Online Deceit: The Use Of Idiosyncratic Cues In Identifying Duplicitous User-Generated Content
Abstract
The emergence of online information-seekers harnessing the aggregated experiences of others to evaluate online information has coincided with deceptive entities exploiting this tool to bias judgments. One method through which deceit about user-generated content can occur is through single entities impersonating multiple, independent content providers to saturate content samples. Two studies are introduced to explore how idiosyncratic indicators, features co-occurring between content messages that implicate a higher probability of deceit, can be used as a criterion to identify content that is not independently authored. In Study 1, analyses of a pairwise comparison of hypothetical reviews revealed that ratings of content independence were significantly lower when review pairs co-occurred in the attributes, text, and usernames compared to being heterogenous. In a high-fidelity experiment, Study 2 assessed if the effect of idiosyncratic indicators on independence is increased in the presence of multiple indicators, if it is attenuated with a high number of reviews, and if it impacts factors relevant to the choice selection process. As expected, the findings of Study 1 were replicated in addition to further revealing that the presence of multiple idiosyncratic cues yielded lower independence ratings. An interaction effect with idiosyncratic indicators and high review number was observed such that the effect of the former on independence was attenuated when there were a high number of reviews to obscure the presence of these indicators.
Degree
Ph.D.
Advisors
Reimer, Purdue University.
Subject Area
Communication|Mass communications|Web Studies
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