Randall Sell | Searcharxiv

arXiv2025

Hope vs. Hate: Understanding User Interactions with LGBTQ+ News Content in Mainstream US News Media through the Lens of Hope Speech

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.

cs.CL↗

arXiv2013

Respondent-Driven Sampling in Online Social Networks

Respondent-driven sampling (RDS) is a commonly used method for acquiring data on hidden communities, i.e., those that lack unbiased sampling frames or face social stigmas that make their mem- bers unwilling to identify themselves. Obtaining accurate statistical data about such communities is important because, for instance, they often have different health burdens from the greater population, and without good statistics it is hard and expensive to effectively reach them for pre- vention or treatment interventions. Online social networks (OSN) have the potential to transform RDS for the better. We present a new RDS recruitment protocol for (OSNs) and show via simulation that it out- performs the standard RDS protocol in terms of sampling accuracy and approaches the accuracy of Markov chain Monte Carlo random walks.

cs.SI↗