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.
Sockpuppetry is situated in the broader field of deception. Deception online is aided by the virtue of anonymity @cite_3 . It can occur as deceptive content as well as a deceptive agent @cite_38 . The behavior of people changes when they deceive, for example, they reduce communication @cite_43 and change the focus of their presentation @cite_44 . When writing deceptively to hide their identity, authors tend to increase use of particles and personal pronouns, write shorter sentences, and show nervousness @cite_23 @cite_27 @cite_36 @cite_15 . Our work adds to this line of research by finding evidence of deceptive writing styles and presentation by sockpuppets -- pretender sockpuppets may pretend to be different people by using different display names and they tend to write deceptively as well.
@cite_38 @cite_36 @cite_3 @cite_44 @cite_43 @cite_27 @cite_23 @cite_15
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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 widespread use of deception in online sources has motivated the need for methods to automatically profile and identify deceivers. This work explores deception, gender and age detection in short texts using a machine learning approach. First, we collect a new open domain deception dataset also containing demographic data such as gender and age. Second, we extract feature sets including n-grams, shallow and deep syntactic features, semantic features, and syntactic complexity and readability metrics. Third, we build classifiers that aim to predict deception, gender, and age. Our findings show that while deception detection can be performed in short texts even in the absence of a predetermined domain, gender and age prediction in deceptive texts is a challenging task. We further explore the linguistic differences in deceptive content that relate to deceivers gender and age and find evidence that both age and gender play an important role in people’s word choices when fabricating lies. This paper presents preliminary results of using authorship attribution methods for the detection of sockpuppeteering in Wikipedia. Sockpuppets are fake accounts created by malicious users to bypass Wikipedia’s regulations. Our dataset is composed of the comments made by the editors on the talk pages. To overcome the limitations of the short lengths of these comments, we use an voting scheme to combine predictions made on individual user entries. We show that this approach is promising and that it can be a viable alternative to the current human process that Wikipedia uses to resolve suspected sockpuppet cases. A growing field in computer applications is the use of algorithms to spot the lie. The most promising area within this field is the analysis of the language of the liar since speakers effectively control only the meaning they wish to convey, but not the linguistic style of the communication. With the advent of computational means to analyze language, we now have the ability to recognize differences in the way speakers phrase their lies as opposed to their truths. The main goal of this book is to cover the advances of the last 10 years in automatically discriminating truths from lies. To give the reader a grounding in deception studies, it describes a range of behaviors (physiological, gestural as well as verbal) that have been proposed as indicators of deception. An overview of the primary psychological and cognitive theories that have been offered as explanations of deceptive behaviors gives context for the description of specific behaviors. The book also addresses the differences between data collected in a laboratory and real-world data with respect to the emotional and cognitive state of the liar. It discusses sources of real-world data and problematic issues in its collection and identifies the primary areas in which applied studies based on real-world data are critical, including police, security, border crossing, customs, and asylum interviews; congressional hearings; financial reporting; legal depositions; human resource evaluation; predatory communications that include Internet scams, identity theft, fraud, and false product reviews. Having established the background, the book concentrates on computational analyses of deceptive verbal behavior, which have enabled the field of deception studies to move from individual cues to overall differences in behavior. The book concludes with a set of open questions that the computational work has generated.