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Diffusion of deception in social media: Social contagion effects and its antecedents

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  • Arun Vishwanath

    (SUNY at Buffalo)

Abstract

What makes deceptive attacks on social media particularly virulent is the likelihood of a contagion effect, where a perpetrator takes advantage of the connections among people to deceive them. To examine this, the current study experimentally stimulates a phishing type attack, termed as farcing, on Facebook users. Farcing attacks occur in two stages: a first stage where phishers use a phony profile to friend victims, and a second stage, where phishers solicit personal information directly from victims. In the present study, close to one in five respondents fell victim to the first stage attack and one in ten fell victim to the second stage attack. Individuals fell victim to a level 1 attack because they relied primarily on the number of friends or the picture of the requester as a heuristic cue and made snap judgments. Victims also demonstrated a herd mentality, gravitating to a phisher whose page showed more connections. Such profiles caused an upward information cascade, where each victim attracted many more victims through a social contagion effect. Individuals receiving a level 2 information request on Facebook peripherally focused on the source of the request by using the sender’s picture in the message as a credibility cue.

Suggested Citation

  • Arun Vishwanath, 2015. "Diffusion of deception in social media: Social contagion effects and its antecedents," Information Systems Frontiers, Springer, vol. 17(6), pages 1353-1367, December.
    • Handle: RePEc:spr:infosf:v:17:y:2015:i:6:d:10.1007_s10796-014-9509-2
    DOI: 10.1007/s10796-014-9509-2
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    Cited by:

    1. Isaac Owusu Asante & Jiaming Fang & Dennis Fiifi Darko & Hossin M. D. Altab, 2021. "Examining the Antecedents of User Donation Intentions Toward Social Media Articles: Moderation Effects of Social Contagion," SAGE Open, , vol. 11(1), pages 21582440211, March.
    2. Xin Huang & Fei Yan & Liqiang Zhang & Kai Wang, 2021. "HoneyGadget: A Deception Based Approach for Detecting Code Reuse Attacks," Information Systems Frontiers, Springer, vol. 23(2), pages 269-283, April.
    3. Xiaohui Zhang & Qianzhou Du & Zhongju Zhang, 2022. "A theory‐driven machine learning system for financial disinformation detection," Production and Operations Management, Production and Operations Management Society, vol. 31(8), pages 3160-3179, August.
    4. Paul M. Gangi & Allen C. Johnston & James L. Worrell & Samuel C. Thompson, 2018. "What could possibly go wrong? A multi-panel Delphi study of organizational social media risk," Information Systems Frontiers, Springer, vol. 20(5), pages 1097-1116, October.
    5. Reema Aswani & Arpan Kumar Kar & P. Vigneswara Ilavarasan, 2018. "Detection of Spammers in Twitter marketing: A Hybrid Approach Using Social Media Analytics and Bio Inspired Computing," Information Systems Frontiers, Springer, vol. 20(3), pages 515-530, June.
    6. Sheshadri Chatterjee & Ranjan Chaudhuri & Demetris Vrontis, 2023. "Role of fake news and misinformation in supply chain disruption: impact of technology competency as moderator," Annals of Operations Research, Springer, vol. 327(2), pages 659-682, August.
    7. Konstantina Spanaki & Thanos Papadopoulos & Uchitha Jayawickrama & Femi Olan & Shaofeng Liu, 2023. "Editorial: fake news, misinformation, and supply chain disruptions: the role of emerging technologies," Post-Print hal-04158399, HAL.
    8. Konstantina Spanaki & Thanos Papadopoulos & Uchitha Jayawickrama & Femi Olan & Shaofeng Liu, 2023. "Editorial: fake news, misinformation, and supply chain disruptions: the role of emerging technologies," Annals of Operations Research, Springer, vol. 327(2), pages 601-604, August.
    9. Xin Huang & Fei Yan & Liqiang Zhang & Kai Wang, 0. "HoneyGadget: A Deception Based Approach for Detecting Code Reuse Attacks," Information Systems Frontiers, Springer, vol. 0, pages 1-15.
    10. Sarraf, Shagun & Kushwaha, Amit Kumar & Kar, Arpan Kumar & Dwivedi, Yogesh K. & Giannakis, Mihalis, 2024. "How did online misinformation impact stockouts in the e-commerce supply chain during COVID-19 – A mixed methods study," International Journal of Production Economics, Elsevier, vol. 267(C).
    11. Nan Jing & Zhao Wu & Shanshan Lyu & Vijayan Sugumaran, 2021. "Information credibility evaluation in online professional social network using tree augmented naïve Bayes classifier," Electronic Commerce Research, Springer, vol. 21(2), pages 645-669, June.
    12. Kawaljeet Kaur Kapoor & Kuttimani Tamilmani & Nripendra P. Rana & Pushp Patil & Yogesh K. Dwivedi & Sridhar Nerur, 2018. "Advances in Social Media Research: Past, Present and Future," Information Systems Frontiers, Springer, vol. 20(3), pages 531-558, June.
    13. Paul M. Gangi & Allen C. Johnston & James L. Worrell & Samuel C. Thompson, 0. "What could possibly go wrong? A multi-panel Delphi study of organizational social media risk," Information Systems Frontiers, Springer, vol. 0, pages 1-20.

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