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

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.

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Notes

  1. 1.

    Research questions 1, 2, and 5 examined whether the cues in the farcing request significantly predicted the individuals’ likelihood to get deceived. The moderating effect of gender was tested in each of these models. In testing gender effects in research question 1, the 2 × 2 × 2 ordinal regression (picture x friend x gender) was significant (χ2 (6) = 16.40, p < .05) but the gender effects were not significant (β = 0.19, SE = .59, Wald (1) = .09, p = .75). In testing research question 2, the logistic model was not significant (χ2 (3) = 9.70, p = .08) and the main effects of gender were also not significant (β = −.02, SE = .51, Wald (1) = .001, p = .97). Finally, in testing research question 5, the 2 × 2 × 2 ordinal regression that included gender in the model was not significant χ2 (6) = 4.40, p = .62, and the gender effects were also not significant (β = 0.38, SE = .58, Wald (1) = .42, p = .52).

  2. 2.

    A two-way ANOVA alternatively tested the influence of picture cues vs. friend cues on the likely response to the friend-request. The 2 × 2 ANOVA was significant (F(3,140) = 4.92, p < .05, η2 = .10). The main effect of friend cues was significant (F(1,140) = 10.75, p < .05, η2 = .07) and the follow-up contrasts suggested that individuals were significantly more likely to consider accepting a friend-request (M = 1.98, SD = 0.71) when the request came from a stranger with many friends than one without no friends (M = 1.63, SD = 0.71). The interaction effects were also significant (F(1,140) = 4.22, p < .05, η2 = .03). The interaction tests indicated that in the absence of a picture of the sender, individuals were significantly more likely to consider friend-requests from a stranger with many friends (M = 2.16, SD = .65) rather than from a sender with no friends (M = 1.52, SE = .68).

  3. 3.

    As an alternative to the ordinal regression, a 2 × 2 (friend cues vs. picture cues) ANCOVA with prior response to the friend request as the covariate was estimated. The overall model was significant (F(4,138) = 6.39, p < .05, η2 = .16. The main effect of picture cues and friend cues and the interaction effects were non-significant. Only the covariate effect was significant (F(1,138) = 22.76, p < .05, η2 = .14, indicating that individual response to the level 2 information request was influenced by their response to the level 1 attack.

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Correspondence to Arun Vishwanath.

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Appendix

Table 2 Testing the significance of cubic regression coefficients

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Vishwanath, A. Diffusion of deception in social media: Social contagion effects and its antecedents. Inf Syst Front 17, 1353–1367 (2015). https://doi.org/10.1007/s10796-014-9509-2

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Keywords

  • IT diffusion and adoption
  • Social contagion
  • Computer-mediated communication and collaboration
  • Laboratory experiments
  • Social media
  • Online deception
  • Phishing