CAN LINGUISTIC ANALYSIS BE USED TO IDENTIFY WHETHER ADOLESCENTS WITH A CHRONIC ILLNESS ARE DEPRESSED?

Comorbid depression is common in adolescents with chronic illness. We aimed to design and test a linguistic coding scheme for identifying depression in adolescents with chronic fatigue syndrome/myalgic encephalomyelitis (CFS/ME), by exploring features of e-consultations within online cognitive behavioural therapy treatment. E-consultations of 16 adolescents (aged 11–17) receiving FITNET-NHS (Fatigue in teenagers on the interNET in the National Health Service) treatment in a national randomized controlled trial were examined. A theoretically driven linguistic coding scheme was developed and used to categorize comorbid depression in e-consultations using computerized content analysis. Linguistic coding scheme categorization was subsequently compared with classification of depression using the Revised Children's Anxiety and Depression Scale published cut-offs (t-scores ≥65, ≥70). Extra linguistic elements identified deductively and inductively were compared with self-reported depressive symptoms after unblinding. The linguistic coding scheme categorized three (19%) of our sample consistently with self-report assessment. Of all 12 identified linguistic features, differences in language use by categorization of self-report assessment were found for “past focus” words (mean rank frequencies: 1.50 for no depression, 5.50 for possible depression, and 10.70 for probable depression; p &lt;.05) and “discrepancy” words (mean rank frequencies: 16.00 for no depression, 11.20 for possible depression, and 6.40 for probable depression; p &lt;.05). The linguistic coding profile developed as a potential tool to support clinicians in identifying comorbid depression in e-consultations showed poor value in this sample of adolescents with CFS/ME. Some promising linguistic features were identified, warranting further research with larger samples.</p

The impact of an emotionally expressive writing intervention on eating pathology in female students

© 2018 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group.Introduction: Previous research demonstrating emotional influences on eating and weight suggest that emotionally expressive writing may have a significant impact on reducing risk of eating pathology. This study examined the effects of writing about Intensely Positive Experiences on weight and disordered eating during a naturalistic stressor. Method: Seventy-one female students completed an expressive or a control writing task before a period of exams. Both groups were compared on BMI (kg/m2) and the Eating Disorder Examination – Questionnaire (EDE-Q) before the writing task and at 8-week follow-up. A number of secondary analyses were also examined (to identify potential mediators) including measures of attachment, social rank, self-criticism and self-reassurance, stress and mood. Results: There was a significant effect of intervention on changes in the subscales of the EDE-Q (p = .03). Specifically, expressive writers significantly reduced their dietary restraint while those in the control group did not. There was no significant effect of the intervention on changes in BMI or the other subscales of the EDE-Q (Eating, Weight and Shape Concern). There was also no effect of writing on any of the potential mediators in the secondary analyses. Discussion: Emotionally expressive writing may reduce the risk of dietary restraint in women but these findings should be accepted with caution. It is a simple and light touch intervention that has the potential to be widely applied. However, it remains for future research to replicate these results and to identify the mechanisms of action.Peer reviewedFinal Published versio

Breaking down the language of racism:a computerised linguistic analysis of racist groups’ self-defining online statements

The Internet represents a powerful tool for racist groups to build a sense of group consciousness and promote their cause. In the current study, we examined the language used by racist (n = 87), anti-racist (n = 50), and nonactivist (n = 1379) groups when describing their self-defining beliefs online. We used computerized linguistic analysis software to measure psychological indicators and antecedents of group consciousness and to examine the persuasive techniques used in online group communication. Racist and anti-racist groups were similar on some linguistic indicators of group consciousness (e.g., use of words reflecting perceived injustice), but differed on others (e.g., use of words reflecting group identification). Linguistic indicators of antecedents of group consciousness (moral foundations and focus on religion) differed across groups, with racist groups focused more on purity, respect for authority, and religion, and less on fairness than anti-racist groups. Racist groups also used less cognitively complex language than nonactivist groups (but similar levels to anti-racist groups). Our results contribute to understanding how racist groups promote their self-defining beliefs online, and identify several key factors that should be considered when designing policies to reduce racist groups' growth and impact

Controlling for Confounders in Multimodal Emotion Classification via Adversarial Learning

Various psychological factors affect how individuals express emotions. Yet, when we collect data intended for use in building emotion recognition systems, we often try to do so by creating paradigms that are designed just with a focus on eliciting emotional behavior. Algorithms trained with these types of data are unlikely to function outside of controlled environments because our emotions naturally change as a function of these other factors. In this work, we study how the multimodal expressions of emotion change when an individual is under varying levels of stress. We hypothesize that stress produces modulations that can hide the true underlying emotions of individuals and that we can make emotion recognition algorithms more generalizable by controlling for variations in stress. To this end, we use adversarial networks to decorrelate stress modulations from emotion representations. We study how stress alters acoustic and lexical emotional predictions, paying special attention to how modulations due to stress affect the transferability of learned emotion recognition models across domains. Our results show that stress is indeed encoded in trained emotion classifiers and that this encoding varies across levels of emotions and across the lexical and acoustic modalities. Our results also show that emotion recognition models that control for stress during training have better generalizability when applied to new domains, compared to models that do not control for stress during training. We conclude that is is necessary to consider the effect of extraneous psychological factors when building and testing emotion recognition models.Comment: 10 pages, ICMI 201

The Language of the Roberts Court

Article published in the Michigan State Law Review

Language-based personality:a new approach to personality in a digital world

Personality is typically defined as the consistent set of traits, attitudes, emotions, and behaviors that people have. For several decades, a majority of researchers have tacitly agreed that the gold standard for measuring personality was with self-report questionnaires. Surveys are fast, inexpensive, and display beautiful psychometric properties. A considerable problem with this method, however, is that self-reports reflect only one aspect of personality — people's explicit theories of what they think they are like. We propose a complementary model that draws on a big data solution: the analysis of the words people use. Language use is relatively reliable over time, internally consistent, and differs considerably between people. Language-based measures of personality can be useful for capturing/modeling lower-level personality processes that are more closely associated with important objective behavioral outcomes than traditional personality measures. Additionally, the increasing availability of language data and advances in both statistical methods and technological power are rapidly creating new opportunities for the study of personality at ‘big data’ scale. Such opportunities allow researchers to not only better understand the fundamental nature of personality, but at a scale never before imagined in psychological research

In an absolute state: elevated use of absolutist words is a marker specific to anxiety, depression and suicidal ideation

Absolutist thinking is considered a cognitive distortion by most cognitive therapies for anxiety and depression. Yet, there is little empirical evidence of its prevalence or specificity. Across three studies, we conducted a text analysis of 63 internet forums (over 6,400 members) using the Linguistic- Inquiry and Word Count software (Pennebaker, Booth, Boyd, & Francis, 2015) to examine absolutism at the linguistic level. We predicted and found that anxiety, depression and suicidal ideation forums contained more absolutist words than control forums (d’s > 3.14). Suicidal ideation forums also contained more absolutist words than anxiety and depression forums (d’s > 1.71). We show that these differences are more reflective of absolutist thinking than psychological distress. Interestingly, absolutist words tracked the severity of affective disorder forums more faithfully than ‘negative emotion’ words. Finally, we found elevated levels of absolutist words in depression ‘recovery’ forums. This suggests that absolutist thinking may be a vulnerability factor

An Army of Me: Sockpuppets in Online Discussion Communities

In online discussion communities, users can interact and share information and opinions on a wide variety of topics. However, some users may create multiple identities, or sockpuppets, and engage in undesired behavior by deceiving others or manipulating discussions. In this work, we study sockpuppetry across nine discussion communities, and show that sockpuppets differ from ordinary users in terms of their posting behavior, linguistic traits, as well as social network structure. Sockpuppets tend to start fewer discussions, write shorter posts, use more personal pronouns such as "I", and have more clustered ego-networks. Further, pairs of sockpuppets controlled by the same individual are more likely to interact on the same discussion at the same time than pairs of ordinary users. Our analysis suggests a taxonomy of deceptive behavior in discussion communities. Pairs of sockpuppets can vary in their deceptiveness, i.e., whether they pretend to be different users, or their supportiveness, i.e., if they support arguments of other sockpuppets controlled by the same user. We apply these findings to a series of prediction tasks, notably, to identify whether a pair of accounts belongs to the same underlying user or not. Altogether, this work presents a data-driven view of deception in online discussion communities and paves the way towards the automatic detection of sockpuppets.Comment: 26th International World Wide Web conference 2017 (WWW 2017

Anyone Can Become a Troll: Causes of Trolling Behavior in Online Discussions

In online communities, antisocial behavior such as trolling disrupts constructive discussion. While prior work suggests that trolling behavior is confined to a vocal and antisocial minority, we demonstrate that ordinary people can engage in such behavior as well. We propose two primary trigger mechanisms: the individual's mood, and the surrounding context of a discussion (e.g., exposure to prior trolling behavior). Through an experiment simulating an online discussion, we find that both negative mood and seeing troll posts by others significantly increases the probability of a user trolling, and together double this probability. To support and extend these results, we study how these same mechanisms play out in the wild via a data-driven, longitudinal analysis of a large online news discussion community. This analysis reveals temporal mood effects, and explores long range patterns of repeated exposure to trolling. A predictive model of trolling behavior shows that mood and discussion context together can explain trolling behavior better than an individual's history of trolling. These results combine to suggest that ordinary people can, under the right circumstances, behave like trolls.Comment: Best Paper Award at CSCW 201

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