Bots Fired: Examining Social Bot Evidence in Online Mass Shooting Conversations

Building upon our work with respect to how bots impact online conversations pertaining to global events and  health,  we have extended this research to see what role bots play in mass shooting events. In our new paper published in Palgrave Communications entitled "Bots Fired: Examining Social Bot Evidence in Online Mass Shooting Conversations" we examine four mass shooting events (i.e., Las Vegas, Sutherland Springs, Parkland, and Santa Fe) and find that social bots participate and contribute to online mass shooting conversations in a manner that is distinguishable from human contributions. Below we provide the abstract, along with some figures from the paper that highlight our methodology and main results. Finally at the bottom of the post, we provide the full reference to the paper. 

Abstract:

Mass shootings, like other extreme events, have long garnered public curiosity and, in turn, significant media coverage. The media framing, or topic focus, of mass shooting events typically evolves over time from details of the actual shooting to discussions of potential policy changes (e.g., gun control, mental health). Such media coverage has been historically provided through traditional media sources such as print, television, and radio, but the advent of online social networks (OSNs) has introduced a new platform for accessing, producing, and distributing information about such extreme events. The ease and convenience of OSN usage for information within society’s larger growing reliance upon digital technologies introduces potential unforeseen risks. Social bots, or automated software agents, are one such risk, as they can serve to amplify or distort potential narratives associated with extreme events such as mass shootings. In this paper, we seek to determine the prevalence and relative importance of social bots participating in OSN conversations following mass shooting events using an ensemble of quantitative techniques. Specifically, we examine a corpus of more than 46 million tweets produced by 11.7 million unique Twitter accounts within OSN conversations discussing four major mass shooting events: the 2017 Las Vegas concert shooting, the 2017 Sutherland Springs church shooting, the 2018 Parkland school shooting and the 2018 Santa Fe school shooting. This study’s results show that social bots participate in and contribute to online mass shooting conversations in a manner that is distinguishable from human contributions. Furthermore, while social bots accounted for fewer than 1% of total corpus user contributors, social network analysis centrality measures identified many bots with significant prominence in the conversation networks, densely occupying many of the highest eigenvector and out-degree centrality measure rankings, to include 82% of the top-100 eigenvector values of the Las Vegas retweet network.

Keywords: Social bots, mass shootings, school shootings, online social networks, computational social science.

 Overview of social bot analysis framework illustrating methodological steps taken to analyze social bots within online social network conversations involving mass shooting events

Overall tweet corpus volumes and suspected social bot contributions for each associated OSN mass shooting 215 event conversation.

Intra-group and cross-group retweet interaction rates among and between human (blue) and suspected social bot (red) user accounts for a one-month period following the (a) Las Vegas, (b) Sutherland Springs, (c) Parkland and (d) Santa Fe shooting events.

Social bot accounts in the top-N, where N = 1000/100/10, (a) eigenvector, (b) in-degree, (c) out-degree and (d) PageRank centrality measurement rankings within OSN mass shooting retweet networks discussing the Las Vegas (red), Sutherland Springs (green), Parkland (blue) and Santa Fe (purple) shooting events.

Full Reference:

Schuchard, R., Crooks, A.T., Croitoru, A. and Stefanidis, A. (2019) Bots Fired: Examining Social Bot Evidence in Online Mass Shooting Conversations, Palgrave Communications, 5: 158. Available at https://doi.org/10.1057/s41599-019-0359-x. (pdf)

Place-Based Simulation Modelling

Nick Malleson, Alison Heppenstall and myself recently had a chapter published in the Oxford Research Encyclopedia of Criminology and Criminal Justice, entitled "Place-Based Simulation Modelling:  Agent-Based Modelling and Virtual Environments". In the chapter, we discuss how agent-based modeling (ABM) can be used for modeling spatial patterns of crime.

Specifically we discuss the motivation for ABM in criminology. We then introduce the core components of ABM and how one can structure such models and represent human behavior in them (e.g. using Beliefs-Desires-Intentions (BDI) or Physical conditions, Emotional states, Cognitive capabilities and Social status" (PECS) frameworks). Next, we discuss space can be represented in agent-based models, both from an abstract sense but also accurate spatial environments (i.e. by using GIS data). The chapter then moves onto briefly discussing tools for the implementation of agent-based models (e.g. MASON, NetLogo, Repast) before we provide a critique of ABM for modeling spatial crime, including its appeal (e.g. the emergence of crime from the bottom up), the difficulties of using ABM (e.g adequately defining behavior, access to data etc.) and finally the ethical implications of using agent-based models for studying crime. Below you can read the official summary of the chapter along with its full citation.

Chapter Summary: 

Since the earliest geographical explorations of criminal phenomena, scientists have come to the realization that crime occurrences can often be best explained by analysis at local scales. For example, the works of Guerry and Quetelet—which are often credited as being the first spatial studies of crime—analyzed data that had been aggregated to regions approximately similar to US states. The next major seminal work on spatial crime patterns was from the Chicago School in the 20th century and increased the spatial resolution of analysis to the census tract (an American administrative area that is designed to contain approximately 4,000 individual inhabitants). With the availability of higher-quality spatial data, as well as improvements in the computing infrastructure (particularly with respect to spatial analysis and mapping), more recent empirical spatial criminology work can operate at even higher resolutions; the “crime at places” literature regularly highlights the importance of analyzing crime at the street segment or at even finer scales. These empirical realizations—that crime patterns vary substantially at micro places—are well grounded in the core environmental criminology theories of routine activity theory, the geometric theory of crime, and the rational choice perspective. Each theory focuses on the individual-level nature of crime, the behavior and motivations of individual people, and the importance of the immediate surroundings. For example, routine activities theory stipulates that a crime is possible when an offender and a potential victim meet at the same time and place in the absence of a capable guardian. The geometric theory of crime suggests that individuals build up an awareness of their surroundings as they undertake their routine activities, and it is where these areas overlap with crime opportunities that crimes are most likely to occur. Finally, the rational choice perspective suggests that the decision to commit a crime is partially a cost-benefit analysis of the risks and rewards. To properly understand or model these three decisions it is important to capture the motivations, awareness, rationality, immediate surroundings, etc., of the individual and include a highly disaggregate representation of space (i.e. “micro-places”). Unfortunately one of the most common methods for modeling crime, regression, is somewhat poorly suited capturing these dynamics. As with most traditional modeling approaches, regression models represent the underlying system through mathematical aggregations. The resulting models are therefore well suited to systems that behave in a linear fashion (e.g., where a change in model input leads to a predictable change in the model output) and where low-level heterogeneity is not important (i.e., we can assume that everyone in a particular group of people will behave in the same way). However, as alluded to earlier, the crime system does not necessarily meet these assumptions. To really understand the dynamics of crime patterns, and to be able to properly represent the underlying theories, it is necessary to represent the behavior of the individual system components (i.e. people) directly. For this reason, many scientists from a variety of different disciplines are turning to individual-level modeling techniques such as agent-based modeling.

Keywords: agent-based modelling, crime simulation, travel to crime, virtual environment, NetLogo, virtual laboratory.

Figure 1: The process of initializing, running, and analyzing an agent-based model.

Full Reference:

Malleson, N., Heppenstall, A. and Crooks, A.T. (2018). Place-Based Simulation Modelling: Agent-Based Modelling and Virtual Environments, Oxford Research Encyclopedia of Criminology and Criminal Justice, Oxford University Press. DOI: 10.1093/acrefore/9780190264079.013.319 (pdf)

Crime and Slums

This project focuses on the emergence of criminal activity due to the unmet human needs of those living in Rio's favelas. An agent-based model is developed to explore how human needs, environmental factors, and individual attributes impact state-level behaviors. The emergence of organized crime is observed as "common" criminals turn into gang members. The prevention of conflict requires policies that anticipate responses and avoid conflict. By "re-creating" the current environment, we have the ability to potentially predict the onset of violence where it does not yet exist or understand the source of conflict in those areas already in the midst of violence.

Selected outputs from this research: 

Pint, B., Crooks, A. T., and Geller, A. (2010), An Agent-based Model of Organized Crime: Favelas and the Drug Trade. 2nd Brazilian Workshop on Social Simulation, Sao Bernardo do Campo, Brazil. (pdf)