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Identifying Shooting Tweets with Deep Learning and Keywords Filtering: Comparative Study

Abdelhalim Mohamed, Ammar Ahmed
http://orcid.org/0000-0002-5976-2945
http://rave.ohiolink.edu/etdc/view?acc_num=ucin1617105265912086

Abstract Details

2021, MS, University of Cincinnati, Education, Criminal Justice, and Human Services: Information Technology.
During large-scale crises, 911 call centers often become inundated by high call volume, making it difficult for citizens to request help. When this is the case, people may turn to social media for support. This also happens when someone may wish to discuss an incident, such as hearing gunshots, but feel unsure if calling 911 is the most appropriate action. 911 does not typically monitor social media platforms for these types of requests due to challenges in sorting and filtering relevant information. To support the fast identification of important information to be shared with first responders, this research focuses on analyzing social media posts to determine the relevancy of social media posts about shooting incidents and emergencies. It compares the accuracy and relevancy of two methods of filtering social media data. The first is filtering tweets using keywords related to shooting and manually labeling them based on their relevancy to shooting events. The second method is by training a Transfer Learning model to determine the relevancy of collected tweets. The comparison results show that the machine learning technique is more accurate in identifying relevant tweets than the keyword filtering technique.
Jess Kropczynski, Ph.D. (Committee Chair)
Shane Halse, Ph.D. (Committee Member)
48 p.
Information Technology
Social Media; Crises Management; Crises Response; Shooting; Transfer Learning; Keyword Filtering

Recommended Citations

Citations

  • Abdelhalim Mohamed, A. A. (2021). Identifying Shooting Tweets with Deep Learning and Keywords Filtering: Comparative Study [Master's thesis, University of Cincinnati]. OhioLINK Electronic Theses and Dissertations Center. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1617105265912086

    APA Style (7th edition)

  • Abdelhalim Mohamed, Ammar Ahmed. Identifying Shooting Tweets with Deep Learning and Keywords Filtering: Comparative Study. 2021. University of Cincinnati, Master's thesis. OhioLINK Electronic Theses and Dissertations Center, http://rave.ohiolink.edu/etdc/view?acc_num=ucin1617105265912086.

    MLA Style (8th edition)

  • Abdelhalim Mohamed, Ammar Ahmed. "Identifying Shooting Tweets with Deep Learning and Keywords Filtering: Comparative Study." Master's thesis, University of Cincinnati, 2021. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1617105265912086

    Chicago Manual of Style (17th edition)

ucin1617105265912086
644
© 2021, some rights reserved.Creative Commons License Identifying Shooting Tweets with Deep Learning and Keywords Filtering: Comparative Study by Ammar Ahmed Abdelhalim Mohamed is licensed under a Creative Commons Attribution-NonCommercial-NoDerivs 3.0 Unported License. Based on a work at etd.ohiolink.edu.
This open access ETD is published by University of Cincinnati and OhioLINK.