In online discussion communities, users can interact and share information and opinions on a wide variety of topics. However, some users may create multiple identities, or sockpuppets, and engage in undesired behavior by deceiving others or manipulating discussions. In this work, we study sockpuppetry across nine discussion communities, and show that sockpuppets differ from ordinary users in terms of their posting behavior, linguistic traits, as well as social network structure. Sockpuppets tend to start fewer discussions, write shorter posts, use more personal pronouns such as I'', and have more clustered ego-networks. Further, pairs of sockpuppets controlled by the same individual are more likely to interact on the same discussion at the same time than pairs of ordinary users. Our analysis suggests a taxonomy of deceptive behavior in discussion communities. Pairs of sockpuppets can vary in their deceptiveness, i.e., whether they pretend to be different users, or their supportiveness, i.e., if they support arguments of other sockpuppets controlled by the same user. We apply these findings to a series of prediction tasks, notably, to identify whether a pair of accounts belongs to the same underlying user or not. Altogether, this work presents a data-driven view of deception in online discussion communities and paves the way towards the automatic detection of sockpuppets.
The unknown and the invisible exploit the unwary and the uninformed for illicit financial gain and reputation damage. Whistleblowers and activists need the ability to communicate without disclosing their identity, as of course do kidnappers and terrorists. Recent advances in the technology of stylometry (the study of authorial style) or "authorship attribution" have made it possible to identify the author with high reliability in a non-confrontational setting. In a confrontational setting, where the author is deliberately masking their identity (i.e. attempting to deceive), the results are much less promising. In this paper, we show that although the specific author may not be identifiable, the intent to deceive and to hide his identity can be. We show this by a reanalysis of the Brennan and Greenstadt (2009) deception corpus and discuss some of the implications of this surprising finding. Identity plays a key role in virtual communities. In communication, which is the primary activity, knowing the identity of those with whom you communicate is essential for understanding and evaluating an interaction. Yet in the disembodied world of the virtual community, identity is also ambiguous. Many of the basic cues about personality and social role we are accustomed to in the physical world are absent. The goal of this paper is to understand how identity is established in an online community and to examine the effects of identity deception and the conditions that give rise to it. This article investigates whether deceptions in online dating profiles correlate with changes in the way daters write about themselves in the free-text portion of the profile, and whether these changes are detectable by both computerized linguistic analyses and human judges. Computerized analyses (Study 1) found that deceptions manifested themselves through linguistic cues pertaining to (a) liars’ emotions and cognitions and (b) liars’ strategic efforts to manage their self-presentations. Technological affordances (i.e., asynchronicity and editability) affected the production of cognitive cues more than that of emotional cues. Human judges (Study 2) relied on different and nonpredictive linguistic cues to assess daters’ trustworthiness. The findings inform theories concerned with deception, media, and self-presentation, and also expound on how writing style influences perceived trustworthiness. Advancing our knowledge about cues to deception is crucial to successful deception detection. A lengthy list of cues to deception has been identified via a myriad of deception studies. Nonetheless, we identified two major limitations of existing cues to deception: the lack of cues in computer-mediated communication and in non-Western group communication. In this research, we aim to make some contributions to addressing this line of inquiry. We conducted an empirical study on cues to deception using a large real-world online Chinese community. Through hypotheses testing, we observed a number of interesting findings. For example, we found that deceivers tended to communicate less and showed low complexity and high diversity in their messages. These findings provide significant implications to deception research and the broad online communication community.  In digital forensics, questions often arise about the authors of documents: their identity, demographic background, and whether they can be linked to other documents. The field of stylometry uses linguistic features and machine learning techniques to answer these questions. While stylometry techniques can identify authors with high accuracy in non-adversarial scenarios, their accuracy is reduced to random guessing when faced with authors who intentionally obfuscate their writing style or attempt to imitate that of another author. While these results are good for privacy, they raise concerns about fraud. We argue that some linguistic features change when people hide their writing style and by identifying those features, stylistic deception can be recognized. The major contribution of this work is a method for detecting stylistic deception in written documents. We show that using a large feature set, it is possible to distinguish regular documents from deceptive documents with 96.6 accuracy (F-measure). We also present an analysis of linguistic features that can be modified to hide writing style. 