Crossroads - Winter 2009

[ 9, 19]. These works focused on elements of sociability, demographics, network characterizations, privacy implications, and similarity measures within social networking. Our work takes the next step by attempting to determine the validity of an identity on an online social community by mining self-described data.

The data mining techniques in our approach have a long history of use in crime detection [ 17], intrusion detection models [ 5], and lie detection [ 20, 12]. Our algorithm adds to the already existing set of tools that can be used to identify forms of deception in nonverbal communication [ 2].

Although our approach is feasible and our results are promising, there remain many topics to investigate in future work. First, having more data would result in greater accuracy. Second, a comparison of the effectiveness of the present approach against other social networking sites such as Facebook could shed light on its generalizability and robustness. Third, if a user is really attempting to pass off a fake identity, would the classifier be effective in detecting it? Finally, it would be interesting to experiment with the resiliency of our classifier to carefully crafted identities and investigate how this classifier could be used as evidence in computational trust systems.

Biography

Roya Feizy is a PhD student at the University of Sussex. She holds degrees in Applied Mathematics from Azad University in Iran and Multimedia and Computer Science achieved from Middlesex University. Her research interests include identity and online social networking, specifically looking at how individuals present themselves online, whether they are real or fake, and the type and amount of information they are willing to disclose.

References

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