Events

[HCIL] Kimberly Glasgow's Dissertation Defense

Event Start Date:
Friday, April 23, 2021 - 09:00 AM
Event End Date:
Friday, April 23, 2021 - 11:00 AM
Location
Virtual
Add to Calendar 2021-04-23 09:00:00 2021-04-23 11:00:00 [HCIL] Kimberly Glasgow's Dissertation Defense Talking About Justice: Predicting Actor Engagement on Social Media After a Galvanizing Event Request Zoom Information Abstract: Social media contributes to discourse around and framing of major societal issues, and enables community formation, social change, and activism. It provides opportunities to engage in discourse, gain and share knowledge, and form ties with others around an issue, topic, or cause. This dissertation explores how justice, an important concept underlying social systems, is expressed in Twitter data in the context of high-salience, galvanizing local events, and leverages that information to predict whether newcomers to the issue will continue their digital engagement on the topic over time. It also attempts to quantify whether, and how much, a set of factors or dimensions previously associated with engagement in the physical realm contribute to digital engagement. These dimensions—identity, emotion, effort, and social embeddedness—are informed by prior work on social movements, digital activism, and related fields. Rather than rely on hashtags, this dissertation uses machine learning to detect justice-related Twitter activity. This advance in methods provides a richer understanding of discourse around a complex, multifaceted topic like justice. It allows deeper insight into the social media activity of newcomers to the justice community, and the networks they are embedded in. The approach is developed and applied first to Twitter data from Baltimore around the 2015 death of Freddie Gray from injuries sustained while in police custody, and the protests and riots that followed in Baltimore.   To test for generalizability, the same approach is then applied to a second dataset, collected from Cleveland at the time of the death of Tamir Rice, who was shot and killed by police in 2014. Findings show that digital engagement in justice discourse on social media can be predicted, based on aspects of social embeddedness, emotion, and effort. To the degree that committed individuals are at the heart of social movements and efforts to spur social and civic change, and forming and being embedded in appropriate network structures is critical for channeling commitment into action and eventual success, this work contributes to greater understanding of these phenomena. Findings from this research could contribute to the design of technology to support civic engagement through social media platforms. Virtual America/New_York public

Talking About Justice: Predicting Actor Engagement on Social Media After a Galvanizing Event

Request Zoom Information

Abstract:
Social media contributes to discourse around and framing of major societal issues, and enables community formation, social change, and activism. It provides opportunities to engage in discourse, gain and share knowledge, and form ties with others around an issue, topic, or cause. This dissertation explores how justice, an important concept underlying social systems, is expressed in Twitter data in the context of high-salience, galvanizing local events, and leverages that information to predict whether newcomers to the issue will continue their digital engagement on the topic over time. It also attempts to quantify whether, and how much, a set of factors or dimensions previously associated with engagement in the physical realm contribute to digital engagement. These dimensions—identity, emotion, effort, and social embeddedness—are informed by prior work on social movements, digital activism, and related fields. Rather than rely on hashtags, this dissertation uses machine learning to detect justice-related Twitter activity. This advance in methods provides a richer understanding of discourse around a complex, multifaceted topic like justice. It allows deeper insight into the social media activity of newcomers to the justice community, and the networks they are embedded in. The approach is developed and applied first to Twitter data from Baltimore around the 2015 death of Freddie Gray from injuries sustained while in police custody, and the protests and riots that followed in Baltimore.  

To test for generalizability, the same approach is then applied to a second dataset, collected from Cleveland at the time of the death of Tamir Rice, who was shot and killed by police in 2014. Findings show that digital engagement in justice discourse on social media can be predicted, based on aspects of social embeddedness, emotion, and effort. To the degree that committed individuals are at the heart of social movements and efforts to spur social and civic change, and forming and being embedded in appropriate network structures is critical for channeling commitment into action and eventual success, this work contributes to greater understanding of these phenomena. Findings from this research could contribute to the design of technology to support civic engagement through social media platforms.