Johan Bollen’s research while affiliated with Indiana University Bloomington and other places

The proliferation of Large Language Models (LLMs) in late 2022 has impacted academic writing, threatening credibility, and causing institutional uncertainty. We seek to determine the degree to which LLMs are used to generate critical text as opposed to being used for editing, such as checking for grammar errors or inappropriate phrasing. In our study, we analyze arXiv papers for stylistic segmentation, which we measure by varying a PELT threshold against a Bayesian classifier trained on GPT-regenerated text. We find that LLM-attributed language is not predictive of stylistic segmentation, suggesting that when authors use LLMs, they do so uniformly, reducing the risk of hallucinations being introduced into academic preprints.

Background: Social media has been identified as a possible risk factor for depression in young adults, though it is unclear if it is associated with other dimensions of psychopathology. Moreover, it is unclear what aspects of social media use beyond frequency are associated to symptoms. Methods: A large sample of young adults (N= 7453) participated in a screening of transdiagnostic symptoms of psychopathology (i.e., depression, panic, generalized anxiety, social anxiety, alcohol use, drug use, insomnia, and pain). We correlated depression scores to time spent on social media and problematic social media use. We used a principal components analysis to identify shared dimensions of psychopathology and explored the correlations between symptom dimensions and social media use. Results: Participants who reported higher social media usage exhibited significantly higher self-reported depression. Our PCA suggested two dimensions of psychopathology consistent with the HiTOP model which we dubbed “emotional” and “externalizing” symptoms. Time spent on social media was correlated with emotional symptoms and with externalizing symptoms, albeit to a lesser extent. However, these associations were not significant after controlling for problematic social media use, which had differential relations to dimensions of psychopathology. e-value analysis suggested that unmeasured confounds could potentially explain these associations. Discussion: The association between time spent on social media and psychopathology could be accounted for by the way in which social media is used. However, unmeasured confounding remains a threat to these inferences.

Depression is one of the leading causes of disability worldwide. Individuals with depression often experience unrealistic and overly negative thoughts, i.e. cognitive distortions, that cause maladaptive behaviors and feelings. Now that a majority of the US population uses social media platforms, concerns have been raised that they may serve as a vector for the spread of distorted ideas and thinking amid a global mental health epidemic. Here, we study how individuals (N=838) interact with distorted content on social media platforms using a simulated environment similar to Twitter (now X). We find that individuals with higher depression symptoms tend to prefer distorted content more than those with fewer symptoms. However, a simple one-shot intervention can teach individuals to recognize and drastically reduce interactions with distorted content across the entire depression scale. This suggests that distorted thinking on social media may disproportionally affect individuals with depression, but simple awareness training can mitigate this effect. Our findings have important implications for understanding the role of social media in propagating distorted thinking and potential paths to reduce the societal cost of mental health disorders.

Background Depression and anxiety are associated with patterns of negative thinking that can be targeted through cognitive restructuring as a part of cognitive therapy (CT) or cognitive behavioral therapy (CBT). Our team has created a set of cognitive distortion schemata (CDS) n-grams based on theories underlying CT to measure the linguistic markers that indicate cognitive vulnerability to depression. These CDS were specifically designed to examine online language. Our prior work supports a relationship between CDS and a diagnosis of depression, but less is known about the relationship between online language, CDS, and anxiety. The current study measures if CDS can be detected in people who report anxiety symptoms, and whether CDS increase with symptom severity. Methods 1,377 participants were recruited from a study assessing social media use and mental health symptoms, the Studies of Online Cohorts of Internalizing Symptoms and Language (SOCIAL). From this, 804 timelines were harvested, and after removing missing data and bots, our final sample was 537 respondents who posted 999,859 tweets. This is a longitudinal, multi-method design, using surveys and text-based analysis of social media timelines. We used bootstrap resampling to compare differences in CDS prevalence in anxious and depressed participants. Results CDS can be observed in anxiety disorders, significantly increase as a function of anxiety symptom severity, and are related to depression and anxiety comorbidity. Conclusions Using behavioral, affective, and cognitive indicators of distorted thinking from social media may yield new insight into the trajectories of depression and anxiety. This work has implications for the future of CT/CBT and other online interventions that target distorted thinking styles.

The World Health Organization (WHO) declared the COVID-19 outbreak a Public Health Emergency of International Concern (PHEIC) on January 31, 2020. However, rumors of a ``mysterious virus’' had already been circulating in China in December 2019, possibly preceding the first confirmed COVID-19 case. Understanding how awareness about an emerging pandemic spreads through society is vital not only for enhancing disease surveillance, but also for mitigating demand shocks and social inequities, such as shortages of personal protective equipment (PPE) and essential supplies. Here we leverage a massive e-commerce dataset comprising 150 billion online queries and purchase records from 94 million people to detect the traces of early awareness and public response during the cryptic transmission period of COVID-19. Our analysis focuses on identifying information gaps across different demographic cohorts, revealing significant social inequities and the role of cultural factors in shaping awareness diffusion and response behaviors. By modeling awareness diffusion in heterogeneous social networks and analyzing online shopping behavior, we uncover the evolving characteristics of vulnerable populations. Our findings expand the theoretical understanding of awareness spread and social inequality in the early stages of a pandemic, highlighting the critical importance of e-commerce data and social network data in effectively and timely addressing future pandemic challenges. We also provide actionable recommendations to better manage and mitigate dynamic social inequalities in public health crises.

The World Health Organization (WHO) declared the COVID-19 outbreak a Public Health Emergency of International Concern (PHEIC) on January 31, 2020. However, rumors of a "mysterious virus" had already been circulating in China in December 2019, possibly preceding the first confirmed COVID-19 case. Understanding how awareness about an emerging pandemic spreads through society is vital not only for enhancing disease surveillance, but also for mitigating demand shocks and social inequities, such as shortages of personal protective equipment (PPE) and essential supplies. Here we leverage a massive e-commerce dataset comprising 150 billion online queries and purchase records from 94 million people to detect the traces of early awareness and public response during the cryptic transmission period of COVID-19. Our analysis focuses on identifying information gaps across different demographic cohorts, revealing significant social inequities and the role of cultural factors in shaping awareness diffusion and response behaviors. By modeling awareness diffusion in heterogeneous social networks and analyzing online shopping behavior, we uncover the evolving characteristics of vulnerable populations. Our findings expand the theoretical understanding of awareness spread and social inequality in the early stages of a pandemic, highlighting the critical importance of e-commerce data and social network data in effectively and timely addressing future pandemic challenges. We also provide actionable recommendations to better manage and mitigate dynamic social inequalities in public health crises.

Background: Young adults in the United States are heavy users of social media which has been identified as a possible risk factor for depression. However, it is unclear whether social media use is associated with internalizing disorder symptom vulnerability beyond depression or whether it is related to other relevant symptom dimensions like externalizing symptoms (e.g., substance use). Moreover, it is unclear what aspects of social media use beyond frequency are associated to symptoms. Methods: A large cohort of undergraduate students from a Midwest university (N= 7123) participated in a screening of transdiagnostic symptoms of psychopathology. Data collected included: time spent on social media, social media platforms used, and aspects of problematic social media use (i.e., use for emotion regulation, compulsive use, or social-media-related impairment). We also collected sociodemographics and self-reported symptoms: depression, panic, generalized anxiety, social anxiety, alcohol use, drug use, insomnia, and pain. We correlated depression scores to time spent on social media to confirm prior findings of an association between social media use and depression. We then used a principal components analysis to identify shared dimensions of psychopathology across these symptoms and explored the correlations between symptom dimensions and elements of social media use. Results: Participants who reported higher social media usage exhibited significantly higher self-reported depression. Our PCA suggested two dimensions of psychopathology consistent with the HiTOP model which we dubbed “emotional” and “externalizing” symptoms. Time spent on social media was correlated with emotional symptoms and with externalizing symptoms, albeit to a lesser extent. However, these associations were not significant after controlling for problematic social media use. Moreover, problematic social media use had differential relations to dimensions of psychopathology. For example, using social media for emotion regulation was associated with more severe internalizing symptoms but less severe externalizing symptoms. E-value analysis suggested that unmeasured confounds could potentially explain these associations. Discussion: Our results suggest that the association between time spent on social media and psychopathology could be accounted for by the way in which social media is used, which in turn is differentially associated to emotional and externalizing symptoms. However, unmeasured confounding remains a threat to these inferences.

Background: Young adults in the United States are heavy users of social media which has been identified as a possible risk factor for depression. However, it is unclear whether social media use is associated with internalizing disorder symptom vulnerability beyond depression or whether it is related to other relevant symptom dimensions like externalizing symptoms (e.g., substance use). Moreover, it is unclear what aspects of social media use beyond frequency are associated to symptoms. Methods: A large cohort of undergraduate students from a Midwest university (N= 7123) participated in a screening of transdiagnostic symptoms of psychopathology. Data collected included: time spent on social media, social media platforms used, and aspects of problematic social media use (i.e., use for emotion regulation, compulsive use, or social-media-related impairment). We also collected sociodemographics and self-reported symptoms: depression, panic, generalized anxiety, social anxiety, alcohol use, drug use, insomnia, and pain. We correlated depression scores to time spent on social media to confirm prior findings of an association between social media use and depression. We then used a principal components analysis to identify shared dimensions of psychopathology across these symptoms and explored the correlations between symptom dimensions and elements of social media use. Results: Participants who reported higher social media usage exhibited significantly higher self-reported depression. Our PCA suggested two dimensions of psychopathology consistent with the HiTOP model which we dubbed “emotional” and “externalizing” symptoms. Time spent on social media was correlated with emotional symptoms and with externalizing symptoms, albeit to a lesser extent. However, these associations were not significant after controlling for problematic social media use. Moreover, problematic social media use had differential relations to dimensions of psychopathology. For example, using social media for emotion regulation was associated with more severe internalizing symptoms but less severe externalizing symptoms. E-value analysis suggested that unmeasured confounds could potentially explain these associations. Discussion: Our results suggest that the association between time spent on social media and psychopathology could be accounted for by the way in which social media is used, which in turn is differentially associated to emotional and externalizing symptoms. However, unmeasured confounding remains a threat to these inferences.

BACKGROUND Depression and anxiety are associated with patterns of negative thinking that can be targeted through cognitive restructuring as a part of cognitive behavioral therapy (CBT). Our team has created a set of cognitive distortion schemata (CDS) n-grams based on theories underlying CBT to measure the linguistic markers that indicate cognitive vulnerability to depression. These CDS were specifically designed to examine online language. OBJECTIVE Our prior work supports a relationship between CDS and a diagnosis of depression, but less is known about the relationship between online language, CDS, and anxiety. The current study measures if CDS can be detected in people who report an anxiety symptoms, and if CDS increase with symptom severity. METHODS 691 participants were recruited from a study assessing social media use and mental health symptoms, the Studies of Online Cohorts of Internalizing Symptoms and Language (SOCIAL). We used bootstrap resampling to compare differences in CDS prevalence in anxious and depressed participants. RESULTS CDS can be observed in anxiety disorders, increase as a function of anxiety symptom severity, and are related to depression and anxiety comorbidity. CONCLUSIONS Using behavioral, affective, and cognitive indicators of distorted thinking from social media may yield new insight into the trajectories of depression and anxiety. This work has implications for the future of CBT and other online interventions that target distorted thinking styles. CLINICALTRIAL n/a

Few disagree that we should pass on the Earth in good shape to future generations, and many scientists want their work to contribute to that goal. Recent work has shown that hopelessness stands in the way of people taking an active attitude. At the same time, it is becoming clear what can be done about that: providing compelling visions of attractive futures and highlighting feasible pathways. Currently, science and the humanities are not well designed for this task. Practices that stand in the way of a more holistic change‐making approach include proposal‐based funding, paralyzing rigor requirements, and a focus on explanation rather than action. Removing those barriers may require culture shifts, a notoriously difficult and slow kind of change. Meanwhile, realistic inspiring future scenarios can be developed by bringing diverse thinkers together in environments where time, space, and immediate outcomes are not pressing.