Visibility vs. Engagement: How Two Indian News Websites Reported on LGBTQ+ Individuals and Communities during the Pandemic
Abstract: Online news media outlets were an important source of information for people with digital access during the COVID–19 pandemic. In India, where “transgender” was legally recognised as a category only in 2014, and same–sex marriages are yet to be legalised, it becomes crucial to analyse whether and how news media reported the lived realities of vulnerable LGBTQ+ communities during the pandemic. This study analysed articles from online editions of two English–language newspaper websites, which differed vastly in their circulation figures—The Times of India and The Indian Express.
The results of our study suggest that these newspaper websites covered articles surrounding various aspects of the lives of LGBTQ+ individuals with a greater focus on transgender communities. However, they lacked quality and depth. Focusing on the period spanning March 2020 to August 2021, we analysed articles from The Times of India and The Indian Express using distil–RoBERTa–base and ChatGPT–3.5 for sentiment analysis and BERTopic for topic modelling. We also compared our results to the period before the pandemic (January 2019–December 2019) to understand the shift in topics and sentiments across the two newspaper websites. Our topic modelling results indicate that The Times of India and The Indian Express primarily wrote soft news on LGBTQ+ communities. Similarly, our sentiment analysis results indicate a difference in prevailing sentiment as depicted by the two models. Furthermore, our manual analysis of the articles indicates that the language used in certain articles by The Times of India was transphobic and obsolete. Our study captures the visibility and representation of the LGBTQ+ communities in online Indian news media outlets, the language they use, and the narratives they follow.
Paper Type: Long
Research Area: Computational Social Science and Cultural Analytics
Research Area Keywords: Computational Social Science and Cultural Analytics
Contribution Types: Data analysis, Position papers
Languages Studied: English
Submission Number: 5770
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