Weaponized Crowdsourcing: An Emerging Threat and Potential Countermeasures

Abstract

The crowdsourcing movement has spawned a host of successful efforts that organize large numbers of globally-distributed participants to tackle a range of tasks, including crisis mapping (e.g., Ushahidi), translation (e.g., Duolingo), and protein folding (e.g., Foldit). Alongside these specialized systems, we have seen the rise of general-purpose crowdsourcing marketplaces like Amazon Mechanical Turk and Crowdflower that aim to connect task requesters with task workers, toward creating new crowdsourcing systems that can intelligently organize large numbers of people. However, these positive opportunities have a sinister counterpart: what we dub “Weaponized Crowdsourcing”. Already we have seen the first glimmers of this ominous new trend—including large-scale “crowdturfing”, wherein masses of cheaply paid shills can be organized to spread malicious URLs in social media (Grier, Thomas, Paxson, & Zhang, 2010; Lee & Kim, 2012), form artificial grassroots campaigns (“astroturf”) (Gao et al., 2010; Lee, Caverlee, Cheng, & Sui, 2013), spread rumor and misinformation (Castillo, Mendoza, & Poblete, 2011; Gupta, Lamba, Kumaraguru, & Joshi, 2013), and manipulate search engines. A recent study finds that 90 % of tasks on many crowdsourcing platforms are for crowdturfing (Wang et al., 2012), and our initial research (Lee, Tamilarasan, & Caverlee, 2013) shows that most malicious tasks in crowdsourcing systems target either online communities (56 %) or search engines (33 %). Unfortunately, little is known about Weaponized Crowdsourcing as it manifests in existing systems, nor what are the ramifications on the design and operation of emerging socio-technical systems. Hence, this chapter shall focus on key research questions related to Weaponized Crowdsourcing as well as outline the potential of building new preventative frameworks for maintaining the information quality and integrity of online communities in the face of this rising challenge.