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Preprint (Author's original)arXiv.org - Non-exclusive license to distribute, Open
Preprint
Hope vs. Hate: Understanding User Interactions with LGBTQ+ News Content in Mainstream US News Media through the Lens of Hope Speech
13 Feb 2025
Abstract
This paper makes three contributions. First, via a substantial corpus of
1,419,047 comments posted on 3,161 YouTube news videos of major US cable news
outlets, we analyze how users engage with LGBTQ+ news content. Our analyses
focus both on positive and negative content. In particular, we construct a
fine-grained hope speech classifier that detects positive (hope speech),
negative, neutral, and irrelevant content. Second, in consultation with a
public health expert specializing on LGBTQ+ health, we conduct an annotation
study with a balanced and diverse political representation and release a
dataset of 3,750 instances with fine-grained labels and detailed annotator
demographic information. Finally, beyond providing a vital resource for the
LGBTQ+ community, our annotation study and subsequent in-the-wild assessments
reveal (1) strong association between rater political beliefs and how they rate
content relevant to a marginalized community; (2) models trained on individual
political beliefs exhibit considerable in-the-wild disagreement; and (3)
zero-shot large language models (LLMs) align more with liberal raters.
Metrics
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Details
- Title
- Hope vs. Hate: Understanding User Interactions with LGBTQ+ News Content in Mainstream US News Media through the Lens of Hope Speech
- Creators
- Jonathan PofcherChristopher M HomanRandall SellAshiqur R KhudaBukhsh
- Resource Type
- Preprint
- Language
- English
- Academic Unit
- Community Health and Prevention
- Other Identifier
- 991022028082804721