Molly Ireland

Language Style Matching Predicts Relationship Initiation and Stability

Previous relationship research has largely ignored the importance of similarity in how people talk with one another. Using natural language samples, we investigated whether similarity in dyads’ use of function words, called language style matching (LSM), predicts outcomes for romantic relationships. In Study 1, greater LSM in transcripts of 40 speed dates predicted increased likelihood of mutual romantic interest (odds ratio = 3.05). Overall, 33.3% of pairs with LSM above the median mutually desired future contact, compared with 9.1% of pairs with LSM at or below the median. In Study 2, LSM in 86 couples’ instant messages positively predicted relationship stability at a 3-month follow-up (odds ratio = 1.95). Specifically, 76.7% of couples with LSM greater than the median were still dating at the follow-up, compared with 53.5% of couples with LSM at or below the median. LSM appears to reflect implicit interpersonal processes central to romantic relationships.

Future-oriented tweets predict lower county-level HIV prevalence in the United States.

OBJECTIVE Future orientation promotes health and well-being at the individual level. Computerized text analysis of a dataset encompassing billions of words used across the United States on Twitter tested whether community-level rates of future-oriented messages correlated with lower human immunodeficiency virus (HIV) rates and moderated the association between behavioral risk indicators and HIV. METHOD Over 150 million tweets mapped to U.S. counties were analyzed using 2 methods of text analysis. First, county-level HIV rates (cases per 100,000) were regressed on aggregate usage of future-oriented language (e.g., will, gonna). A second data-driven method regressed HIV rates on individual words and phrases. RESULTS Results showed that counties with higher rates of future tense on Twitter had fewer HIV cases, independent of strong structural predictors of HIV such as population density. Future-oriented messages also appeared to buffer health risk: Sexually transmitted infection rates and references to risky behavior on Twitter were associated with higher HIV prevalence in all counties except those with high rates of future orientation. Data-driven analyses likewise showed that words and phrases referencing the future (e.g., tomorrow, would be) correlated with lower HIV prevalence. CONCLUSION Integrating big data approaches to text analysis and epidemiology with psychological theory may provide an inexpensive, real-time method of anticipating outbreaks of HIV and etiologically similar diseases.

Action Tweets Linked to Reduced County-Level HIV Prevalence in the United States: Online Messages and Structural Determinants.

Abstract HIV is uncommon in most US counties but travels quickly through vulnerable communities when it strikes. Tracking behavior through social media may provide an unobtrusive, naturalistic means of predicting HIV outbreaks and understanding the behavioral and psychological factors that increase communities’ risk. General action goals, or the motivation to engage in cognitive and motor activity, may support protective health behavior (e.g., using condoms) or encourage activity indiscriminately (e.g., risky sex), resulting in mixed health effects. We explored these opposing hypotheses by regressing county-level HIV prevalence on action language (e.g., work, plan) in over 150 million tweets mapped to US counties. Controlling for demographic and structural predictors of HIV, more active language was associated with lower HIV rates. By leveraging language used on social media to improve existing predictive models of geographic variation in HIV, future targeted HIV-prevention interventions may have a better chance of reaching high-risk communities before outbreaks occur.

A Dictionary-Based Comparison of Autobiographies by People and Murderous Monsters

People typically assume that killers are mentally ill or fundamentally different from the rest of humanity. Similarly, people often associate mental health conditions (such as schizophrenia or autism) with violence and otherness - treatable perhaps, but not empathically understandable. We take a dictionary approach to explore word use in a set of autobiographies, comparing the narratives of 2 killers (Adolf Hitler and Elliot Rodger) and 39 non-killers. Although results suggest several dimensions that differentiate these autobiographies - such as sentiment, temporal orientation, and references to death - they appear to reflect subject matter rather than psychology per se. Additionally, the Rodger text shows roughly typical developmental arcs in its use of words relating to friends, family, sex, and affect. From these data, we discuss the challenges of understanding killers and people in general.

Can you catch Ebola from a stork bite? Inductive reasoning influences generalization of perceived zoonosis risk

Emerging zoonoses are a prominent global health threat. Human beliefs are central to drivers of emerging zoonoses, yet little is known about how people make inferences about risk in such scenarios. We present an inductive account of zoonosis risk perception, suggesting that beliefs about the range of animals able to transmit diseases to each other influence how people generalize risks to other animals and health behaviors. Consistent with our account, in Study 1, we find that participants who endorse higher likelihoods of cross-species disease transmission have stronger intentions to report animal bites. In Study 2, using real-world descriptions of Ebola virus from the WHO and CDC, we find that communications conveying a broader range of animals as susceptible to the virus increase intentions to report animal bites and decrease perceived safety of wild game meat. These results suggest that inductive reasoning principles may be harnessed to modulate zoonosis risk perception and combat emerging infectious diseases.

The Clinical Panel: Leveraging Psychological Expertise During NLP Research

Computational social science is, at its core, a blending of disciplines—the best of human experience, judgement, and anecdotal case studies fused with novel computational methods to extract subtle patterns from immense data. Jointly leveraging such diverse approaches effectively is decidedly nontrivial, but with tremendous potential benefits. We provide frank assessments from our work bridging the computational linguistics and psychology communities during a range of short and longterm engagements, in the hope that these assessments might provide a foundation upon which those embarking on novel computational social science projects might structure their interactions.

Can you catch Ebola from a stork bite? Inductive reasoning influences on zoonosis risk perception

Emerging zoonoses are a prominent global health threat. Human beliefs are central to drivers of emerging zoonoses, yet little is known about the factors that influence perceived risks of animal contact. We present an inductive account of zoonosis risk perception, suggesting that beliefs about the range of animals that are able to transmit diseases to each other influence zoonosis risk perception. Consistent with our account, in Study 1, we find that participants who endorse higher likelihoods of cross-species disease transmission have stronger intention to report animal bites. In Study 2, using real world descriptions of Ebola virus from the WHO and CDC, we find that communications conveying a broader range of animals as susceptible increase intentions to report animal bites and decrease perceived safety of wild game meat. These results suggest that cognitive factors may be harnessed to modulate zoonosis risk perception and combat emerging infectious diseases.