Characterizing empathy and compassion using computational linguistic analysis.

Many scholars have proposed that feeling what we believe others are feeling-often known as "empathy"-is essential for other-regarding sentiments and plays an important role in our moral lives. Caring for and about others (without necessarily sharing their feelings)-often known as "compassion"-is also frequently discussed as a relevant force for prosocial motivation and action. Here, we explore the relationship between empathy and compassion using the methods of computational linguistics. Analyses of 2,356,916 Facebook posts suggest that individuals (N = 2,781) high in empathy use different language than those high in compassion, after accounting for shared variance between these constructs. Empathic people, controlling for compassion, often use self-focused language and write about negative feelings, social isolation, and feeling overwhelmed. Compassionate people, controlling for empathy, often use other-focused language and write about positive feelings and social connections. In addition, high empathy without compassion is related to negative health outcomes, while high compassion without empathy is related to positive health outcomes, positive lifestyle choices, and charitable giving. Such findings favor an approach to moral motivation that is grounded in compassion rather than empathy. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Social comparison, parasocial relationships, and attachment style: how and when do celebrities improve self-liking?

The recent exponential increase in information available online has not only increased access to information about celebrities, but also decreased the degree to which that information is unambiguously positive. In the current work, we examined how positive celebrities (i.e. celebrities who are primarily admired) versus more ambiguous celebrities (i.e. celebrities about whom people have mixed feelings) differentially affect feelings about the self. Across three studies, we found that high attachment anxiety was associated with assimilating positive celebrities to feel better about the self, whereas attachment avoidance was associated with contrasting ambivalent celebrities to feel better to feel better about the self. Implications for parasocial relationships, social comparison, and attachment are discussed.

Closed- and open-vocabulary approaches to text analysis: A review, quantitative comparison, and recommendations.

Technology now makes it possible to understand efficiently and at large scale how people use language to reveal their everyday thoughts, behaviors, and emotions. Written text has been analyzed through both theory-based, closed-vocabulary methods from the social sciences as well as data-driven, open-vocabulary methods from computer science, but these approaches have not been comprehensively compared. To provide guidance on best practices for automatically analyzing written text, this narrative review and quantitative synthesis compares five predominant closed- and open-vocabulary methods: Linguistic Inquiry and Word Count (LIWC), the General Inquirer, DICTION, Latent Dirichlet Allocation, and Differential Language Analysis. We compare the linguistic features associated with gender, age, and personality across the five methods using an existing dataset of Facebook status updates and self-reported survey data from 65,896 users. Results are fairly consistent across methods. The closed-vocabulary approaches efficiently summarize concepts and are helpful for understanding how people think, with LIWC2015 yielding the strongest, most parsimonious results. Open-vocabulary approaches reveal more specific and concrete patterns across a broad range of content domains, better address ambiguous word senses, and are less prone to misinterpretation, suggesting that they are well-suited for capturing the nuances of everyday psychological processes. We detail several errors that can occur in closed-vocabulary analyses, the impact of sample size, number of words per user and number of topics included in open-vocabulary analyses, and implications of different analytical decisions. We conclude with recommendations for researchers, advocating for a complementary approach that combines closed- and open-vocabulary methods. (PsycInfo Database Record (c) 2021 APA, all rights reserved).

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 'topics' 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 < .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.

Cultural Differences in Tweeting about Drinking Across the US.

Excessive alcohol use in the US contributes to over 88,000 deaths per year and costs over $250 billion annually. While previous studies have shown that excessive alcohol use can be detected from general patterns of social media engagement, we characterized how drinking-specific language varies across regions and cultures in the US. From a database of 38 billion public tweets, we selected those mentioning "drunk", found the words and phrases distinctive of drinking posts, and then clustered these into topics and sets of semantically related words. We identified geolocated "drunk" tweets and correlated their language with the prevalence of self-reported excessive alcohol consumption (Behavioral Risk Factor Surveillance System; BRFSS). We then identified linguistic markers associated with excessive drinking in different regions and cultural communities as identified by the American Community Project. "Drunk" tweet frequency (of the 3.3 million geolocated "drunk" tweets) correlated with excessive alcohol consumption at both the county and state levels (r = 0.26 and 0.45, respectively, p < 0.01). Topic analyses revealed that excessive alcohol consumption was most correlated with references to drinking with friends (r = 0.20), family (r = 0.15), and driving under the influence (r = 0.14). Using the American Community Project classification, we found a number of cultural markers of drinking: religious communities had a high frequency of anti-drunk driving tweets, Hispanic centers discussed family members drinking, and college towns discussed sexual behavior. This study shows that Twitter can be used to explore the specific sociocultural contexts in which excessive alcohol use occurs within particular regions and communities. These findings can inform more targeted public health messaging and help to better understand cultural determinants of substance abuse.

The language of character strengths: Predicting morally valued traits on social media.

OBJECTIVE: Social media is increasingly being used to study psychological constructs. This study is the first to use Twitter language to investigate the 24 Values in Action Inventory of Character Strengths, which have been shown to predict important life domains such as well-being. METHOD: We use both a top-down closed-vocabulary (Linguistic Inquiry and Word Count) and a data-driven open-vocabulary (Differential Language Analysis) approach to analyze 3,937,768 tweets from 4,423 participants (64.3% female), who answered a 240-item survey on character strengths. RESULTS: We present the language profiles of (a) a global positivity factor accounting for 36% of the variances in the strengths, and (b) each of the 24 individual strengths, for which we find largely face-valid language associations. Machine learning models trained on language data to predict character strengths reach out-of-sample prediction accuracies comparable to previous work on personality (rmedian = 0.28, ranging from 0.13 to 0.51). CONCLUSIONS: The findings suggest that Twitter can be used to characterize and predict character strengths. This technique could be used to measure the character strengths of large populations unobtrusively and cost-effectively.

Can Twitter be used to predict county excessive alcohol consumption rates?

OBJECTIVES: The current study analyzes a large set of Twitter data from 1,384 US counties to determine whether excessive alcohol consumption rates can be predicted by the words being posted from each county. METHODS: Data from over 138 million county-level tweets were analyzed using predictive modeling, differential language analysis, and mediating language analysis. RESULTS: Twitter language data captures cross-sectional patterns of excessive alcohol consumption beyond that of sociodemographic factors (e.g. age, gender, race, income, education), and can be used to accurately predict rates of excessive alcohol consumption. Additionally, mediation analysis found that Twitter topics (e.g. 'ready gettin leave') can explain much of the variance associated between socioeconomics and excessive alcohol consumption. CONCLUSIONS: Twitter data can be used to predict public health concerns such as excessive drinking. Using mediation analysis in conjunction with predictive modeling allows for a high portion of the variance associated with socioeconomic status to be explained.

Empathic Concern and the Desire to Help as Separable Components of Compassionate Responding.

When do people experience versus regulate responses to compassion-evoking stimuli? We hypothesized that compassionate responding is composed of two factors-empathic concern and the desire to help-and that these would be differentially affected by perspective taking and self-affirmation. Exploratory (Study 1) and confirmatory (Study 2) factor analyses indicated that a compassion measure consisted of two factors corresponding to empathic concern and the desire to help. In Study 1 ( N = 237), participants with high emotion regulation ability reported less empathic concern for multiple children than for one, but perspective taking prevented this effect. In Study 2 ( N = 155), participants reported less desire to help multiple children, but only in the presence of self-affirmation. In both the studies, empathic concern predicted greater distress while the desire to help predicted greater chances of donating. Compassionate responding may consist of two separable facets that collapse under distinct conditions and that predict distinct outcomes.

Empathy, target distress, and neurohormone genes interact to predict aggression for others-even without provocation.

Can empathy for others motivate aggression on their behalf? This research examined potential predictors of empathy-linked aggression including the emotional state of empathy, an empathy target's distress state, and the function of the social anxiety-modulating neuropeptides oxytocin and vasopressin. In Study 1 (N = 69), self-reported empathy combined with threat to a close other and individual differences in genes for the vasopressin receptor (AVPR1a rs3) and oxytocin receptor (OXTR rs53576) to predict self-reported aggression against a person who threatened a close other. In Study 2 (N = 162), induced empathy for a person combined with OXTR variation or with that person's distress and AVPR1a variation led to increased amount of hot sauce assigned to that person's competitor. Empathy uniquely predicts aggression and may do so by way of aspects of the human caregiving system in the form of oxytocin and vasopressin.

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.