Purpose: The purpose of this paper is to explore the spillover effects of offensive commenting in online community from the lens of emotional and behavioral contagion. Moreover, through simulations, we show how POISED is effective in the early detection of spam messages and how it is resilient against two well-known adversarial machine learning attacks. POISED significantly outperforms each of these systems. We also show that POISED's detection is more comprehensive than previous systems, by comparing it to three state-of-the-art spam detection systems that have been proposed by the research community in the past.

We test our system on a dataset of 1.3M tweets collected from 64K users, and we show that our approach is effective in detecting malicious messages, reaching 91% precision and 93% recall. In this paper, we follow this insight and present POISED, a system that leverages the differences in propagation between benign and malicious messages on social networks to identify spam and other unwanted content. Spam and other malicious content, on the other hand, follow different spreading patterns. Dissemination paths may emerge where some communities post similar messages, based on the interests of those communities. Moreover, content shared on these social network tend to propagate according to the interests of people. In this paper, we first show that users within a networked community share some topics of interest. Online social networks bring people who have personal connections or share common interests to form communities. However, advanced attackers can still successfully evade these defenses. Existing techniques for detecting spam include predicting the trustworthiness of accounts and analyzing the content of these messages. Overall, our work paves the way for providing video platforms like YouTube with proactive systems to detect and mitigate coordinated hate attacks.Ĭybercriminals have found in online social networks a propitious medium to spread spam and malicious content. Then, we use an ensemble of classifiers to determine the likelihood that a video will be raided with high accuracy (AUC up to 94%). First, we characterize and model YouTube videos along several axes (metadata, audio transcripts, thumbnails) based on a ground truth dataset of raid victims. In this paper, we propose an automated solution to identify videos that are likely to be targeted by coordinated harassers. Therefore, the de-facto solution is to reactively rely on user reports and human reviews. Unlike well-studied problems like spam and phishing, coordinated aggressive behavior both targets and is perpetrated by humans, making defense mechanisms that look for automated activity unsuitable. Despite the increasing relevance of this phenomenon, online services often lack effective countermeasures to mitigate it. In particular, recent work has showed how these attacks often take place as a result of 'raids,' i.e., organized efforts coordinated by ad-hoc mobs from third-party communities. Unfortunately, these communities are periodically plagued with aggression and hate attacks. Video sharing platforms like YouTube receive uploads from millions of users, covering a wide variety of topics and allowing others to comment and interact in response. At the same time, however, it has also enabled anti-social and toxic behavior to occur at an unprecedented scale. Over the years, the Web has shrunk the world, allowing individuals to share viewpoints with many more people than they are able to in real life.

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