If it looks like a spammer and behaves like a spammer, it must be a spammer: analysis and detection of microblogging spam accounts

Abstract

Spam in Online Social Networks (OSNs) is a systemic problem that imposes a threat to these services in terms of undermining their value to advertisers and potential investors, as well as negatively affecting users' engagement. As spammers continuously keep creating newer accounts and evasive techniques upon being caught, a deeper understanding of their spamming strategies is vital to the design of future social media defense mechanisms. In this work, we present a unique analysis of spam accounts in OSNs viewed through the lens of their behavioral characteristics. Our analysis includes over 100 million messages collected from Twitter over the course of one month. We show that there exist two behaviorally distinct categories of spammers and that they employ different spamming strategies. Then, we illustrate how users in these two categories demonstrate different individual properties as well as social interaction patterns. Finally, we analyze the detectability of spam accounts with respect to three categories of features, namely, content attributes, social interactions, and profile properties.

We Built a Fake News & Click-bait Filter: What Happened Next Will Blow Your Mind!

“…Identification of malicious accounts in social networks is another related research direction. This includes detecting spam accounts (Almaatouq et al, 2016;Mccord and Chuah, 2011), fake accounts (Fire et al, 2014;Cresci et al, 2015), compromised accounts and phishing accounts (Adewole et al, 2017). Fake profile detection has also been studied in the context of cyber-bullying (Galán-García et al, 2014).…”

GroupFound: An effective approach to detect suspicious accounts in online social networks

“…It needs to identify more spammers as many as possible while reducing the misjudgment of normal accounts. Based on the considerations above, we can use formula (15) to calculate the accuracy of our algorithm…”

“…When the optimal threshold value a = 3:01, the TPR is 81:08% and the FPR is 8:54%. According to formula (15), the accuracy of GroupFound is 86:27%.…”

Analysis of Web Spam for Non-English Content: Toward More Effective Language-Based Classifiers

“…Recent surveys of existing spam detection techniques and mechanisms have analyzed their advantages and disadvantages (e.g., [42] and [43]). It should be noted that spam and automated accounts in social networks have also contributed to the prevalence of web spam (e.g., see [44–48]). The detection features used for web spam in previous studies belong to two categories: (1) those that exploit topology and network-related data; and (2) those that exploit the web page content.…”