Online Social Spammer Detection

The explosive use of social media also makes it a popular platform for malicious users, known as social spammers, to overwhelm normal users with unwanted content. One effective way for social spammer detection is to build a classifier based on content and social network information. However, social spammers are sophisticated and adaptable to game the system with fast evolving content and network patterns. First, social spammers continually change their spamming content patterns to avoid being detected. Second, reflexive reciprocity makes it easier for social spammers to establish social influence and pretend to be normal users by quickly accumulating a large number of “human” friends. It is challenging for existing anti-spamming systems based on batch-mode learning to quickly respond to newly emerging patterns for effective social spammer detection. In this paper, we present a general optimization framework to collectively use content and network information for social spammer detection, and provide the solution for efficient online processing. Experimental results on Twitter datasets confirm the effectiveness and efficiency of the proposed framework.  Social media services, like Facebook and Twitter, are increasingly used in various scenarios such as marketing, journalism and public relations. While social media services have emerged as important platforms for information dissemination and communication, it has also become infamous for spammers who overwhelm other users with unwanted content. The (fake) accounts, known as social spammers (Webb et al. 2008; Lee et al. 2010), are a special type of spammers who coordinate among themselves to launch various attacks such as spreading ads to generate sales, disseminating pornography, viruses, phishing, befriending victims and then surreptitiously grabbing their personal information (Bilge et al. 2009), or simply sabotaging a system’s reputation (Lee et al. 2010). The problem of social spamming is a serious issue prevalent in social media sites. Characterizing and detecting social spammers can significantly improve the quality of user experience, and promote the healthy use and development of a social networking system. Following spammer detection in traditional platforms like Email and the Web (Chen et al. 2012), some efforts have Copyright c 2014, Association for the Advancement of Artificial Intelligence (www.aaai.org). All rights reserved. been devoted to detect spammers in various social networking sites, including Twitter (Lee et al. 2010), Renren (Yang et al. 2011), Blogosphere (Lin et al. 2007), etc. Existing methods can generally be divided into two categories. First category is to employ content analysis for detecting spammers in social media. Profile-based features (Lee et al. 2010) such as content and posting patterns are extracted to build an effective supervised learning model, and the model is applied on unseen data to filter social spammers. Another category of methods is to detect spammers via social network analysis (Ghosh et al. 2012). A widely used assumption in the methods is that spammers cannot establish an arbitrarily large number of social trust relations with legitimate users. The users with relatively low social influence or social status in the network will be determined as spammers. Traditional spammer detection methods become less effective due to the fast evolution of social spammers. First, social spammers show dynamic content patterns in social media. Spammers’ content information changes too fast to be detected by a static anti-spamming system based on offline modeling (Zhu et al. 2012). Spammers continue to change their spamming strategies and pretend to be normal users to fool the system. A built system may become less effective when the spammers create many new, evasive accounts. Second, many social media sites like Twitter have become a target of link farming (Ghosh et al. 2012). The reflexive reciprocity (Weng et al. 2010; Hu et al. 2013b) indicates that many users simply follow back when they are followed by someone for the sake of courtesy. It is easier for spammers to acquire a large number of follower links in social media. Thus, with the perceived social influence, they can avoid being detected by network-based methods. Similar results targeting other platforms such as Renren (Yang et al. 2011) have been reported in literature as well. Existing systems rely on building a new model to capture newly emerging content-based and network-based patterns of social spammers. Given the rapidly evolving nature, it is necessary to have a framework that efficiently reflects the effect of newly emerging data. One efficient approach to incrementally update existing model in large-scale data analysis is online learning. While online learning has been studied for years and shown its effectiveness in many applications such as image and video processing (Mairal et al. 2009) and human computer inProceedings of the Twenty-Eighth AAAI Conference on Artificial Intelligence