The popularity of social media platforms such as Twitter has led to the proliferation of automated bots, creating both opportunities and challenges in information dissemination, user engagements, and quality of services. Past works on profiling bots had been focused largely on malicious bots, with the assumption that these bots should be removed. In this work, however, we find many bots that are benign, and propose a new, broader categorization of bots based on their behaviors. This includes broadcast, consumption, and spam bots. To facilitate comprehensive analyses of bots and how they compare to human accounts, we develop a systematic profiling framework that includes a rich set of features and classifier bank. We conduct extensive experiments to evaluate the performances of different classifiers under varying time windows, identify the key features of bots, and infer about bots in a larger Twitter population. Our analysis encompasses more than 159K bot and human (non-bot) accounts in Twitter. The results provide interesting insights on the behavioral traits of both benign and malicious bots.
From politicians and nation states to terrorist groups, numerous organizations reportedly conduct explicit campaigns to influence opinions on social media, posing a risk to freedom of expression. Thus, there is a need to identify and eliminate "influence bots"--realistic, automated identities that illicitly shape discussions on sites like Twitter and Facebook--before they get too influential. The Social Mediator forum was created to bridge the gaps between the theory and practice of social media research and development. The articles are intended to promote greater awareness of new insights and experiences in the rapidly evolving domain of social media, some of which may influence perspectives and approaches in the more established areas of human-computer interaction. Each article in the forum is made up of several short contributions from people representing different perspectives on a particular topic. Previous installments of this forum have woven together diverse perspectives on the ways that social media is transforming relationships among different stakeholders in the realms of healthcare and government. The current article highlights some of the ways social robots (socialbots)---programs that operate autonomously on social networking sites---are transforming relationships within those sites, and how these transformations may more broadly influence relationships among people and organizations in the future. A recent article in Communications of the ACM called "The Social Life of Robots" reported that "researchers have started to explore the possibilities of 'social' machines capable of working together with minimal human supervision" [1]. That article illuminates recent developments involving interactions between humans and robots in the physical world; this article focuses on the interactions between humans and robots in the virtual world. Our authors are exploring and expanding the frontiers of designing, deploying, and analyzing the behavior and impact of robots operating in online social networks, and they have invited a number of other frontierspeople to share some of their insights, experiences, and future expectations for social robotics. Online Social Networks (OSNs) such as Twitter and Facebook have become a significant testing ground for Artificial Intelligence developers who build programs, known as socialbots, that imitate actual users by automating their social-network activities such as forming social links and posting content. Particularly, Twitter users have shown difficulties in distinguishing these socialbots from the human users in their social graphs. Frequently, legitimate users engage in conversations with socialbots. More impressively, socialbots are effective in acquiring human users as followers and exercising influence within them. While the success of socialbots is certainly a remarkable achievement for AI practitioners, their proliferation in the Twitter-sphere opens many possibilities for cybercrime. The proliferation of socialbots in the Twitter-sphere motivates us to assess the characteristics or strategies that make socialbots most likely to succeed. In this direction, we created 120 socialbot accounts in Twitter, which have a profile, follow other users, and generate tweets either by reposting messages that others have posted or by creating their own synthetic tweets. Then, we employ a 2k factorial design experiment in order to quantify the infiltration effectiveness of different socialbot strategies. Our analysis is the first of a kind, and reveals what strategies make socialbots successful in the Twitter-sphere. Online Social Networks (OSNs) have attracted millions of active users and have become an integral part of today's web ecosystem. Unfortunately, in the wrong hands, OSNs can be used to harvest private user data, distribute malware, control botnets, perform surveillance, spread misinformation, and even influence algorithmic trading. Usually, an adversary starts off by running an infiltration campaign using hijacked or adversary-owned OSN accounts, with an objective to connect with a large number of users in the targeted OSN. In this article, we evaluate how vulnerable OSNs are to a large-scale infiltration campaign run by socialbots: bots that control OSN accounts and mimic the actions of real users. We adopted the design of a traditional web-based botnet and built a prototype of a Socialbot Network (SbN): a group of coordinated programmable socialbots. We operated our prototype on Facebook for 8weeks, and collected data about user behavior in response to a large-scale infiltration campaign. Our results show that (1) by exploiting known social behaviors of users, OSNs such as Facebook can be infiltrated with a success rate of up to 80 , (2) subject to user profile privacy settings, a successful infiltration can result in privacy breaches where even more private user data are exposed, (3) given the economics of today's underground markets, running a large-scale infiltration campaign might be profitable but is still not particularly attractive as a sustainable and independent business, (4) the security of socially-aware systems that use or integrate OSN platforms can be at risk, given the infiltration capability of an adversary in OSNs, and (5) defending against malicious socialbots raises a set of challenges that relate to web automation, online-offline identity binding, and usable security. Social bots are automatic or semi-automatic computer programs that mimic humans and or human behavior in online social networks. Social bots can attack users (targets) in online social networks to pursue a variety of latent goals, such as to spread information or to influence targets. Without a deep understanding of the nature of such attacks or the susceptibility of users, the potential of social media as an instrument for facilitating discourse or democratic processes is in jeopardy. In this paper, we study data from the Social Bot Challenge 2011 - an experiment conducted by the WebEcologyProject during 2011 - in which three teams implemented a number of social bots that aimed to influence user behavior on Twitter. Using this data, we aim to develop models to (i) identify susceptible users among a set of targets and (ii) predict users’ level of susceptibility. We explore the predictiveness of three different groups of features (network, behavioral and linguistic features) for these tasks. Our results suggest that susceptible users tend to use Twitter for a conversational purpose and tend to be more open and social since they communicate with many different users, use more social words and show more affection than non-susceptible users.