Clayton A. Davis

The rise of social bots

Todays social bots are sophisticated and sometimes menacing. Indeed, their presence can endanger online ecosystems as well as our society.

The Geospatial Characteristics of a Social Movement Communication Network

Social movements rely in large measure on networked communication technologies to organize and disseminate information relating to the movements’ objectives. In this work we seek to understand how the goals and needs of a protest movement are reflected in the geographic patterns of its communication network, and how these patterns differ from those of stable political communication. To this end, we examine an online communication network reconstructed from over 600,000 tweets from a thirty-six week period covering the birth and maturation of the American anticapitalist movement, Occupy Wall Street. We find that, compared to a network of stable domestic political communication, the Occupy Wall Street network exhibits higher levels of locality and a hub and spoke structure, in which the majority of non-local attention is allocated to high-profile locations such as New York, California, and Washington D.C. Moreover, we observe that information flows across state boundaries are more likely to contain framing language and references to the media, while communication among individuals in the same state is more likely to reference protest action and specific places and times. Tying these results to social movement theory, we propose that these features reflect the movement’s efforts to mobilize resources at the local level and to develop narrative frames that reinforce collective purpose at the national level.

BotOrNot: A System to Evaluate Social Bots

While most online social media accounts are controlled by humans, these platforms also host automated agents called social bots or sybil accounts. Recent literature reported on cases of social bots imitating humans to manipulate discussions, alter the popularity of users, pollute content and spread misinformation, and even perform terrorist propaganda and recruitment actions. Here we present BotOrNot, a publicly-available service that leverages more than one thousand features to evaluate the extent to which a Twitter account exhibits similarity to the known characteristics of social bots. Since its release in May 2014, BotOrNot has served over one million requests via our website and APIs.

The social media response to Black Lives Matter: how Twitter users interact with Black Lives Matter through hashtag use

ABSTRACT This paper focuses on the social media presence of Black Lives Matter (BLM). Specifically, we examine how social media users interact with BLM by using hashtags and thus modify the framing of the movement. We call this decentralized interaction with the movement “distributed framing”. Empirically, we illustrate this idea with an analysis of 66,159 tweets that mention #BlackLivesMatter in 2014, when #BlackLivesMatter becomes prominent on social media. We also tally the other hashtags that appear with #BlackLivesMatter in order to measure how online communities influence the framing of the movement. We find that #BlackLivesMatter is associated with five types of hashtags. These hashtags mention solidarity or approval of the movement, refer to police violence, mention movement tactics, mention Ferguson, or express counter-movement sentiments. The paper concludes with hypotheses about the development of movement framings that can be addressed in future research.

On the Influence of Social Bots in Online Protests

Social bots can affect online communication among humans. We study this phenomenon by focusing on #YaMeCanse, the most active protest hashtag in the history of Twitter in Mexico. Accounts using the hashtag are classified using the BotOrNot bot detection tool. Our preliminary analysis suggests that bots played a critical role in disrupting online communication about the protest movement.

Supporting a Social Media Observatory with Customizable Index Structures: Architecture and Performance

The intensive research activity in analysis of social media and micro-blogging data in recent years suggests the necessity and great potential of platforms that can efficiently store, query, analyze, and visualize social media data. To support these “social media observatories” effectively, a storage platform must satisfy special requirements for loading and storage of multi-terabyte datasets, as well as efficient evaluation of queries involving analysis of the text of millions of social updates. Traditional inverted indexing techniques do not meet such requirements. As a solution, we propose a general indexing framework, IndexedHBase, to build specially customized index structures for facilitating efficient queries on an HBase distributed data storage system. IndexedHBase is used to support a social media observatory that collects and analyzes data obtained through the Twitter streaming API. We develop a parallel query evaluation strategy that can explore the customized index structures efficiently, and test it on a set of typical social media data queries. We evaluate the performance of IndexedHBase on FutureGrid and compare it with Riak, a widely adopted commercial NoSQL database system. The results show that IndexedHBase provides a data loading speed that is six times faster than Riak and is significantly more efficient in evaluating queries involving large result sets.

A Role for Network Science in Social Norms Intervention

Abstract Social norms theory has provided a foundation for public health interventions on critical issues such as alcohol and substance use, sexual violence, and risky sexual behavior. We assert that modern social norms interventions can be better informed with the use of network science methods. Social norms can be seen as complex contagions on a social network, and their propagation as an information diffusion process. We observe instances where the recommendations of social norms theory match up to theoretical predictions from information diffusion models. Conversely, the network science viewpoint highlights aspects of intervention design not addressed by the existing theory. Information about network structure and dynamics are often not used in existing social norms interventions; we argue that these factors may be contributing to the lack of efficacy of social norms interventions delivered via online social networks. Further, delivery via online social networks may enable novel intervention designs employing realtime feedback.