UNDERSTANDING SOCIAL MOVEMENTS THROUGH SIMULATIONS OF ANGER CONTAGION IN SOCIAL MEDIA

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Bacaksizlar, N. (2019). UNDERSTANDING SOCIAL MOVEMENTS THROUGH SIMULATIONS OF ANGER CONTAGION IN SOCIAL MEDIA. Unc Charlotte Electronic Theses And Dissertations.
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Abstract

This dissertation investigates emotional contagion in social movements within social media platforms, such as Twitter. The main research question is: How does a protest behavior spread in social networks? The following sub-questions are: (a) What is the dynamic behind the anger contagion in online social networks? (b) What are the key variables for ensuring emotional spread? We gained access to Twitter data sets on protests in Charlotte, NC (2016) and Charlottesville, VA (2017). Although these two protests differ in their triggering points, they have similarities in their macro behaviors during the peak protest times. To understand the influence of anger spread among users, we extracted user mention networks from the data sets. Most of the mentioned users are influential ones, who have a significant number of followers. This shows that influential users occur as the highest in-degree nodes in the core of the networks, and a change in these nodes affects all connected public users/nodes. Then, we examined modularity measures quite high within users’ own communities. After implementing the networks, we ran experiments on the anger spread according to various theories with two main assumptions: (1) Anger is the triggering emotion for protests and (2) Twitter mentions affect distribution of influence in social networks. We found that user connections with directed links are essential for the spread of influence and anger; i.e., the angriest users are the most isolated ones with less number of followers, which signifies their low impact level in the network.

Details
Author

Bacaksizlar, Nazmiye

Title

UNDERSTANDING SOCIAL MOVEMENTS THROUGH SIMULATIONS OF ANGER CONTAGION IN SOCIAL MEDIA

Physical Description

1 online resource (103 pages) : PDF

Date

2019

Degree Granting Institution

University of North Carolina at Charlotte

Abstract

This dissertation investigates emotional contagion in social movements within social media platforms, such as Twitter. The main research question is: How does a protest behavior spread in social networks? The following sub-questions are: (a) What is the dynamic behind the anger contagion in online social networks? (b) What are the key variables for ensuring emotional spread? We gained access to Twitter data sets on protests in Charlotte, NC (2016) and Charlottesville, VA (2017). Although these two protests differ in their triggering points, they have similarities in their macro behaviors during the peak protest times. To understand the influence of anger spread among users, we extracted user mention networks from the data sets. Most of the mentioned users are influential ones, who have a significant number of followers. This shows that influential users occur as the highest in-degree nodes in the core of the networks, and a change in these nodes affects all connected public users/nodes. Then, we examined modularity measures quite high within users’ own communities. After implementing the networks, we ran experiments on the anger spread according to various theories with two main assumptions: (1) Anger is the triggering emotion for protests and (2) Twitter mentions affect distribution of influence in social networks. We found that user connections with directed links are essential for the spread of influence and anger; i.e., the angriest users are the most isolated ones with less number of followers, which signifies their low impact level in the network.

Genre

doctoral dissertations

Subjects--Topics

Sociology
Information technology
System theory

Degree

Ph.D.

Keywords

Agent-Based Modeling
Computational Social Science
Emotion Contagion
Natural Language Processing
Social Media
Social Movements

Subject Area

Information Technology

Advisor(s)

Hadzikadic, Mirsad

Committee Members

Shaikh, Samira
Ras, Zbigniew W.
Maestas, Cherie

Degree Note

Thesis (Ph.D.)--University of North Carolina at Charlotte, 2019.

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Rights Holder Information

Copyright is held by the author unless otherwise indicated.

Identifier

Bacaksizlar_uncc_0694D_11978

Permalink

http://hdl.handle.net/20.500.13093/etd:1406