We present a game-theoretic model for the spread of deviant behavior in online social networks. We utilize a two-strategy framework wherein each player's behavior is classified as normal or deviant and evolves according to the cooperate-defect payoff scheme of the classic prisoner's dilemma game. We demonstrate convergence of individual behavior over time to a final strategy vector and indicate counterexamples to this convergence outside the context of prisoner's dilemma. Theoretical results are validated on a real-world dataset collected from a popular online forum.
Several recent works focus on detecting social spammers @cite_12 @cite_20 @cite_19 @cite_1 @cite_29 @cite_4 . Social spammers, according to this body of work, are users controlled either by humans or bots, who use social networking sites and in particular their social connections to promote products, advertise events, or simply post useless and or inappropriate comments. @cite_20 studied social spammers in online social networks. To do so, they deployed social honeypots for harvesting deceptive spam profiles from social networking communities, and created spam classifiers using machine learning methods (e.g., SVM) based on a variety of features. Similarly, Kantchelian @cite_19 developed an approach for detecting comment spam by leveraging the level of a comment; he showed that spammers' comments have low information levels. @cite_1 also proposed a comprehensive framework for social spam detection, based on social network content, attaining a high accuracy of detection. Our focus is not on social spammers only but rather on activities related to vandalism and misbehavior, which may or may not be generated by social spammers.
@cite_4 @cite_29 @cite_1 @cite_19 @cite_12 @cite_20
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Online social networks, such as Twitter, have soared in popularity and in turn have become attractive targets of spam. In fact, spammers have evolved their strategies to stay ahead of Twitter's anti-spam measures in this short period of time. In this paper, we investigate the strategies Twitter spammers employ to reach relevant target audiences. Due to their targeted approaches to send spam, we see evidence of a large number of the spam accounts forming relationships with other Twitter users, thereby becoming deeply embedded in the social network. We analyze nearly 20 million tweets from about 7 million Twitter accounts over a period of five days. We identify a set of 14,230 spam accounts that manage to live longer than the other 73 of other spam accounts in our data set. We characterize their behavior, types of tweets they use, and how they target their audience. We find that though spam campaigns changed little from a recent work by , spammer strategies evolved much in the same short time span, causing us to sometimes find contradictory spammer behavior from what was noted in 's work. Specifically, we identify four major strategies used by 2 3rd of the spammers in our data. The most popular of these was one where spammers targeted their own followers. The availability of various kinds of services that help garner followers only increases the popularity of this strategy. The evolution in spammer strategies we observed in our work suggests that studies like ours should be undertaken frequently to keep up with spammer evolution. The availability of microblogging, like Twitter and Sina Weibo, makes it a popular platform for spammers to unfairly overpower normal users with unwanted content via social networks, known as social spamming. The rise of social spamming can significantly hinder the use of microblogging systems for effective information dissemination and sharing. Distinct features of microblogging systems present new challenges for social spammer detection. First, unlike traditional social networks, microblogging allows to establish some connections between two parties without mutual consent, which makes it easier for spammers to imitate normal users by quickly accumulating a large number of "human" friends. Second, microblogging messages are short, noisy, and unstructured. Traditional social spammer detection methods are not directly applicable to microblogging. In this paper, we investigate how to collectively use network and content information to perform effective social spammer detection in microblogging. In particular, we present an optimization formulation that models the social network and content information in a unified framework. Experiments on a real-world Twitter dataset demonstrate that our proposed method can effectively utilize both kinds of information for social spammer detection. In this work, we design a method for blog comment spam detection using the assumption that spam is any kind of uninformative content. To measure the "informativeness" of a set of blog comments, we construct a language and tokenization independent metric which we call content complexity, providing a normalized answer to the informal question "how much information does this text contain?" We leverage this metric to create a small set of features well-adjusted to comment spam detection by computing the content complexity over groupings of messages sharing the same author, the same sender IP, the same included links, etc. We evaluate our method against an exact set of tens of millions of comments collected over a four months period and containing a variety of websites, including blogs and news sites. The data was provided to us with an initial spam labeling from an industry competitive source. Nevertheless the initial spam labeling had unknown performance characteristics. To train a logistic regression on this dataset using our features, we derive a simple mislabeling tolerant logistic regression algorithm based on expectation-maximization, which we show generally outperforms the plain version in precision-recall space. By using a parsimonious hand-labeling strategy, we show that our method can operate at an arbitrary high precision level, and that it significantly dominates, both in terms of precision and recall, the original labeling, despite being trained on it alone. The content complexity metric, the use of a noise-tolerant logistic regression and the evaluation methodology are thus the three central contributions with this work. In this study, we examine the abuse of online social networks at the hands of spammers through the lens of the tools, techniques, and support infrastructure they rely upon. To perform our analysis, we identify over 1.1 million accounts suspended by Twitter for disruptive activities over the course of seven months. In the process, we collect a dataset of 1.8 billion tweets, 80 million of which belong to spam accounts. We use our dataset to characterize the behavior and lifetime of spam accounts, the campaigns they execute, and the wide-spread abuse of legitimate web services such as URL shorteners and free web hosting. We also identify an emerging marketplace of illegitimate programs operated by spammers that include Twitter account sellers, ad-based URL shorteners, and spam affiliate programs that help enable underground market diversification. Our results show that 77 of spam accounts identified by Twitter are suspended within on day of their first tweet. Because of these pressures, less than 9 of accounts form social relationships with regular Twitter users. Instead, 17 of accounts rely on hijacking trends, while 52 of accounts use unsolicited mentions to reach an audience. In spite of daily account attrition, we show how five spam campaigns controlling 145 thousand accounts combined are able to persist for months at a time, with each campaign enacting a unique spamming strategy. Surprisingly, three of these campaigns send spam directing visitors to reputable store fronts, blurring the line regarding what constitutes spam on social networks. Web-based social systems enable new community-based opportunities for participants to engage, share, and interact. This community value and related services like search and advertising are threatened by spammers, content polluters, and malware disseminators. In an effort to preserve community value and ensure longterm success, we propose and evaluate a honeypot-based approach for uncovering social spammers in online social systems. Two of the key components of the proposed approach are: (1) The deployment of social honeypots for harvesting deceptive spam profiles from social networking communities; and (2) Statistical analysis of the properties of these spam profiles for creating spam classifiers to actively filter out existing and new spammers. We describe the conceptual framework and design considerations of the proposed approach, and we present concrete observations from the deployment of social honeypots in MySpace and Twitter. We find that the deployed social honeypots identify social spammers with low false positive rates and that the harvested spam data contains signals that are strongly correlated with observable profile features (e.g., content, friend information, posting patterns, etc.). Based on these profile features, we develop machine learning based classifiers for identifying previously unknown spammers with high precision and a low rate of false positives.