Stylometric Identification in Electronic Markets: Scalability and Robustness

Online reputation systems are intended to facilitate the propagation of word of mouth as a credibility scoring mechanism for improved trust in electronic 50 AbbASI, chEN, AND NuNAMAkEr marketplaces. however, they experience two problems attributable to anonymity abuse—easy identity changes and reputation manipulation. In this study, we propose the use of stylometric analysis to help identify online traders based on the writing style traces inherent in their posted feedback comments. We incorporated a rich stylistic feature set and developed the Writeprint technique for detection of anonymous trader identities. The technique and extended feature set were evaluated on a test bed encompassing thousands of feedback comments posted by 200 ebay traders. Experiments conducted to assess the scalability (number of traders) and robustness (against intentional obfuscation) of the proposed approach found it to significantly outperform benchmark stylometric techniques. The results indicate that the proposed method may help militate against easy identity changes and reputation manipulation in electronic markets. key words And phrAses: anti-aliasing, electronic markets, online trust, similarity detection, stylometry. electronic mArkets hAve seen unprecedented growth in recent years. Online auction marketplaces such as ebay are one type of electronic market that has become especially popular. however, the lack of physical contact and prior interaction makes such places more susceptible to opportunistic member behavior [40]. While reputation systems attempt to alleviate some of the troubles with electronic markets, these systems suffer from two problems—easy identity changes and reputation manipulation. Easy identity changes stem from the fact that online traders can create new identities, thereby refreshing their reputation [10]. reputation manipulation allows online market traders to inflate their reputations using multiple identities or to sabotage competitors’ reputation scores. consequently, fraud and deception are highly prevalent in electronic markets, particularly in online auctions, which account for 50 percent of Internet fraud [9]. The aforementioned problems stem from online anonymity. however, individuals leave behind textual traces of their identity in the feedback comments posted to other traders. Stylometric similarity detection techniques applied to reputation system feedback comments can help minimize problems stemming from anonymity abuses in reputation systems. These techniques attempt to assess the degree of similarity between individuals based on writing style. Since text traces are often the only identity cues left behind in cyberspace, researchers have begun to use online stylometric analysis techniques as a forensic tool. They have recently been applied to e-mail, Web forums, and program code [11, 19, 49], as well as group support system comments [21, 22]. Despite significant progress, online stylometry has several current limitations. Most previous work focused on the identification task (where potential authorship identities are known in advance). There has been limited evaluation of similarity detection techniques where no identities are known a priori, and are clustered based on their similarity scores. Similarity detection is more practical for cyberspace applications, such as reputation systems. Furthermore, there has been a lack of evaluation of the scalability of stylometric analysis in terms of number of authors and identities per author for reputation systems. Moreover, there has been a lack of assessment of roSTyLOMETrIc IDENTIFIcATION IN ELEcTrONIc MArkETS 51 bustness against intentional stylistic alteration and message copycatting or forging. In this study, we propose a system that can provide stylometric analysis scalability and robustness for identifying traders in online reputation systems based on their feedback comments posted for others. The proposed system is highly accurate at differentiating across hundreds of identities based on stylistic tendencies inherent in feedback comments, and is also fairly robust against intentional stylistic alteration. The system uses an extended feature set consisting of several static and dynamic feature categories and also includes the Writeprint technique, which assesses the degree of stylistic similarity and dissimilarity between authors. Writeprint uses karhunen–Loeve transforms to assess the degree of similarity between traders and a pattern disruption mechanism to determine stylistic dissimilarity. The system can be used for similarity detection in reputation systems to alleviate the identity change and rank manipulation problems.