skip to main content
US FlagAn official website of the United States government
dot gov icon
Official websites use .gov
A .gov website belongs to an official government organization in the United States.
https lock icon
Secure .gov websites use HTTPS
A lock ( lock ) or https:// means you've safely connected to the .gov website. Share sensitive information only on official, secure websites.


Title: Fame through surprise: How fame-seeking mass shooters diversify their attacks
Mass shootings are becoming more frequent in the United States, as we routinely learn from the media about attempts that have been prevented or tragedies that destroyed entire communities. To date, there has been limited understanding of the modus operandi of mass shooters, especially those who seek fame through their attacks. Here, we explore whether the attacks of these fame-seeking mass shooters were more surprising than those of others and clarify the link between fame and surprise in mass shootings. We assembled a dataset of 189 mass shootings from 1966 to 2021, integrating data from multiple sources. We categorized the incidents in terms of the targeted population and shooting location. We measured “surprisal” (often known as “Shannon information content”) with respect to these features, and we scored fame from Wikipedia traffic data—a commonly used metric of fame. Surprisal was significantly higher for fame-seeking mass shooters than non-fame-seeking ones. We also registered a significant positive correlation between fame and surprisal controlling for the number of casualties and injured victims. Not only do we uncover a link between fame-seeking behavior and surprise in the attacks but also we demonstrate an association between the fame of a mass shooting and its surprise.  more » « less
Award ID(s):
1953135
PAR ID:
10420375
Author(s) / Creator(s):
; ; ; ;
Date Published:
Journal Name:
Proceedings of the National Academy of Sciences
Volume:
120
Issue:
20
ISSN:
0027-8424
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
More Like this
  1. Objectives: Mass shooting incidents have drastically increased in the United States in the last 10 years, with a disproportionate number of incidents occurring in some states. Gun laws vary greatly by state, but little research has been conducted to examine the association between the strength of state gun laws and mass shootings. This study aims to explore the aggregate effect of state gun laws on the rate of mass shooting incidents and fatalities. Study design: This was a cross-sectional time series. Methods: This study applied the negative binomial generalized linear mixed model to assess the impact of state gun laws restrictivenessdas measured by the total number of active gun lawsdon the rate of mass shooting incidents and fatalities. Results: The restrictiveness of state gun laws was significantly associated with the rate of mass shooting fatalities; specifically, for every 1 standard deviation (SD) increase in the state gun law restrictiveness score (i.e. for every additional 27 gun laws in place), the rate of mass shooting fatalities was decreased by 24% (P-value <0.0001), controlling for other predictor variables in the model. However, no significant association was found between the restrictiveness of state gun laws and rate of mass shooting incidents. Conclusions: State gun laws may not decrease the number of mass shooting events, but they appear to help reduce the number of deaths when these mass shootings occur. Better data collection on mass shootings and further research on the impacts of specific gun laws are needed to help understand the effectiveness of gun laws and inform law-based interventions. 
    more » « less
  2. Abstract Mass shootings (incidents with four or more people shot in a single event, not including the shooter) are becoming more frequent in the United States, posing a significant threat to public health and safety in the country. In the current study, we intended to analyze the impact of state-level prevalence of gun ownership on mass shootings—both the frequency and severity of these events. We applied the negative binomial generalized linear mixed model to investigate the association between gun ownership rate, as measured by a proxy (i.e., the proportion of suicides committed with firearms to total suicides), and population-adjusted rates of mass shooting incidents and fatalities at the state level from 2013 to 2022. Gun ownership was found to be significantly associated with the rate of mass shooting fatalities. Specifically, our model indicated that for every 1-SD increase—that is, for every 12.5% increase—in gun ownership, the rate of mass shooting fatalities increased by 34% (pvalue < 0.001). However, no significant association was found between gun ownership and rate of mass shooting incidents. These findings suggest that restricting gun ownership (and therefore reducing availability to guns) may not decrease the number of mass shooting events, but it may save lives when these events occur. 
    more » « less
  3. The United States has had more mass shooting incidents than any other country. It is reported that more than 1800 incidents occurred in the US during the past three years. Mass shooters often display warning signs before committing crimes, such as childhood traumas, domestic violence, firearms access, and aggressive social media posts. With the advancement of machine learning (ML), it is more possible than ever to predict mass shootings before they occur by studying the behavior of prospective mass shooters. This paper presents an ML-based system that uses various unsupervised ML models to warn about a balanced progressive tendency of a person to commit a mass shooting. Our system used two models, namely local outlier factor and K-means clustering, to learn both the psychological factors and social media activities of previous shooters to provide a probabilistic similarity of a new observation to an existing shooter. The developed system can show the similarity between a new record for a prospective shooter and one or more records from our dataset via a GUI-friendly interface. It enables users to select some social and criminal observations about the prospective shooter. Then, the webpage creates a new record, classifies it, and displays the similarity results. Furthermore, we developed a feed-in module, which allows new observations to be added to our dataset and retrains the ML models. Finally, we evaluated our system using various performance metrics. 
    more » « less
  4. Abstract Surprisal theory posits that less-predictable words should take more time to process, with word predictability quantified as surprisal, i.e., negative log probability in context. While evidence supporting the predictions of surprisal theory has been replicated widely, much of it has focused on a very narrow slice of data: native English speakers reading English texts. Indeed, no comprehensive multilingual analysis exists. We address this gap in the current literature by investigating the relationship between surprisal and reading times in eleven different languages, distributed across five language families. Deriving estimates from language models trained on monolingual and multilingual corpora, we test three predictions associated with surprisal theory: (i) whether surprisal is predictive of reading times, (ii) whether expected surprisal, i.e., contextual entropy, is predictive of reading times, and (iii) whether the linking function between surprisal and reading times is linear. We find that all three predictions are borne out crosslinguistically. By focusing on a more diverse set of languages, we argue that these results offer the most robust link to date between information theory and incremental language processing across languages. 
    more » « less
  5. Abstract Does providing information about police shootings influence policing reform preferences? We conducted an online survey experiment in 2021 among approximately 2,600 residents of 10 large US cities. It incorporated original data we collected on police shootings of civilians. After respondents estimated the number of police shootings in their cities in 2020, we randomized subjects into three treatment groups and a control group. Treatments included some form of factual information about the police shootings in respondents’ cities (e.g., the actual total number). Afterward, respondents were asked their opinions about five policing reform proposals. Police shooting statistics did not move policing reform preferences. Support for policing reforms is primarily associated with partisanship and ideology, coupled with race. Our findings illuminate key sources of policing reform preferences among the public and reveal potential limits of information-driven, numeric-based initiatives to influence policing in the US. 
    more » « less