How developments in natural language processing help us in understanding human behaviour.

The ways people use language can reveal clues to their emotions, social behaviours, thinking styles, cultures and the worlds around them. In the past two decades, research at the intersection of social psychology and computer science has been developing tools to analyse natural language from written or spoken text to better understand social processes and behaviour. The goal of this Review is to provide a brief overview of the methods and data currently being used and to discuss the underlying meaning of what language analyses can reveal in comparison with more traditional methodologies such as surveys or hand-scored language samples.

Exploring adult hearing aid user experiences: meaning extraction methods, content patterns, and associations with demographic and outcome variables.

OBJECTIVE: Employing automated language analysis, specifically Meaning Extraction Method (MEM) and Principal Component Analysis (PCA), to identify key factors in open-text responses about hearing aid experiences. DESIGN: Exploratory, cross-sectional design, using an online questionnaire. Responses to a single open-ended question were analysed using MEM, PCA, regression, and correlation analyses. STUDY SAMPLE: Participants (n = 538) included adult hearing aid users sampled from the Hearing Tracker website community and Lexie Hearing user databases in the United States. RESULTS: The MEM-derived items revealed six factors related to hearing aid experiences: (1) life change, (2) social situation, (3) quality of life, (4) impact and speech understanding, (5) communication and interaction, and (6) music and environmental sounds. IOI-HA item 3 had the most statistically significant correlations with PCA factors. Quantile regression revealed that factors one and two significantly predicted the IOI-HA total score. Positive correlations were observed between self-reported hearing difficulty and factors one, four, and five, as well as between factor one and general health and factor two and physical activity. CONCLUSION: Natural language analysis of open-ended textual responses can offer valuable insights into hearing aid users' experiences. Future studies should aim to refine this methodology to enhance clinical relevance and generalisability.

Verbal behavior and the future of social science.

Natural language processing (NLP)-previously the domain of a select few language and computer scientists-is undergoing an unprecedented surge in popularity across disciplines. The ubiquity of language data, alongside extremely rapid methodological innovations, has magnetized the field, attracting researchers with the promise of measuring, forecasting, and understanding the most central questions in business, psychology, biology, sociology, the humanities, and beyond. The power of language analysis to reveal insights into human thought, feeling, and behavior has become a core interest emerging from recent technological advances, which are being probed to unearth deeply embedded truths about the human condition. However, NLP research has reached a critical juncture, sitting at the cusp of societal transformation in many aspects of daily life. The details of how NLP research develops over the next 3-5 years will define this transformation. In this emerging, near-infinite space of NLP-driven research, we provide a critical frame of reference for how, when, and why these technologies should evolve in a particularly transdisciplinary manner. Specifically, we discuss (a) the urgency of pairing existing and emerging NLP research with existing scientific knowledge, theory, and principles from the behavioral sciences; (b) the coevolution of NLP technologies; and (c) the practical implications and ethical consequences of expanding language analysis using broader psychosocial theories of the human condition. While our discussion focuses principally on using language as a window in the individual mind, this topic holds substantial implications for other disciplines and lines of inquiry, including the dynamics of social interaction and beyond. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

HIV, multimorbidity, and health-related quality of life in rural KwaZulu-Natal, South Africa: A population-based study.

Health-related quality of life (HRQoL) assesses the perceived impact of health status across life domains. Although research has explored the relationship between specific conditions, including HIV, and HRQoL in low-resource settings, less attention has been paid to the association between multimorbidity and HRQoL. In a secondary analysis of cross-sectional data from the Vukuzazi ("Wake up and know ourselves" in isiZulu) study, which identified the prevalence and overlap of non-communicable and infectious diseases in the uMkhanyakunde district of KwaZulu-Natal, we (1) evaluated the impact of multimorbidity on HRQoL; (2) determined the relative associations among infectious diseases, non-communicable diseases (NCDs), and HRQoL; and (3) examined the effects of controlled versus non-controlled disease on HRQoL. HRQoL was measured using the EQ-5D-3L, which assesses overall perceived health, five specific domains (mobility, self-care, usual activities, pain/discomfort, and anxiety/depression), and three levels of problems (no problems, some problems, and extreme problems). Six diseases and disease states were included in this analysis: HIV, diabetes, stroke, heart attack, high blood pressure, and TB. After examining the degree to which number of conditions affects HRQoL, we estimated the effect of joint associations among combinations of diseases, each HRQoL domain, and overall health. Then, in one set of ridge regression models, we assessed the relative impact of HIV, diabetes, stroke, heart attack, high blood pressure, and tuberculosis on the HRQoL domains; in a second set of models, the contribution of treatment (controlled vs. uncontrolled disease) was added. A total of 14,008 individuals were included in this analysis. Having more conditions adversely affected perceived health (r = -0.060, p<0.001, 95% CI: -0.073 to -0.046) and all HRQoL domains. Infectious conditions were related to better perceived health (r = 0.051, p<0.001, 95% CI: 0.037 to 0.064) and better HRQoL, whereas non-communicable diseases (NCDs) were associated with worse perceived health (r = -0.124, p<0.001, -95% CI: 0.137 to -0.110) and lower HRQoL. Particular combinations of NCDs were detrimental to perceived health, whereas HIV, which was characterized by access to care and suppressed viral load in the large majority of those affected, was counterintuitively associated with better perceived health. With respect to disease control, unique combinations of uncontrolled NCDs were significantly related to worse perceived health, and controlled HIV was associated with better perceived health. The presence of controlled and uncontrolled NCDs was associated with poor perceived health and worse HRQoL, whereas the presence of controlled HIV was associated with improved HRQoL. HIV disease control may be critical for HRQoL among people with HIV, and incorporating NCD prevention and attention to multimorbidity into healthcare strategies may improve HRQoL.

Comparing the language style of heads of state in the US, UK, Germany and Switzerland during COVID-19.

The COVID-19 pandemic posed a global threat to nearly every society around the world. Individuals turned to their political leaders to safely guide them through this crisis. The most direct way political leaders communicated with their citizens was through official speeches and press conferences. In this report, we compare psychological language markers of four different heads of state during the early stage of the pandemic. Specifically, we collected all pandemic-related speeches and press conferences delivered by political leaders in the USA (Trump), UK (Johnson), Germany (Merkel), and Switzerland (Swiss Federal Council) between February 27th and August 31st, 2020. We used natural language analysis to examine language markers of expressed positive and negative emotions, references to the community (we-talk), analytical thinking, and authenticity and compare these language markers across the four nations. Level differences in the language markers between the leaders can be detected: Trump's language was characterized by a high expression of positive emotion, Merkel's by a strong communal focus, and Johnson's and the Swiss Federal Council by a high level of analytical thinking. Overall, these findings mirror different strategies used by political leaders to deal with the COVID-19 pandemic.

Posting patterns in peer online support forums and their associations with emotions and mood in bipolar disorder: Exploratory analysis.

BACKGROUND: Mental health (MH) peer online forums offer robust support where internet access is common, but healthcare is not, e.g., in countries with under-resourced MH support, rural areas, and during pandemics. Despite their widespread use, little is known about who posts in such forums, and in what mood states. The discussion platform Reddit is ideally suited to study this as it hosts forums (subreddits) for MH and non-MH topics. In bipolar disorder (BD), where extreme mood states are core defining features, mood influences are particularly relevant. OBJECTIVES: This exploratory study investigated posting patterns of Reddit users with a self-reported BD diagnosis and the associations between posting and emotions, specifically: 1) What proportion of the identified users posts in MH versus non-MH subreddits? 2) What differences exist in the emotions that they express in MH or non-MH subreddit posts? 3) How does mood differ between those users who post in MH subreddits compared to those who only post in non-MH subreddits? METHODS: Reddit users were automatically identified via self-reported BD diagnosis statements and all their 2005-2019 posts were downloaded. First, the percentages of users who posted only in MH (non-MH) subreddits were calculated. Second, affective vocabulary use was compared in MH versus non-MH subreddits by measuring the frequency of words associated with positive emotions, anxiety, sadness, anger, and first-person singular pronouns via the LIWC text analysis tool. Third, a logistic regression distinguished users who did versus did not post in MH subreddits, using the same LIWC variables (measured from users' non-MH subreddit posts) as predictors, controlling for age, gender, active days, and mean posts/day. RESULTS: 1) Two thirds of the identified 19,685 users with a self-reported BD diagnosis posted in both MH and non-MH subreddits. 2) Users who posted in both MH and non-MH subreddits exhibited less positive emotion but more anxiety and sadness and used more first-person singular pronouns in their MH subreddit posts. 3) Feminine gender, higher positive emotion, anxiety, and sadness were significantly associated with posting in MH subreddits. CONCLUSIONS: Many Reddit users who disclose a BD diagnosis use a single account to discuss MH and other concerns. Future work should determine whether users exhibit more anxiety and sadness in their MH subreddit posts because they more readily post in MH subreddits when experiencing lower mood or because they feel more able to express negative emotions in these spaces. MH forums may reflect the views of people who experience more extreme mood (outside of MH subreddits) compared to people who do not post in MH subreddits. These findings can be useful for MH professionals to discuss online forums with their clients. For example, they may caution them that forums may underrepresent people living well with BD.

Uncovering the Social-Cognitive Contributors to Social Dysfunction in Borderline Personality Disorder Through Language Analysis.

Borderline personality disorder (BPD) is characterized by severe interpersonal dysfunction, yet the underlying nature of such dysfunction remains poorly understood. The present study adopted a behavioral approach to more objectively describe the social-cognitive contributors to interpersonal dysfunction in BPD. Participants (N = 530) completed an online survey comprising validated measures of BPD features and other problematic interpersonal traits (e.g., narcissism), as well as a writing prompt where they were asked to share their personal thoughts about relationships. Computerized language analysis methods were used to quantify various psychosocial dimensions of participants' writing, which were incorporated into a principal component analysis. Analyses revealed four core social dimensions of thought: (1) Connectedness/Intimacy; (2) Immediacy; (3) Social Rumination; (4) Negative Affect. All four dimensions correlated with BPD features in intuitive ways, some of which were specific to BPD. This study highlights the value of natural language analysis to explore fundamental dimensions of personality disorder.

An exploration of marks/injuries related to BDSM sexual experiences.

Background: Despite its growing prevalence, BDSM practice (bondage, discipline, dominance, submission, and sadomasochism) is still stigmatized, and little is known about the physical marks and injuries that individuals sustain from consensual BDSM-related activities. Aim: In this exploratory study, we examined BDSM sexual experiences and the physical marks and injuries resulting from these experiences in a convenience sample of 513 US adults. Methods: We examined the extent to which individuals at various stages of BDSM exploration engage with different types of BDSM and kink behaviors, along with the frequency with which they communicate about, inflict, and receive physical marks from these activities. Outcomes: Our main outcome measures were intentional and unintentional marks sustained from BDSM-related activities, BDSM experiences, and the use of safe words. Results: Results revealed that BDSM marks and injuries are common and quite varied (from small scratches to very large bruises) and unintentional and intentional marks differ in terms of typical size, place on body, and severity of injury. Additionally, BDSM experience is positively associated with the use of safe words and marking behaviors. Clinical Translation: Disclosure of marks and injuries, intentional and unintentional, may be improved if health care providers are aware of, and make efforts to reduce stigma surrounding, BDSM and rough sex activity. Strengths and Limitations: The findings of this study must be interpreted with acknowledgment of the limitations that it was a survey study with a convenience sample. However, as one of the few studies exploring the marks and injuries sustained from BDSM activities, the exploration of intentional and unintentional marks sustained provides a critical starting point for future examination of BDSM consequences that intersect with health care and the law. Conclusion: Marks and injuries from BDSM-related activities are common, and despite the greater use of safe words, practitioners with more BDSM experience inflict more marks than those with less BDSM experience.

Emotional Granularity is Associated with Daily Experiential Diversity.

Emotional granularity is the ability to create differentiated and nuanced emotional experiences and is associated with positive health outcomes. Individual differences in granularity are hypothesized to reflect differences in emotion concepts, which are informed by prior experience and impact current and future experience. Greater variation in experience, then, should be related to the rich and diverse emotion concepts that support higher granularity. Using natural language processing methods, we analyzed descriptions of everyday events to estimate the diversity of contexts and activities encountered by participants. Across three studies varying in language (English, Dutch) and modality (written, spoken), we found that participants who referred to a more varied and balanced set of contexts and activities reported more differentiated and nuanced negative emotions. Experiential diversity was not consistently associated with granularity for positive emotions. We discuss the contents of daily life as a potential source and outcome of individual differences in emotion. Supplementary Information: The online version contains supplementary material available at 10.1007/s42761-023-00185-2.

You Do Not Have to Get through This Alone: Interpersonal Emotion Regulation and Psychosocial Resources during the COVID-19 Pandemic across Four Countries.

While experiencing the unpredictable events of the COVID-19 pandemic, we are likely to turn to people in order to regulate our emotions. In this research, we investigate how this interpersonal emotion regulation is connected to affective symptoms, above and beyond intrapersonal emotion regulation. Furthermore, we explore whether perceived psychosocial resources moderate these associations, i.e., if individuals reporting healthier social connections benefit differently from interpersonal emotion regulation. N = 1401 participants from the USA, UK, Germany, and Switzerland completed an online survey that included text samples. Affective symptoms (depression, adjustment disorder, fear of COVID-19) were examined based on self-reported as well as language-based indicators. As psychosocial resources, we examined social support, loneliness, attachment style, and trust. We defined latent variables for adaptive and maladaptive interpersonal emotion regulation and analyzed how they were associated with affective symptoms controlling for intrapersonal emotion regulation. Further, we analyzed how they interacted with psychosocial resources. Maladaptive interpersonal emotion regulation strategies were associated with affective symptoms. With lower psychosocial resources, the associations between interpersonal emotion regulation and depressive symptoms were more pronounced. The results highlight that maladaptive interpersonal emotion regulation is associated with worse mental health. These effects are not buffered by more psychosocial resources and are stronger for people with low psychosocial resources.

'Against the cult of veganism': Unpacking the social psychology and ideology of anti-vegans.

Despite the established health and ecological benefits of a plant-based diet, the decision to eschew meat and other animal-derived food products remains controversial. So polarising is this topic that anti-vegan communities - groups of individuals who stand vehemently against veganism - have sprung up across the internet. Much scholarship on veganism characterizes anti-vegans in passing, painting them as ill-informed, uneducated, or simply obstinate. However, little empirical work has investigated these communities and the individuals within them. Accordingly, we conducted a study using social media data from the popular platform, Reddit. Specifically, we collected all available submissions (∼3523) and comments (∼45,528) from r/AntiVegan subreddit users (N = 3819) over a five-year period. Using a battery of computerized text analytic tools, we examined the psychosocial characteristics of Reddit users who publicly identify as anti-vegan, how r/AntiVegan users discuss their beliefs, and how the individual user changes as a function of community membership. Results from our analyses suggest several individual differences that align r/AntiVegan users with the community, including dark entertainment, ex-veganism and science denial. Several topics were extensively discussed by r/AntiVegan members, including nuanced discourse on the ethicality and health implications of vegan diets, and the naturalness of animal death, which ran counter to our expectations and lay stereotypes of r/AntiVegan users. Finally, several longitudinal changes in language use were observed within the community, reflecting enhanced group commitment over time, including an increase in group-focused language and a decrease in cognitive processing. Implications for vegan-nonvegan relations are discussed.

Online Discussions About Tinnitus: What Can We Learn From Natural Language Processing of Reddit Posts?

BACKGROUND: This study was aimed at identifying key topics in online discussions about tinnitus by examining a large data set extracted from Reddit social media using a natural language processing technique. METHOD: A corpus of 113,215 posts about tinnitus was extracted from Reddit's application programming interface. After cleaning the data for duplications and posts without any text information, the sample was reduced to 101,905 posts, which was subjected to cluster analysis using the open-source IRaMuTeQ software to identify main topics based on the co-occurrence of texts. These clusters were named by a panel of tinnitus experts (n = 9) by reading typical text segments within each cluster. RESULTS: The cluster analysis identified 16 unique clusters that belong to two topics, which were named "tinnitus causes and consequences" and "tinnitus management and coping." Based on their characteristics, the clusters were named: tinnitus timeline (10%), tinnitus perception (9.7%), medical triggers and modulators (8.8%), hearing research (8.8%), attention and silence (8.6%), social media posts about tinnitus (7.4%), hearing protection (7.3%), interaction with hearing health care providers (6.7%), mental health and coping (5.8%), music listening (5.7%), hope for a cure (5.6%), interactions with people without tinnitus (5.4%), dietary supplements and alternative therapies (3.2%), sleep (3.9%), dietary effects (1.7%), and writing about tinnitus and being thankful to online community (1.4%). CONCLUSIONS: Despite some limitations, tinnitus posts on Reddit provide rich real-world data to identify various issues and complaints that tinnitus patients and their significant others discuss in online communities. Some of the clusters identified here are novel (e.g., tinnitus timeline, interactions with people without tinnitus) and have not been much discussed in the tinnitus literature. The results suggest that individuals with tinnitus relay on social media for support and highlight the service delivery needs in providing social support through other means (e.g., support groups).

Social Media Discussions Predict Mental Health Consultations on College Campuses.

The mental health of college students is a growing concern, and gauging the mental health needs of college students is difficult to assess in real-time and in scale. To address this gap, researchers and practitioners have encouraged the use of passive technologies. Social media is one such "passive sensor" that has shown potential as a viable "passive sensor" of mental health. However, the construct validity and in-practice reliability of computational assessments of mental health constructs with social media data remain largely unexplored. Towards this goal, we study how assessing the mental health of college students using social media data correspond with ground-truth data of on-campus mental health consultations. For a large U.S. public university, we obtained ground-truth data of on-campus mental health consultations between 2011-2016, and collected 66,000 posts from the university's Reddit community. We adopted machine learning and natural language methodologies to measure symptomatic mental health expressions of depression, anxiety, stress, suicidal ideation, and psychosis on the social media data. Seasonal auto-regressive integrated moving average (SARIMA) models of forecasting on-campus mental health consultations showed that incorporating social media data led to predictions with r = 0.86 and SMAPE = 13.30, outperforming models without social media data by 41%. Our language analyses revealed that social media discussions during high mental health consultations months consisted of discussions on academics and career, whereas months of low mental health consultations saliently show expressions of positive affect, collective identity, and socialization. This study reveals that social media data can improve our understanding of college students' mental health, particularly their mental health treatment needs.

An inclusive, real-world investigation of persuasion in language and verbal behavior.

Linguistic features of a message necessarily shape its persuasive appeal. However, studies have largely examined the effect of linguistic features on persuasion in isolation and do not incorporate properties of language that are often involved in real-world persuasion. As such, little is known about the key verbal dimensions of persuasion or the relative impact of linguistic features on a message's persuasive appeal in real-world social interactions. We collected large-scale data of online social interactions from a social media website in which users engage in debates in an attempt to change each other's views on any topic. Messages that successfully changed a user's views are explicitly marked by the user themselves. We simultaneously examined linguistic features that have been previously linked with message persuasiveness between persuasive and non-persuasive messages. Linguistic features that drive persuasion fell along three central dimensions: structural complexity, negative emotionality, and positive emotionality. Word count, lexical diversity, reading difficulty, analytical language, and self-references emerged as most essential to a message's persuasive appeal: messages that were longer, more analytic, less anecdotal, more difficult to read, and less lexically varied had significantly greater odds of being persuasive. These results provide a more parsimonious understanding of the social psychological pathways to persuasion as it operates in the real world through verbal behavior. Our results inform theories that address the role of language in persuasion, and provide insight into effective persuasion in digital environments.

Use of open-ended questionnaires to examine the effects of tinnitus and its relation to patient-reported outcome measures.

OBJECTIVE: The primary aim of the study was to examine the automated linguistic analysis of the open-ended problem (PQ) and life-effects (LEQ) questionnaires to understand the psychological effects of tinnitus. DESIGN: The study used a cross-sectional design. Participants completed online questionnaires which included demographic questions, several standardised patient-reported outcome measures (PROMs), and two open-ended questions focussing on PQ and LEQ related to tinnitus. The response to open-ended questions was analysed using the Linguistic Inquiry Word Count (LIWC) software to identify the frequency of text on various linguistic dimensions relevant to tinnitus. STUDY SAMPLE: 336 individuals with tinnitus. RESULTS: The study results point to two broad findings. First, although PQ and LEQ have some similarities with PROMs (e.g. the linguistic dimension negative emotions having a weak positive correlation with anxiety and depression), no correlation with the number of dimensions suggests that the open-ended questions identify additional elements that are not captured in PROMs. Second, more linguistic dimensions from the PQ correlate with PROMs compared to LEQ suggesting that the current PROMs are problem-oriented. CONCLUSIONS: The study results support the idea that the use of open-ended questions in addition to PROMs may help optimise the efforts in examining the effects of chronic conditions such as tinnitus.

Emotion regulation across the lifespan: age differences in intrapersonal and interpersonal strategies for the adjustment to the COVID-19 pandemic in four countries.

OBJECTIVES: Studies have shown age differences in adapting to the COVID-19 pandemic. The processes explaining these age differences remain unclear. Intrapersonal and interpersonal emotion regulation play an important role in psycho-social adjustment and develop across the lifespan. This study investigated whether differences in COVID-19-related adjustment disorder symptoms can be explained by age-differences in rumination in a multi-national sample. Furthermore, linguistic indicators of ruminative processing were examined with reference to age. METHODS: N = 1401 participants (from USA, UK, Switzerland, Germany, aged 18-88) completed an online survey and a writing task. Measures included brooding, co-brooding, adjustment disorder symptoms, and language indicators of negative self- and communal-focus . RESULTS: Older participants reported less adjustment disorder symptoms which was mediated by less (co-)brooding. Participants reporting more (co-)brooding wrote about COVID-19 more negatively. While in younger adults more self-focus was associated with higher ruminative brooding, in older adults it was associated with less brooding. CONCLUSION: These findings contribute to a better understanding of regulatory mechanisms that help explain age differences in mental health. They warrant further research considering age-related differences, as our results suggest not only more adaptive emotion regulation as resilience factor in older individuals but also different qualities of self-focus while processing stressful events.

Dirty laundry: The nature and substance of seeking relationship help from strangers online.

Interpersonal relationships are vital to our well-being. In recent years, it has become increasingly common to seek relationship help through anonymous online platforms. Accordingly, we conducted a large-scale analysis of real-world relationship help-seeking to create a descriptive overview of the nature and substance of online relationship help-seeking. By analyzing the demographic characteristics and language of relationship help-seekers on Reddit (N = 184,631), we establish the first-ever big data analysis of relationship help-seeking and relationship problems in situ among the general population. Our analyses highlight real-world relationship struggles found in the general population, extending beyond past work that is typically limited to counseling/intervention settings. We find that relationship problem estimates from our sample are closer to those found in the general population, providing a more generalized insight into the distribution and prevalence of relationship problems as compared with past work. Further, we find several meaningful associations between relationship help-seeking behavior, gender, and attachment. Notably, numerous gender differences in help-seeking and romantic attachment emerged. Our findings suggest that, contrary to more traditional contexts, men are more likely to seek help with their relationships online, are more expressive of their emotions (e.g., discussing the topic of "heartache"), and show language patterns generally consistent with more secure attachment. Our analyses highlight pathways for further exploration, providing even deeper insights into the timing, lifecycle, and moderating factors that influence who, what, why, and how people seek help for their interpersonal relationships.

Natural Language Analysis and the Psychology of Verbal Behavior: The Past, Present, and Future States of the Field.

Throughout history, scholars and laypeople alike have believed that our words contain subtle clues about what we are like as people, psychologically speaking. However, the ways in which language has been used to infer psychological processes has seen dramatic shifts over time and, with modern computational technologies and digital data sources, we are on the verge of a massive revolution in language analysis research. In this article, we discuss the past and current states of research at the intersection of language analysis and psychology, summarizing the central successes and shortcomings of psychological text analysis to date. We additionally outline and discuss a critical need for language analysis practitioners in the social sciences to expand their view of verbal behavior. Lastly, we discuss the trajectory of interdisciplinary research on language and the challenges of integrating analysis methods across paradigms, recommending promising future directions for the field along the way.

Applied Behavior Analysis as Treatment for Autism Spectrum Disorders: Topic Modeling and Linguistic Analysis of Reddit Posts.

Background: It is critical for professionals to understand the discourse landscape within various online and social media outlets in order to support families of children with autism in treatment decision-making. This need is heightened when considering treatments that have garnered excitement and controversy, such as applied behavioral analysis (ABA) therapy. Method: The specific aims of this study were to identify the main themes in Reddit posts about ABA-based interventions for autism using topic modeling, to examine the linguistic aspects of Reddit conversations using the Linguistic Inquiry and Word Count (LIWC) analysis, and to examine the relationship between linguistic aspects and user category (i.e., pro- vs. anti-ABA vs. undecided, parent vs. professional vs. an individual with autism). Results: The topic modeling resulted in 11 themes that ranged across various elements, such as autism as a condition and its management, stakeholders, and consequences of autism and the support needed. The posts of individuals were focused on personal experiences and opinions as opposed to clinical and research information sharing. Linguistic analysis indicated that the posts reveal an intimate stance rather than an empirical stance. Conclusions: Results provide insight into perspectives of ABA. This type of research may help in developing and distributing appropriate and evidence-based information.

Stereotyping in the digital age: Male language is "ingenious", female language is "beautiful" - and popular.

The huge power for social influence of digital media may come with the risk of intensifying common societal biases, such as gender and age stereotypes. Speaker's gender and age also behaviorally manifest in language use, and language may be a powerful tool to shape impact. The present study took the example of TED, a highly successful knowledge dissemination platform, to study online influence. Our goal was to investigate how gender- and age-linked language styles-beyond chronological age and identified gender-link to talk impact and whether this reflects gender and age stereotypes. In a pre-registered study, we collected transcripts of TED Talks along with their impact measures, i.e., views and ratios of positive and negative talk ratings, from the TED website. We scored TED Speakers' (N = 1,095) language with gender- and age-morphed language metrics to obtain measures of female versus male, and younger versus more senior language styles. Contrary to our expectations and to the literature on gender stereotypes, more female language was linked to higher impact in terms of quantity, i.e., more talk views, and this was particularly the case among talks with a lot of views. Regarding quality of impact, language signatures of gender and age predicted different types of positive and negative ratings above and beyond main effects of speaker's gender and age. The differences in ratings seem to reflect common stereotype contents of warmth (e.g., "beautiful" for female, "courageous" for female and senior language) versus competence (e.g., "ingenious", "informative" for male language). The results shed light on how verbal behavior may contribute to stereotypical evaluations. They also illuminate how, within new digital social contexts, female language might be uniquely rewarded and, thereby, an underappreciated but highly effective tool for social influence. WC = 286 (max. 300 words).