandemic levels, their Indian counterparts experienced sustained job strain and reduced job satisfaction recovery. These persistent negative outcomes demonstrate that the impact of using technologies in India has led to them being more marginalized driven by the new realities of technology-dependent work. That Indian workers experience significantly higher job strain, which has not returned to pre-COVID levels, represents a grave concern. This serves as an urgent call for scientific and managerial attention to the problem of emergent marginalization due to increased social media and other technology use.
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    Who Drives the Diffusion of Incivility in Immigration Discussion? Exploring the Role of Morality and Issue Stance
    (2026-01-06) Li, Yiqi; Yang, Aimei
    This study examines incivility diffusion in immigration discourse networks from the lens of three incivility communicator types: amplifiers, copycats, and attenuators. This research draws on Moral Foundation Theory to analyze how users’ predispositions, as manifested in their moral expressions and immigration stance (anti-immigration or pro-immigration), help explain their roles in the diffusion of incivility on social media. A combination of human efforts, AI-assisted annotation, and a transformer-based language model was adopted to classify users’ immigration stance, morality expression, and incivility expression. Multinomial regression revealed that amplifiers score high on binding moral values, especially sanctity. Attenuators often hold pro-immigration stances. Findings have implications for platform design and intervention strategies targeting the relational dynamics of incivility diffusion
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    Characterizing YouTube Channels via Multi-View Content Similarity
    (2026-01-06) Shajari, Shadi; Agarwal, Nitin
    YouTube channels communicate their themes and attract viewers through titles, descriptions, transcripts, and categories. While prior research has focused on user engagement, the internal consistency of content across a channel’s videos remains underexplored. This paper presents a framework for characterizing YouTube channels based on semantic similarity among key content features. Using a dataset of 150 channels and 157,235 videos, pairwise similarity scores were computed across six content feature combinations. five unsupervised clustering algorithms were applied to each pair, and results were integrated through majority voting to produce stable channel clusters. Similarity-based analysis revealed recurring alignment patterns, leading to five high-level content behavior characterizations. These profiles reflect distinct strategies in metadata coherence, narrative structure, and category usage. The proposed method enables scalable, label-free, and language-agnostic analysis of channel behavior. By revealing gaps between content and metadata, the framework surfaces potential editorial biases that may disproportionately affect marginalized audiences on social media.
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