The Bright and Dark Side of Social Media in the Marginalized Contexts

Permanent URI for this collectionhttps://hdl.handle.net/10125/107502

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    Curse or Cure: Exploring Responses to Mental Health Related Posts in Reddit and ChatGPT Using Terror Management Theory
    (2024-01-03) Mattson, Tom; Weng, Qin; Ren, Jie
    In our paper, we investigate the responses that individuals with mental illnesses receive on their posts on Reddit and ChatGPT. Using terror management theory (TMT), we propose that the level of empathy of the comments is a function of the mortality salience of the commenter and the technical platform. To test our proposed effects empirically, we extracted a sample of posts and their comments from the “mental illness” and “mental health” subreddits along with responses generated from those posts on ChatGPT. In our statistical analyses, we found a significant main effect for mortality salience in relation to empathy consistent with the TMT, but this effect was qualified by the technical platform. We found that higher mortality salience favored ChatGPT over Reddit and lower mortality salience had the opposite effect.
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    Towards resource inequities in catching the “dark side” of social media: A hateful meme classification framework for low-resource scenarios
    (2024-01-03) Li, Yuming; Chan, Johnny; Peko, Gabrielle; Sundaram, David
    The increasing prevalence of social media platforms has led to the emergence of multimodal information such as memes. Hateful memes poses a risk by perpetuating discrimination, reinforcing stereotypes, and causing online harassment, thereby marginalising certain groups and impeding efforts towards inclusivity and social justice. Detecting hateful memes is crucial for creating a safe and equitable online environment. However, existing research heavily relies on complex and large deep learning models, requiring substantial computational resources for training. This creates a barrier for under-resourced researchers and small companies, limiting their participation in hateful information detection and exacerbating inequalities in the field of artificial intelligence. This paper attempts to tackle the problem by proposing a low-resource- oriented framework of hateful meme classification to address limitations in training data, computing power, and modality integration. Our approach achieves faster performance with reduced computational requirements, while maintaining a 94.7% accuracy comparable to the existing highest-scoring model.
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    Digital Activism on Social Media: The Role of Brand Ambassadors and Corporate Reputation Management
    (2024-01-03) Marx, Julian; Brünker, Felix; Mirbabaie, Milad; Stieglitz, Stefan
    Social media constitute an important arena for public debates and steady interchange of issues relevant to society. To boost their reputation, commercial organizations also engage in political, social, or environmental debates on social media. To engage in this type of digital activism, organizations increasingly utilize the social media profiles of executive employees and other brand ambassadors. However, the relationship between brand ambassadors’ digital activism and corporate reputation is only vaguely understood. The results of a qualitative inquiry suggest that digital activism via brand ambassadors can be risky (e.g., creating additional surface for firestorms, financial loss) and rewarding (e.g., emitting authenticity, employing ‘megaphones’ for industry change) at the same time. The paper informs both scholarship and practitioners about strategic trade-offs that need to be considered when employing brand ambassadors for digital activism.
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