In this work, we investigate the role of offspring hashtags on the semantic polarization of online discourse between the protest and counter-protest communities over time through the lens of the 2021 farmers' protest in India. Offspring hashtags are those that first appear alongside their more widely known "parent" hashtag (e.g., #WhyIDidntReport and #YesAllWomen are offspring hashtags that first co-appeared alongside their more famous and mainstream parent hashtag, #MeToo). The prominence of parent hashtags and their visible role in facilitating modern day protests have dominated scholarly efforts in understanding the socio-technical influence of social movement hashtags. By contrast, scholarship on the impact of the lesser-known offspring hashtags is rare and if any, typically examined through the lens of its primary parent tag. Our work aims to address this gap. In this research, we examine how the protest and counter-protest communities use offspring hashtags in their tweets to discuss and frame farmers - the key social group at the center of the farmers' protest (RQ1). Our findings reveal that both protests and counter-protests use offspring hashtags in a manner that further polarizes rather than bridges perspectives on core issues - focusing on themes that malign the other side (RQ2). We then measure and track how the semantic polarization in the use of the term "farmer" by the protest vs.
counter-protest communities who use offspring hashtags evolves over time in relation to key protest events (RQ3). Finally, to empirically test and demonstrate whether and how the volume of offspring hashtags throughout the protest period influences semantic polarization trends between the protest and counter-protest discussion of farmers, we create a series of time-series models for causal inference. We use Granger-causality to test whether and how fluctuations in the volume of offspring hashtags significantly predict how the protest and counter-protest communities semantically diverge in how they discuss farmers over time (RQ4). By analyzing offspring hashtags, this work provides a detailed understanding of the nuanced themes and narratives that may be lost under parent hashtags, but significantly influence online discourse between the protest and counter-protest communities. / Master of Science / In this study, we explore how offspring hashtags, impact online conversations between people supporting and opposing the 2021 farmers' protest in India. Offspring hashtags are less popular hashtags that first appear with a more famous "parent" hashtag (for example, #WhyIDidntReport and #YesAllWomen alongside #MeToo). While researchers have extensively studied parent hashtags, the influence of offspring hashtags remains less explored.
Our research looks at how protests and counter-protests use offspring hashtags to talk about farmers, who are at the center of the Indian farmers' protest. We found that both groups use offspring hashtags in a way that increases polarization rather than fostering understanding between opposing sides. This often leads to discussions that focus on attacking the other group. We also analyzed how the polarization in conversations about farmers evolved over time, in relation to key protest events and the use of offspring hashtags. To see if the number of offspring hashtags used during the protest affected polarization trends, we used statistical models and a method called Granger-causality. Our findings show that fluctuations in offspring hashtag volume significantly predict how protesters and counter-protesters diverge in their discussions about farmers over time. By examining offspring hashtags, we gain a deeper understanding of the subtle themes and stories that may be overlooked when focusing only on parent hashtags but play a crucial role in shaping online conversations between opposing groups.
| Identifer | oai:union.ndltd.org:VTETD/oai:vtechworks.lib.vt.edu:10919/115673 |
| Date | 06 July 2023 |
| Creators | Leekha, Rohan Singh |
| Contributors | Computer Science and Applications, Rho, Ha Rim, Fox, Edward A., Lourentzou, Ismini |
| Publisher | Virginia Tech |
| Source Sets | Virginia Tech Theses and Dissertation |
| Language | English |
| Detected Language | English |
| Type | Thesis |
| Format | ETD, application/pdf |
| Rights | In Copyright, http://rightsstatements.org/vocab/InC/1.0/ |
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