The U.S. Department of State disseminates counter-radicalization information through social media but has been unable to reach users due to an inability to create engaging posts due to a lack of understanding of the interests of the general population. The purpose of this quantitative study was to assess the utility of data analytics when administering counter-radicalization social media campaigns. The population for this study were social media posts published on the Quilliam Facebook page between 1 January 2018 and 31 December 2018. The nonexperimental quantitative descriptive research design sought to examine the correlation between the independent variables (topic of a post, use of visual aids in the post, and the geopolitical region the post addresses) and the dependent variables (resulting likes and shares). This study relied on the strategic choice theory which argues that individuals perform a cost and benefit analysis when deciding to join a terrorist organization and commit acts of terrorism. Specifically, individuals are often interested in participating in terror-ism in an effort to gain resources and feel a sense of belonging but can be dissuaded upon realization that terrorism can actually degrade their quality of life. The research found that social media can be used as a tool to increase the perceived costs of terrorism and decrease the perceived benefits of terrorism. The study concluded that posts which involved a personal story emphasizing the ramifications of terrorism and included a video resulted in the highest number of likes and shares, respectively. The findings provide a strong argument for utilizing data analytics to improve the dissemination of counter-radicalization information which could prevent individuals from joining terrorist organizations and committing acts of terrorism.
| Identifer | oai:union.ndltd.org:waldenu.edu/oai:scholarworks.waldenu.edu:dissertations-8733 |
| Date | 01 January 2019 |
| Creators | Berman, Ellen |
| Publisher | ScholarWorks |
| Source Sets | Walden University |
| Language | English |
| Detected Language | English |
| Type | text |
| Format | application/pdf |
| Source | Walden Dissertations and Doctoral Studies |
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