PREDICTING COLLECTIVE VIOLENCE FROM COORDINATED HOSTILE INFORMATION CAMPAIGNS IN SOCIAL MEDIA

The ability to predict conflicts prior to their occurrence can help deter the outbreak of collective violence and avoid human suffering. Existing approaches use statistical and machine learning models, and even social network analysis techniques; however, they are generally confined to long-range predictions in specific regions and are based on only a few languages. Understanding collective violence from signals in multiple or mixed languages in social media remains understudied. In this work, we construct a multilingual language model (MLLM) that can accept input from any language in social media, a model that is language-agnostic in nature. The purpose of this study is twofold. First, it aims to collect a multilingual violence corpus from archived Twitter data using a proposed set of heuristics that account for spatial-temporal features around past and future violent events. And second, it attempts to compare the performance of traditional machine learning classifiers against deep learning MLLMs for predicting message classes linked to past and future occurrences of violent events. Our findings suggest that MLLMs substantially outperform traditional ML models in predictive accuracy. One major contribution of our work is that military commands now have a tool to evaluate and learn the language of violence across all human languages. Finally, we made the data, code, and models publicly available.Outstanding ThesisCommander, Ecuadorian NavyApproved for public release. Distribution is unlimited

Three Essays on the Relationship Between Mental Illness

Access to firearms among individuals with mental health problems has been a source of protracted debate among policymakers, the media, and the public, writ large. At the center of this debate is the question of whether mental illness drives the nation’s gun violence problem. The lack of substantial empirical evidence, due in part to limited access to quality data, plays a significant role in perpetuating ongoing debate. To address this problem, I conducted three studies that explored the relationship between mental health problems and firearm access using empirical methods and data sources that have gone underutilized in the mental illness-firearm literature. Using data from the National Comorbidity Study Replication (NCS-R), my first paper compared clinical, cultural, and criminological explanations for firearm access and carrying among people with and without mental health problems. My second paper estimates a predictive model to approximate multiyear firearm access among individuals with mental illnesses using data from both the NCS-R and the National Survey of Drug Use and Health. The paper also includes a simulation analysis to explore the potential effects of various firearm policies on gun access among the target population. Finally, because data on gun access, alone, is of limited use in explicating the relationship between mental illness and gun violence, the third paper will report the results of a study exploring the consequences of gun access among a sample of individuals with severe mental illnesses recently released from inpatient treatment

People Talking and AI Listening: How Stigmatizing Language in EHR Notes Affect AI Performance

Electronic health records (EHRs) serve as an essential data source for the envisioned artificial intelligence (AI)-driven transformation in healthcare. However, clinician biases reflected in EHR notes can lead to AI models inheriting and amplifying these biases, perpetuating health disparities. This study investigates the impact of stigmatizing language (SL) in EHR notes on mortality prediction using a Transformer-based deep learning model and explainable AI (XAI) techniques. Our findings demonstrate that SL written by clinicians adversely affects AI performance, particularly so for black patients, highlighting SL as a source of racial disparity in AI model development. To explore an operationally efficient way to mitigate SL's impact, we investigate patterns in the generation of SL through a clinicians' collaborative network, identifying central clinicians as having a stronger impact on racial disparity in the AI model. We find that removing SL written by central clinicians is a more efficient bias reduction strategy than eliminating all SL in the entire corpus of data. This study provides actionable insights for responsible AI development and contributes to understanding clinician behavior and EHR note writing in healthcare.Comment: 54 pages, 9 figure

Abnormal attentions towards the British Royal Family. Factors associated with approach and escalation

Abnormal approach and escalation from communication to physical intrusion are central concerns in managing risk to prominent people. This study was a retrospective analysis of police files of those who have shown abnormal attentions toward the British Royal Family. Approach (n = 222), compared with communication only (n = 53), was significantly associated with specific factors, most notably serious mental illness and grandiosity. In a sample of those who engaged in abnormal communication (n = 132), those who approached (n = 79) were significantly more likely to evidence mental illness and grandiosity, to use multiple communications, to employ multiple means of communication, and to be driven by motivations that concerned a personal entitlement to the prominent individual. Logistic regression produced a model comprising grandiosity, multiple communications, and multiple means of communication, for which receiver operating characteristic (ROC) analysis gave an area under the curve (AUC) of 0.82. The implications of these findings are discussed in relation to those for other target groups