Information Assurance: Detection of Web Spam Attacks in Social Media
نویسندگان
چکیده
As online social media applications continue to gain its popularity, concerns about the rapid proliferation of Web spam has grown in recent years. These applications allow spammers to submit links anonymously, diverting unsuspected users to spam Web sites. This paper presents a novel co-classification framework to simultaneously detect Web spam and spammers in social media Web sites based on their content and link-based features. Using data from two real-world applications, we empirically showed that the proposed co-classification framework is more effective that learning to classify the Web spam and spammers independently. We also investigate the efficacy of combining data from multiple social media applications to improve Web spam detection.
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