Abstract
Identity deception in online social networks is a pervasive problem. Ongoing research is developing methods for identity deception detection. However, the real-world efficacy of these methods is currently unknown because they have been evaluated largely through laboratory experiments. We present a review of representative state-of-the-art results on identity deception detection. Based on this analysis, we identify common methodological weaknesses for these approaches, and we propose recommendations that can increase their effectiveness for when they are applied in real-world environments.
Highlights
The spread of deceptive behaviors online has strongly reshaped the way that people interact
Our work focuses primarily on identity deception detection methods as they are applied in online social networks
The limitation of available datasets can often lead to studies utilizing simulated data that are based on real-world observations and assumptions. The effect of these practices is similar to data sanitization [34] with the additional risk that the assumptions about user behavior that deception detection studies make to generate datasets can influence both internal and external validity. Another dataset weakness we found in recent literature is the fact that all studies we referenced in this work have exclusively used a single Online Social Networks (OSNs) dataset; as such, the identity deception detection models may be highly customized for a specific OSN, and such models are unlikely to be able to generalize to other OSNs
Summary
The spread of deceptive behaviors online has strongly reshaped the way that people interact. Several methods have presented highly efficient results in detecting identity deception. We conduct an analysis of the key causes that could be responsible for the efficiency gap between lab results and real-world implementations by analyzing the research approaches on identity deception detection that have been reported in the literature recently. We identify the key shortcomings that may inhibit the effectiveness of these methods when implemented in real-world online social networks.
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