Increasing the veracity of event detection on social media networks through user trust modeling

Todd Bodnar, Conrad Tucker, Kenneth Hopkinson, Sven G. Bilen

Research output: Chapter in Book/Report/Conference proceedingConference contribution

24 Scopus citations

Abstract

With the success and ubiquity of large scale, social media networks comes the challenge of assessing the veracity of information shared across them that inform individuals about emerging real-world events and trends. We propose a veracity-assessment model for information dissemination on social media networks that combines natural language processing and machine learning algorithms to mine textual content generated by each user. Large scale social media networks (such as Twitter and Facebook) are considered digital communication platforms, in which information can be quickly and easily exchanged, thereby expanding the breadth of knowledge across the globe. In this paper, four case studies spanning multiple geographic regions, threat scenarios and time frames are investigated, in order to demonstrate how real-world events impact the manner in which information/misinformation is communicated and spread through a social media network. Our results show that metadata associated with each user can provide significant insight on the social media network's users' tendency to accurately discuss a topic.

Original languageEnglish (US)
Title of host publicationProceedings - 2014 IEEE International Conference on Big Data, IEEE Big Data 2014
EditorsWo Chang, Jun Huan, Nick Cercone, Saumyadipta Pyne, Vasant Honavar, Jimmy Lin, Xiaohua Tony Hu, Charu Aggarwal, Bamshad Mobasher, Jian Pei, Raghunath Nambiar
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages636-643
Number of pages8
ISBN (Electronic)9781479956654
DOIs
StatePublished - Jan 7 2015
Event2nd IEEE International Conference on Big Data, IEEE Big Data 2014 - Washington, United States
Duration: Oct 27 2014Oct 30 2014

Publication series

NameProceedings - 2014 IEEE International Conference on Big Data, IEEE Big Data 2014

Other

Other2nd IEEE International Conference on Big Data, IEEE Big Data 2014
CountryUnited States
CityWashington
Period10/27/1410/30/14

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All Science Journal Classification (ASJC) codes

  • Artificial Intelligence
  • Information Systems

Cite this

Bodnar, T., Tucker, C., Hopkinson, K., & Bilen, S. G. (2015). Increasing the veracity of event detection on social media networks through user trust modeling. In W. Chang, J. Huan, N. Cercone, S. Pyne, V. Honavar, J. Lin, X. T. Hu, C. Aggarwal, B. Mobasher, J. Pei, & R. Nambiar (Eds.), Proceedings - 2014 IEEE International Conference on Big Data, IEEE Big Data 2014 (pp. 636-643). [7004286] (Proceedings - 2014 IEEE International Conference on Big Data, IEEE Big Data 2014). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/BigData.2014.7004286