Title: More or Less? Predict the Social Influence of Malicious URLs on Social Media,
Abstract: Users of Online Social Networks (OSNs) interact with each other more than
ever. In the context of a public discussion group, people receive, read, and
write comments in response to articles and postings. In the absence of access
control mechanisms, OSNs are a great environment for attackers to influence
others, from spreading phishing URLs, to posting fake news. Moreover, OSN user
behavior can be predicted by social science concepts which include conformity
and the bandwagon effect. In this paper, we show how social recommendation
systems affect the occurrence of malicious URLs on Facebook. We exploit
temporal features to build a prediction framework, having greater than 75%
accuracy, to predict whether the following group users' behavior will increase
or not. Included in this work, we demarcate classes of URLs, including those
malicious URLs classified as creating critical damage, as well as those of a
lesser nature which only inflict light damage such as aggressive commercial
advertisements and spam content. It is our hope that the data and analyses in
this paper provide a better understanding of OSN user reactions to different
categories of malicious URLs, thereby providing a way to mitigate the influence
of these malicious URL attacks.