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A Trust-Based Tool for Detecting Potentially Damaging Users in Social Networks

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Advances in Networked-Based Information Systems (NBiS 2021)

Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 313))

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Abstract

We offer a trust-based framework to detect potentially damaging users in social networks. This method captures human and community trust within the network to identify users who are likely to cause harm. Human and community-based frameworks offer an advantage over other approaches because trust and credibility are hard to fake as they are built over time. Furthermore, given that the metric represents human trust with evidence, it serves as an excellent label to inform users of potential damage associated with another account. We illustrate the proposed metric with Twitter data, distinguishing potentially damaging users from both trustworthy users and those who lack trustworthiness but have a low chance of causing harm.

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Notes

  1. 1.

    https://www.facebook.com/formedia/blog/working-to-stop-misinformation-and-false-news.

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Acknowledgments

This work was partially supported by the National Science Foundation under No. 1547411 and by the U.S. Department of Agriculture (USDA), National Institute of Food and Agriculture (NIFA) (Award Number 2017-67003-26057) via an interagency partnership between USDA-NIFA and the National Science Foundation (NSF) on the research program Innovations at the Nexus of Food, Energy and Water Systems.

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Authors and Affiliations

  1. Indiana University-Purdue University Indianapolis, Indianapolis, IN, USA

    Kaley J. Rittichier, Davinder Kaur, Suleyman Uslu & Arjan Durresi

Authors
  1. Kaley J. Rittichier
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  2. Davinder Kaur
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  3. Suleyman Uslu
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  4. Arjan Durresi
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Corresponding author

Correspondence to Arjan Durresi .

Editor information

Editors and Affiliations

  1. Department of Information and Communication Engineering, Fukuoka Institute of Technology, Fukuoka, Japan

    Leonard Barolli

  2. Department of Computer Science and Information Engineering, Asia University, Taichung, Taiwan

    Hsing-Chung Chen

  3. Faculty of Business Administration, Rissho University, Tokyo, Japan

    Tomoya Enokido

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Rittichier, K.J., Kaur, D., Uslu, S., Durresi, A. (2022). A Trust-Based Tool for Detecting Potentially Damaging Users in Social Networks. In: Barolli, L., Chen, HC., Enokido, T. (eds) Advances in Networked-Based Information Systems. NBiS 2021. Lecture Notes in Networks and Systems, vol 313. Springer, Cham. https://doi.org/10.1007/978-3-030-84913-9_9

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