Johannes C Eichstaedt

Stanford University | SU · Department of Psychology

Use of large language models such as ChatGPT (GPT-4) for mental health support has grown rapidly, emerging as a promising route to assess and help people with mood disorders, like depression. However, we have a limited understanding of GPT-4's schema of mental disorders, that is, how it internally associates and interprets symptoms. In this work, w...

Social media can provide real-time insight into trends in substance use, addiction, and recovery. Prior studies have used platforms such as Reddit and X (formerly Twitter), but evolving policies around data access have threatened these platforms’ usability in research. We evaluate the potential of a broad set of platforms to detect emerging trends...

Trust is predictive of civic cooperation and economic growth. Recently, the U.S. public has demonstrated increased partisan division and a surveyed decline in trust in institutions. There is a need to quantify individual and community levels of trust unobtrusively and at scale. Using observations of language across more than 16,000 Facebook users,...

In the most comprehensive population surveys, mental health is only broadly captured through questionnaires asking about “mentally unhealthy days” or feelings of “sadness.” Further, population mental health estimates are predominantly consolidated to yearly estimates at the state level, which is considerably coarser than the best estimates of physi...

Large language models (LLMs) such as Open AI’s GPT-4 (which power ChatGPT) and Google’s Gemini, built on artificial intelligence, hold immense potential to support, augment, or even eventually automate psychotherapy. Enthusiasm about such applications is mounting in the field as well as industry. These developments promise to address insufficient m...

The Cantril Ladder is among the most widely administered subjective well-being measures; every year, it is collected in 140+ countries in the Gallup World Poll and reported in the World Happiness Report. The measure asks respondents to evaluate their lives on a ladder from worst (bottom) to best (top). Prior work found Cantril Ladder scores sensiti...

Large language models (LLMs), such as OpenAI's GPT-4, Google's Bard or Meta's LLaMa, have created unprecedented opportunities for analysing and generating language data on a massive scale. Because language data have a central role in all areas of psychology, this new technology has the potential to transform the field. In this Perspective, we revie...

Full national coverage below the state level is difficult to attain through survey-based data collection. Even the largest survey-based data collections, such as the CDC’s Behavioral Risk Factor Surveillance System or the Gallup-Healthways Well-being Index (both with more than 300,000 responses p.a.) only allow for the estimation of annual averages...

The emergence of large language models (LLMs) that leverage deep learning and web-scale corpora has made it possible for artificial intelligence (AI) to tackle many higher-order cognitive tasks, with critical implications for industry, government, and labor markets in the US and globally. Here, we investigate whether existing, openly-available LLMs...

Depression has been associated with heightened first-person singular pronoun use (I-usage; e.g., “I,” “my”) and negative emotion words. However, past research has relied on nonclinical samples and nonspecific depression measures, raising the question of whether these features are unique to depression vis-à-vis frequently co-occurring conditions, es...

Wellbeing is predominantly measured through self-reports, which is time-consuming and costly. It can also be measured by automatically analysing language expressed on social media platforms, through social media text mining (SMTM). We present a systematic review based on 45 studies, and a meta-analysis of 32 convergent validities from 18 studies re...

Opioid poisoning mortality is a substantial public health crisis in the United States, with opioids involved in approximately 75% of the nearly 1 million drug related deaths since 1999. Research suggests that the epidemic is driven by both over-prescribing and social and psychological determinants such as economic stability, hopelessness, and isola...

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 pro...

Wellbeing is predominantly measured through surveys but is increasingly measured by analysing individuals' language on social media platforms using social media text mining (SMTM). To investigate whether the structure of wellbeing is similar across both data collection methods, we compared networks derived from survey items and social media languag...

Depression is known to have heterogeneous symptom manifestations. Investigating various symptoms of depression is essential to understanding underlying mechanisms and personalizing treatments. Reddit, an online peer-to-peer social media platform, contains varied communities (subreddits) where individuals discuss their detailed mental health experie...

Large Language Models (LLMs), such as OpenAI’s GPT-4 or Google’s Bard, have created unprecedented opportunities for analyzing and generating language data on a massive scale. Because language is core to all areas of psychology, this new technology holds the potential to transform the field. In this Review, we first present emerging applications of...

Extensive evidence demonstrates the effects of area-based disadvantage on a variety of life outcomes, such as increased mortality and low economic mobility. Despite these well-established patterns, disadvantage, often measured using composite indices, is inconsistently operationalized across studies. To address this issue, we systematically compare...

Compared to physical health, population mental health measurement in the U.S. is very coarse-grained. Currently, in the largest population surveys, such as those carried out by the Centers for Disease Control or Gallup, mental health is only broadly captured through "mentally unhealthy days" or "sadness", and limited to relatively infrequent state...

Introduction While surveys are a well-established instrument to capture population prevalence of mental health at a moment in time, public Twitter is a continuously available data source that can provide a broader window into population mental health. We characterized the relationship between COVID-19 case counts, stay-at-home orders due to COVID-1...

The word embedding association test (WEAT) is an important method for measuring linguistic biases against social groups such as ethnic minorities in large text corpora. It does so by comparing the semantic relatedness of words prototypical of the groups (e.g., names unique to those groups) and attribute words (e.g., ‘pleasant’ and ‘unpleasant’ word...

Empathy is a cognitive and emotional reaction to an observed situation of others. Empathy has recently attracted interest because it has numerous applications in psychology and AI, but it is unclear how different forms of empathy (e.g., self-report vs counterpart other-report, concern vs. distress) interact with other affective phenomena or demogra...

The word embedding association test (WEAT) is an important method for measuring linguistic biases against social groups such as ethnic minorities in large text corpora. It does so by comparing the semantic relatedness of words prototypical of the groups (e.g., names unique to those groups) and attribute words (e.g., 'pleasant' and 'unpleasant' word...

Background Personal sensing has shown promise for detecting behavioral correlates of depression, but there is little work examining personal sensing of cognitive and affective states. Digital language, particularly through personal text messages, is one source that can measure these markers. Methods We correlated privacy-preserving sentiment analy...

Significance On May 25, 2020, George Floyd, an unarmed Black American male, was murdered by a White police officer in Minneapolis. Footage of his death was widely shared and caused widespread protests. Using data from Gallup before and after his death, we found an unprecedented level of anger and sadness in the population, particularly among Black...

Objective We explore the personality of counties as assessed through linguistic patterns on social media. Such studies were previously limited by the cost and feasibility of large-scale surveys; however, language-based computational models applied to large social media datasets now allow for large-scale personality assessment. Method We applied a...

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 thes...

Background Oral histories from 9/11 responders to the World Trade Center (WTC) attacks provide rich narratives about distress and resilience. Artificial Intelligence (AI) models promise to detect psychopathology in natural language, but they have been evaluated primarily in non-clinical settings using social media. This study sought to test the abi...

Religion and spirituality are multidimensional constructs including practices, rituals, and experiences, though they are often treated solely in terms of belief. In this study (N = 2,389), we investigate dimensions examined in previous linguistic analysis studies—religious affiliation and experiences of unity—and new dimensions: religious services,...

Psychological research has shown that subjective well-being is sensitive to social comparison effects; individuals report decreased happiness when their neighbors earn more than they do. In this work, we use Twitter language to estimate the well-being of users, and model both individual and neighborhood income using hierarchical modeling across cou...

Looking to supplement common economic indicators, politicians and policymakers are increasingly interested in how to measure and improve the subjective well-being of communities. Theories about nonprofit organizations suggest that they represent a potential policy-amenable lever to increase community subjective well-being. Using longitudinal cross-...

Users’ information-seeking and information-sharing behavior provide socioeconomic and psychological insights that are useful to understand regional trends in health. We study the spatial variations in aggregate Google Search and Twitter trends across 208 Designated Market Areas (DMAs) in the United States and their association with regional health....

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 thes...

Background: Oral histories from 9/11 responders to the World Trade Center (WTC) attacks provide rich narratives about distress and resilience. Artificial Intelligence (AI) models promise to detect psychopathology in natural language, but they have been evaluated primarily in non-clinical settings using social media. This study sought to test the ab...

In this paper, we present an iterative graph-based approach for the detection of symptoms of COVID-19, the pathology of which seems to be evolving. More generally, the method can be applied to finding context-specific words and texts (e.g. symptom mentions) in large imbalanced corpora (e.g. all tweets mentioning #COVID-19). Given the novelty of COV...

The novelty and global scale of the COVID-19 pandemic has lead to rapid societal changes in a short span of time. As government policy and health measures shift, public perceptions and concerns also change, an evolution documented within discourse on social media. We propose a dynamic content-specific LDA topic modeling technique that can help to i...

There is increasing interest in the potential of artificial intelligence and Big Data (e.g., generated via social media) to help understand economic outcomes. But can artificial intelligence models based on publicly available Big Data identify geographical differences in entrepreneurial personality or culture? We use a machine learning model based...

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 w...

A rapidly growing literature has attempted to explain Donald Trump's success in the 2016 U.S. presidential election as a result of a wide variety of differences in individual characteristics, attitudes, and social processes. We propose that the economic and psychological processes previously established have in common that they generated or elector...

Personality psychologists are increasingly documenting dynamic, within‐person processes. Big data methodologies can augment this endeavour by allowing for the collection of naturalistic and personality‐relevant digital traces from online environments. Whereas big data methods have primarily been used to catalogue static personality dimensions, here...

Researchers and policy makers worldwide are interested in measuring the subjective well-being of populations. When users post on social media, they leave behind digital traces that reflect their thoughts and feelings. Aggregation of such digital traces may make it possible to monitor well-being at large scale. However, social media-based methods ne...

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...

This study investigates the role of the Internet in civic participation inequality across 108 countries. Merging individual-level survey data from the 2016 Gallup World Poll with country-level indices, we conduct multilevel analyses to answer three broader sets of questions: (1) Does access to the Internet increase the likelihood of civic participa...

Previous work has found strong links between the choice of social media images and users’ emotions, demographics and personality traits. In this study, we examine which attributes of profile and posted images are associated with depression and anxiety of Twitter users. We used a sample of 28,749 Facebook users to build a language prediction model o...

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 physica...

We studied whether medical conditions across 21 broad categories were predictable from social media content across approximately 20 million words written by 999 consenting patients. Facebook language significantly improved upon the prediction accuracy of demographic variables for 18 of the 21 disease categories; it was particularly effective at pre...

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 (VIA) 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 I...

Previous work has found strong links between the choice of social media images and users' emotions, demographics and personality traits. In this study, we examine which attributes of profile and posted images are associated with depression and anxiety of Twitter users. We used a sample of 28,749 Facebook users to build a language prediction model o...

Fluctuations in mood states are driven by unpredictable outcomes in daily life but also appear to drive consequential behaviors such as risk-taking. However, our understanding of the relationships between unexpected outcomes, mood, and risk-taking behavior has relied primarily upon constrained and artificial laboratory settings. Here we examine, us...

Regression equations. (PDF)

Correspondence of our valence-predictions and SwissCheese, a standard Twitter sentiment tool (Deriu, Gonzenbach, Uzdilli, Lucchi, Luca & Jaggi, 2016). (PDF)

Fixed-effects regression coefficients for model estimating effect of Citywide (Sum) Sports PEs upon Twitter-inferred Mood across all MSAs (2013; Confirmatory Dataset). (DOCX)

Exploratory analyses conducted on 2012 datasets. (A) Relationship between citywide sports prediction errors on the previous day and Twitter-inferred citywide mood. (B) Relationship between sunshine prediction errors on the current day and Twitter-inferred citywide mood. The population of each MSA included in the regression model is represented by p...

Fixed-effects regression coefficients for model estimating effect of Sunshine PEs upon Twitter-inferred mood across all MSAs (2013; confirmatory dataset). (DOCX)

Fixed-effects regression coefficients for model estimating effect of Twitter-inferred mood upon log per-person lottery purchases in Chicago (2013; confirmatory dataset). (DOCX)

Estimated causal effects in mediation analysis examining sunshine PEs (direct effect), Twitter-inferred mood (indirect effect), and Per-capita log per-person lottery purchases (outcome variable) in Chicago (2013; confirmatory dataset). (DOCX)

Fixed-effects regression coefficients for model estimating effect of citywide sports PEs upon log per-person lottery purchases in Chicago (2013; confirmatory dataset). (DOCX)

Estimated causal effects in mediation analysis examining citywide Sports PEs (direct effect), Twitter-inferred mood (indirect effect), and Per-capita log per-person lottery purchases (outcome variable) in New York city (2013; confirmatory dataset). (DOCX)

Estimated causal effects in mediation analysis examining sunshine PEs (direct effect), Twitter-inferred mood (indirect effect), and Per-capita log per-person lottery purchases (outcome variable) in New York city (2013; confirmatory dataset). (DOCX)

Fixed-effects regression coefficients for model estimating effect of Twitter-inferred Mood upon log per-person lottery purchases in New York city (2013; confirmatory dataset). (DOCX)

Fixed-effects regression coefficients for model estimating effect of citywide sports PEs upon log per-person lottery purchases in New York City (2013; confirmatory dataset). (DOCX)

Fixed-effects regression coefficients for model estimating effect of Sunshine PEs upon log per-person lottery purchases in New York city (2013; confirmatory dataset). (DOCX)

Fixed-effects regression coefficients for model estimating effect of Sunshine PEs upon log per-person lottery purchases in Chicago (2013; confirmatory dataset). (DOCX)

Estimated causal effects in mediation analysis examining citywide Sports PEs (direct effect), Twitter-inferred mood (indirect effect), and Per-capita log per-person lottery purchases (outcome variable) in Chicago (2013; confirmatory dataset). (DOCX)

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 physica...

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 physica...

Significance Depression is disabling and treatable, but underdiagnosed. In this study, we show that the content shared by consenting users on Facebook can predict a future occurrence of depression in their medical records. Language predictive of depression includes references to typical symptoms, including sadness, loneliness, hostility, rumination...

Advances in biotechnology and information technology are poised to transform well-being research. This article reviews the technologies that we predict will have the most impact on both measurement and intervention in the field of positive psychology over the next decade. These technologies include: psychopharmacology, non-invasive brain stimulatio...

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 t...

There is increasing interest in the potential of artificial intelligence and Big Data (e.g., generated via social media) to help understand economic outcomes and processes. But can artificial intelligence models, solely based on publicly available Big Data (e.g., language patterns left on social media), reliably identify geographical differences in...