Modeling Empathy and Distress in Reaction to News Stories

Computational detection and understanding of empathy is an important factor in advancing human-computer interaction. Yet to date, text-based empathy prediction has the following major limitations: It underestimates the psychological complexity of the phenomenon, adheres to a weak notion of ground truth where empathic states are ascribed by third parties, and lacks a shared corpus. In contrast, this contribution presents the first publicly available gold standard for empathy prediction. It is constructed using a novel annotation methodology which reliably captures empathy assessments by the writer of a statement using multi-item scales. This is also the first computational work distinguishing between multiple forms of empathy, empathic concern, and personal distress, as recognized throughout psychology. Finally, we present experimental results for three different predictive models, of which a CNN performs the best.Comment: To appear at EMNLP 201

Understanding and Measuring Psychological Stress using Social Media

A body of literature has demonstrated that users' mental health conditions, such as depression and anxiety, can be predicted from their social media language. There is still a gap in the scientific understanding of how psychological stress is expressed on social media. Stress is one of the primary underlying causes and correlates of chronic physical illnesses and mental health conditions. In this paper, we explore the language of psychological stress with a dataset of 601 social media users, who answered the Perceived Stress Scale questionnaire and also consented to share their Facebook and Twitter data. Firstly, we find that stressed users post about exhaustion, losing control, increased self-focus and physical pain as compared to posts about breakfast, family-time, and travel by users who are not stressed. Secondly, we find that Facebook language is more predictive of stress than Twitter language. Thirdly, we demonstrate how the language based models thus developed can be adapted and be scaled to measure county-level trends. Since county-level language is easily available on Twitter using the Streaming API, we explore multiple domain adaptation algorithms to adapt user-level Facebook models to Twitter language. We find that domain-adapted and scaled social media-based measurements of stress outperform sociodemographic variables (age, gender, race, education, and income), against ground-truth survey-based stress measurements, both at the user- and the county-level in the U.S. Twitter language that scores higher in stress is also predictive of poorer health, less access to facilities and lower socioeconomic status in counties. We conclude with a discussion of the implications of using social media as a new tool for monitoring stress levels of both individuals and counties.Comment: Accepted for publication in the proceedings of ICWSM 201

Linguistic analysis of empathy in medical school admission essays.

Objectives: This study aimed to determine whether words used in medical school admissions essays can predict physician empathy. Methods: A computational form of linguistic analysis was used for the content analysis of medical school admissions essays. Words in medical school admissions essays were computationally grouped into 20 \u27topics\u27 which were then correlated with scores on the Jefferson Scale of Empathy. The study sample included 1,805 matriculants (between 2008-2015) at a single medical college in the North East of the United States who wrote an admissions essay and completed the Jefferson Scale of Empathy at matriculation. Results: After correcting for multiple comparisons and controlling for gender, the Jefferson Scale of Empathy scores significantly correlated with a linguistic topic (r = .074, p \u3c .05). This topic was comprised of specific words used in essays such as understanding, compassion, empathy, feeling, and trust. These words are related to themes emphasized in both theoretical writing and empirical studies on physician empathy. Conclusions: This study demonstrates that physician empathy can be predicted from medical school admission essays. The implications of this methodological capability, i.e. to quantitatively associate linguistic features or words with psychometric outcomes, bears on the future of medical education research and admissions. In particular, these findings suggest that those responsible for medical school admissions could identify more empathetic applicants based on the language of their application essays

The neurogenetics of nice: receptor genes for oxytocin and vasopressin interact with threat to predict prosocial behavior.

Oxytocin, vasopressin, and their receptor genes influence prosocial behavior in the laboratory and in the context of close relationships. These peptides may also promote social engagement following threat. However, the scope of their prosocial effects is unknown. We examined oxytocin receptor (OXTR) polymorphism rs53576, as well as vasopressin receptor 1a (AVPR1a) polymorphisms rs1 and rs3 in a national sample of U.S. residents (n = 348). These polymorphisms interacted with perceived threat to predict engagement in volunteer work or charitable activities and commitment to civic duty. Specifically, greater perceived threat predicted engagement in fewer charitable activities for individuals with A/A and A/G genotypes of OXTR rs53576, but not for G/G individuals. Similarly, greater perceived threat predicted lower commitment to civic duty for individuals with one or two short alleles for AVPR1a rs1, but not for individuals with only long alleles. Oxytocin, vasopressin, and their receptor genes may significantly influence prosocial behavior and may lie at the core of the caregiving behavioral system

Modeling and Visualizing Locus of Control with Facebook Language

A body of literature has demonstrated that users' psychological traits such as personality can be predicted from their posts on social media. However, there is still a gap between the computational and descriptive analyses of the language features associated with different psychological traits, and their use by social scientists and psychologists to make deeper behavioral inferences. In this study, we aim to bridge this gap with a visualization that situates the language associated with one psychological trait in the context of other psychological dimensions. We predict Locus of Control (LoC), an individual's perception of personal control over events in their lives, from their Facebook language (F1=0.82). We then look at how language explains the relationship of LoC with consciousness and emotional stability

Recognizing Pathogenic Empathy in Social Media

Empathy is an integral part of human social life, as people care about and for others who experience adversity. However, a specific “pathogenic” form of empathy, marked by automatic contagion of negative emotions, can lead to stress and burnout. This is particularly detrimental for individuals in caregiving professions who experience empathic states more frequently, because it can result in illness and high costs for health systems. Automatically recognizing pathogenic empathy from text is potentially valuable to identify at-risk individuals and monitor burnout risk in caregiving populations. We build a model to predict this type of empathy from social media language on a data set we collected of users’ Facebook posts and their answers to a new questionnaire measuring empathy. We obtain promising results in identifying individuals' empathetic states from their social media (Pearson r = 0.252, p <0.003)