Reverse Engineering Socialbot Infiltration Strategies in Twitter

Abstract

Data extracted from social networks like Twitter are increasingly being used to build applications and services that mine and summarize public reactions to events, such as traffic monitoring platforms, identification of epidemic outbreaks, and public perception about people and brands. However, such services are vulnerable to attacks from socialbots − automated accounts that mimic real users − seeking to tamper statistics by posting messages generated automatically and interacting with legitimate users. Potentially, if created in large scale, socialbots could be used to bias or even invalidate many existing services, by infiltrating the social networks and acquiring trust of other users with time. This study aims at understanding infiltration strategies of socialbots in the Twitter microblogging platform. To this end, we create 120 socialbot accounts with different characteristics and strategies (e.g., gender specified in the profile, how active they are, the method used to generate their tweets, and the group of users they interact with), and investigate the extent to which these bots are able to infiltrate the Twitter social network. Our results show that even socialbots employing simple automated mechanisms are able to successfully infiltrate the network. Additionally, using a 2 k factorial design, we quantify infiltration effectiveness of different bot strategies. Our analysis unveils findings that are key for the design of detection and counter measurements approaches.

The rise of social bots

“…2 To acquire visibility, they can infiltrate popular discussions, generating topically appropriate-and even potentially interesting-content, by identifying relevant keywords and searching online for information fitting that conversation. 17 After the tion. Proposed strategies to detect sybil accounts often rely on examining the structure of a social graph.…”

Arming the public with artificial intelligence to counter social bots

“…A more extreme example of these kinds of bots are identity thieves: they copy usernames, profile information, and pictures of other accounts and use them as their own, making only small changes (Cresci, Di Pietro, Petrocchi, Spognardi, & Tesconi, 2015). State-of-the-art machine learning technologies can be employed as part of the algorithms that automatically generate bot content (Freitas, Benevenuto, Ghosh, & Veloso, 2015). Sophisticated bots can emulate temporal patterns of content posting and consumption by humans.…”

Online Misinformation

“…This works also highlights the relevance of bots initiating the process of misinformation spread. Boshmaf [8] and Freitas [25] reported that simple automated mechanisms that produce contents and boost followers yield successful infiltration strategies of misinformation. However, nobody knows exactly how many social bots populate social media, or what share of content, and particularly misinformation, can be attributed to bots [21].…”

“…While Twitter mentions that a survey will be send to a small group of people to gain feedback, little is known so far about the effects of this initiative. 25 Regarding the methods focused on the early detection of malicious accounts we can highlight works that aim to identify spammers [69], bots [21], crowdturfing [68] [40] and malicious accounts in general [19] [41]. These techniques generally focus on the analysis of various user, temporal, geographical and linguistic features in order to successfully identify these accounts.…”