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A Multi-Agent Model to Study the Effects of Crowdsourcing on the Spread of Misinformation in Social Networks.

Bhattacharya, Ankur
http://orcid.org/0009-0009-4908-8369
http://rave.ohiolink.edu/etdc/view?acc_num=ucin1684770124758418

Abstract Details

2023, MS, University of Cincinnati, Engineering and Applied Science: Computer Science.
The last decade has seen a considerable uptick in the spread of misinformation through the medium of Online Social Networks (OSNs) such as Facebook and Twitter. About 47% of Americans are receiving their daily news via social media with Facebook being the most dominant source of their information. Facebook has about 210 million American users averaging to about 98.7 million Americans who rely on Facebook for their news. Due to the rise in the spread of misinformation through these platforms, we also find a rise in the amount of research on the topic in the past few years. A typical AI-based approach in this regard requires semantic analysis of text to derive meaning, which proves to be quite difficult due to differences in regions and languages. Alternative approaches study the behavior of participants under different circumstances when introduced to false information. Recent findings show that improving news literacy amongst individuals leads to a reduction in the spread of misinformation. The work in this thesis focuses on targeting news literate individuals in an abstract OSN, and utilizing crowdsourcing mechanics to reduce the rate of spread of misinformation in the system. We modify the traditional SIR model of disease spread to treat each article as an individual disease such that individuals in a network can asynchronously interact with multiple articles. We then evaluate the performance of the model under different rates of article generation and different fractions of false articles in the system.
Ali Minai, Ph.D. (Committee Chair)
Yizong Cheng, Ph.D. (Committee Member)
Raj Bhatnagar, Ph.D. (Committee Member)
105 p.
Computer Science
Social Networks; Multi-Agent Systems; Fake News; Viral Misinformation

Recommended Citations

Citations

  • Bhattacharya, A. (2023). A Multi-Agent Model to Study the Effects of Crowdsourcing on the Spread of Misinformation in Social Networks. [Master's thesis, University of Cincinnati]. OhioLINK Electronic Theses and Dissertations Center. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1684770124758418

    APA Style (7th edition)

  • Bhattacharya, Ankur. A Multi-Agent Model to Study the Effects of Crowdsourcing on the Spread of Misinformation in Social Networks. 2023. University of Cincinnati, Master's thesis. OhioLINK Electronic Theses and Dissertations Center, http://rave.ohiolink.edu/etdc/view?acc_num=ucin1684770124758418.

    MLA Style (8th edition)

  • Bhattacharya, Ankur. "A Multi-Agent Model to Study the Effects of Crowdsourcing on the Spread of Misinformation in Social Networks." Master's thesis, University of Cincinnati, 2023. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1684770124758418

    Chicago Manual of Style (17th edition)

ucin1684770124758418
64
© 2023, some rights reserved.Creative Commons License A Multi-Agent Model to Study the Effects of Crowdsourcing on the Spread of Misinformation in Social Networks. by Ankur Bhattacharya is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 3.0 Unported License. Based on a work at etd.ohiolink.edu.
This open access ETD is published by University of Cincinnati and OhioLINK.