Mass Mobilization in the Middle East: Form, Perception, and Language
| Autor: | Smith, Evann |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2016 |
| Předmět: | |
| Druh dokumentu: | Text |
| Popis: | This dissertation consists of three separate but related papers on mass mobilization in the Middle East. The first paper investigates the landscape of collective resistance and empowerment struggles in the Middle East. It exploits new data that catalogues mass political movements in the 19 countries of the Middle East and North Africa from 1900 to 2012 to offer a framework for understanding two basic aspects of mass political movements in the region: the forms such movements take, and the forms that are more likely to emerge and endure. Using Latent Class Analysis, it develops a complete typology of mass political movements in the Middle East based on three central aspects of mass mobilization--organization, collective identity, and action--and finds evidence that these three aspects not only constitute three dimensions of difference in mass movements that are orthogonal, but that each ranges from "fluid" to "stable" extremes, which jointly determine the likelihood of movements forming and deforming. The second paper explores how the occurrence of mass movements in the Middle East affects individual citizens' perceived economic grievances. By pairing public opinion data with the new data on mass movements in the Middle East, it finds a strong and consistent negative relationship between the occurrence of mass mobilization and individual perceptions of well-being. Using causal mediation analysis, however, it finds no evidence that this relationship is the product of real economic or institutional declines. Instead, it finds consistent evidence that mass movements directly and negatively impact individuals' perceptions and that this is plausibly the product of three psychological processes, which suggest an alternative micro-level explanation for "cycles of contention." The third paper develops a computer-assisted keyword-based approach to the retrieval and identification of Arabic dialects--which pose a distinct challenge to the machine processing of languages--that systematically incorporates machine learning and human expertise in a manner that is fast, efficient, transparent, and effective. Using a dataset of over 11 million tweets, it then applies this approach to an analysis of the linguistic character of Arabic Twitter during the 2013 Egyptian protests, which led to the military coup of Egypt's first democratically elected president. Analysis of the linguistic trends indicates that spikes of dialectical Arabic mark two notable types of discourse: 1) reporting and reacting in real-time to unexpected events, and 2) capturing major emotional responses to landmark events, which "take the temperature" of the country's politically engaged population. Government |
| Databáze: | Networked Digital Library of Theses & Dissertations |
| Externí odkaz: |
|