Examining the Demand for Spam

Abstract

Despite significant advances in automated spam detection, some spam content manages to evade detection and engage users. While the spam supply chain is well understood through previous research, there is little understanding of spam consumers. We focus on the demand side of the spam equation examining what drives users to click on spam via a largescale analysis of de-identified, aggregated Facebook log data (n=600,000). We find (1) that the volume of spam and clicking norms in a users' network are significantly related to individual consumption behavior; (2) that more active users are less likely to click, suggesting that experience and internet skill (weakly correlated with activity level) may create more savvy consumers; and (3) we confirm previous findings about the gender effect in spam consumption, but find this effect largely corresponds to spam topics. Our findings provide practical insights to reduce demand for spam content, thereby affecting spam profitability.

Deceptive Previews: A Study of the Link Preview Trustworthiness in Social Platforms

“…Similarly to Redmiles et al [27], our work intends to shed some light on the dynamics behind user clicks on social networks. However, as opposed to Redmiles et al [27], our work does not study social connections between users or user properties such as demographics. Instead, our work focuses on the content of a link preview, the trustworthiness of the link preview creation, and it explores the extent to which an attacker can control the fields displayed to the victims.…”

“…Victim -The victim of these attacks can be a specific individual or small group of individuals (i.e., targeted attack), or as many users as possible, indiscriminately. For the analysis of this paper, we consider skilled and experienced social network users-a category of users who is less prone to click on malicious URLs [27], [8], [17], [38].…”

“…When deciding whether to click on link previews, users rely on an ensemble of signals that are displayed by the social platform's web pages. For example, Redmiles et al [27] show that users take into account who shares the web content and the online community the content originates from. Similarly to Redmiles et al [27], our work intends to shed some light on the dynamics behind user clicks on social networks.…”

“…For example, Redmiles et al [27] show that users take into account who shares the web content and the online community the content originates from. Similarly to Redmiles et al [27], our work intends to shed some light on the dynamics behind user clicks on social networks. However, as opposed to Redmiles et al [27], our work does not study social connections between users or user properties such as demographics.…”

“…Existing work has studied different aspects such as the pervasiveness of spam campaigns in social networks (e.g., [32], [15]), the infrastructure used by attackers to distribute malicious pages [29], and the accounts spreading malicious content (e.g., [35], [9]). Other lines of works looked at the demographics of the victims (e.g., [27]), showing that individual and communities behavior influence the likelihood to click. This paper looks at the problem of malicious link distribution by investigating one of the elements used by attackers to draw the attention of the victims, i.e., link previews.…”