Uncovering Gender Bias within Journalist-Politician Interaction in Indian Twitter

• URL: http://arxiv.org/abs/2310.18911v1
• Date: Sun, 29 Oct 2023 05:41:53 GMT
• Title: Uncovering Gender Bias within Journalist-Politician Interaction in Indian Twitter
• Authors: Brisha Jain, Mainack Mondal
• Abstract summary: We curated a gender-balanced set of 100 most-followed Indian journalists on Twitter and 100 most-followed politicians. We collected 21,188 unique tweets posted by these journalists that mentioned these politicians. Our analysis revealed that there is a significant gender bias -- the frequency with which journalists mention male politicians vs. how frequently they mention female politicians.
• Score: 7.964711577522729
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Gender bias in political discourse is a significant problem on today's social media. Previous studies found that the gender of politicians indeed influences the content directed towards them by the general public. However, these works are particularly focused on the global north, which represents individualistic culture. Furthermore, they did not address whether there is gender bias even within the interaction between popular journalists and politicians in the global south. These understudied journalist-politician interactions are important (more so in collectivistic cultures like the global south) as they can significantly affect public sentiment and help set gender-biased social norms. In this work, using large-scale data from Indian Twitter we address this research gap. We curated a gender-balanced set of 100 most-followed Indian journalists on Twitter and 100 most-followed politicians. Then we collected 21,188 unique tweets posted by these journalists that mentioned these politicians. Our analysis revealed that there is a significant gender bias -- the frequency with which journalists mention male politicians vs. how frequently they mention female politicians is statistically significantly different ($p<<0.05$). In fact, median tweets from female journalists mentioning female politicians received ten times fewer likes than median tweets from female journalists mentioning male politicians. However, when we analyzed tweet content, our emotion score analysis and topic modeling analysis did not reveal any significant gender-based difference within the journalists' tweets towards politicians. Finally, we found a potential reason for the significant gender bias: the number of popular male Indian politicians is almost twice as large as the number of popular female Indian politicians, which might have resulted in the observed bias. We conclude by discussing the implications of this work.

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