Radhika Krishnan | Searcharxiv

arXiv2025

Framing Climate Change on YouTube: North-South Divides in Narratives and Public Engagement

Climate change debates unfold increasingly on social media platforms, with YouTube serving as both a news source and a space for public discourse. While prior studies have often examined climate discourse at a global level, less attention has been paid to how geopolitical divides shape narratives and public responses online. This paper presents an exploratory analysis of climate-related YouTube videos through the lens of the Global North-South divide. We analyze 758 English-language videos linked to major international climate negotiation events and their associated comment sections. Using topic modeling to examine video transcripts and sentiment analysis to study audience reactions, we identify distinct patterns in how climate issues are framed and received. Videos that originate from the Global North more frequently emphasize emission reduction policies and institutional responsibility, while those from the Global South foreground development-related concerns. Audience responses diverge more sharply: comment sections under Global North videos are dominated by criticism and conspiracy-related discourse, whereas audiences are comparatively more supportive and offer constructive arguments under Global South videos. These findings highlight a gap between curated climate narratives and public sentiment on YouTube and suggest that platform dynamics may reinforce or reshape existing geopolitical divides in climate communication.

cs.SI↗

arXiv2019

Multi-label Categorization of Accounts of Sexism using a Neural Framework

Sexism, an injustice that subjects women and girls to enormous suffering, manifests in blatant as well as subtle ways. In the wake of growing documentation of experiences of sexism on the web, the automatic categorization of accounts of sexism has the potential to assist social scientists and policy makers in studying and countering sexism better. The existing work on sexism classification, which is different from sexism detection, has certain limitations in terms of the categories of sexism used and/or whether they can co-occur. To the best of our knowledge, this is the first work on the multi-label classification of sexism of any kind(s), and we contribute the largest dataset for sexism categorization. We develop a neural solution for this multi-label classification that can combine sentence representations obtained using models such as BERT with distributional and linguistic word embeddings using a flexible, hierarchical architecture involving recurrent components and optional convolutional ones. Further, we leverage unlabeled accounts of sexism to infuse domain-specific elements into our framework. The best proposed method outperforms several deep learning as well as traditional machine learning baselines by an appreciable margin.

cs.CL↗