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

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

Using Linguistic Features to Estimate Suicide Probability of Chinese Microblog Users

If people with high risk of suicide can be identified through social media like microblog, it is possible to implement an active intervention system to save their lives. Based on this motivation, the current study administered the Suicide Probability Scale(SPS) to 1041 weibo users at Sina Weibo, which is a leading microblog service provider in China. Two NLP (Natural Language Processing) methods, the Chinese edition of Linguistic Inquiry and Word Count (LIWC) lexicon and Latent Dirichlet Allocation (LDA), are used to extract linguistic features from the Sina Weibo data. We trained predicting models by machine learning algorithm based on these two types of features, to estimate suicide probability based on linguistic features. The experiment results indicate that LDA can find topics that relate to suicide probability, and improve the performance of prediction. Our study adds value in prediction of suicidal probability of social network users with their behaviors

Sensing Subjective Well-being from Social Media

Subjective Well-being(SWB), which refers to how people experience the quality of their lives, is of great use to public policy-makers as well as economic, sociological research, etc. Traditionally, the measurement of SWB relies on time-consuming and costly self-report questionnaires. Nowadays, people are motivated to share their experiences and feelings on social media, so we propose to sense SWB from the vast user generated data on social media. By utilizing 1785 users' social media data with SWB labels, we train machine learning models that are able to "sense" individual SWB from users' social media. Our model, which attains the state-by-art prediction accuracy, can then be used to identify SWB of large population of social media users in time with very low cost.Comment: 12 pages, 1 figures, 2 tables, 10th International Conference, AMT 2014, Warsaw, Poland, August 11-14, 2014. Proceeding

''Remembering'' World War II and willingness to fight : sociocultural factors in the social representation of historical warfare across 22 societies

Students from 22 nations answered a survey on the most important events in world history. At the national level, free recalling and a positive evaluation of World War II (WWII) were associated with World Values Survey willingness to fight for the country in a war and being a victorious nation. Willingness to fight, a more benign evaluation of WWII, and recall of WWII were associ- ated with nation-level scores on power distance and low postmaterialism, suggesting that values stressing obedience and competition between nations are associated with support for collective violence, whereas values of expressive individualism are negatively related. Internal political vio- lence was unrelated to willingness to fight, excluding direct learning as an explanation of legit- imization of violence. Recall of wars in general (operationalized by WWI recall) was also unrelated to willingness to fight. Results replicate and extend Archer and Gartner’s classic study showing the legitimization of violence by war to the domain of collective remembering

Semantic Sentiment Analysis of Twitter Data

Internet and the proliferation of smart mobile devices have changed the way information is created, shared, and spreads, e.g., microblogs such as Twitter, weblogs such as LiveJournal, social networks such as Facebook, and instant messengers such as Skype and WhatsApp are now commonly used to share thoughts and opinions about anything in the surrounding world. This has resulted in the proliferation of social media content, thus creating new opportunities to study public opinion at a scale that was never possible before. Naturally, this abundance of data has quickly attracted business and research interest from various fields including marketing, political science, and social studies, among many others, which are interested in questions like these: Do people like the new Apple Watch? Do Americans support ObamaCare? How do Scottish feel about the Brexit? Answering these questions requires studying the sentiment of opinions people express in social media, which has given rise to the fast growth of the field of sentiment analysis in social media, with Twitter being especially popular for research due to its scale, representativeness, variety of topics discussed, as well as ease of public access to its messages. Here we present an overview of work on sentiment analysis on Twitter.Comment: Microblog sentiment analysis; Twitter opinion mining; In the Encyclopedia on Social Network Analysis and Mining (ESNAM), Second edition. 201

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

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

The effects of poetry-writing SANTEL on erotic body image in remission of cancer in women: a pilot study

International audienceAbstract Aim: Our pilot study aims to describe the effects ofa new specific and structured protocol focused on poetic/erotic writing (named SANTEL) on the (re)sexualization ofbody image in women, who have experienced cancer.Procedure: The protocol consists of four steps: to choose alist of erotic verses focused on the body parts, to fill a semistructuredpoetic text, to write sentences after target phraseson the body; and in the end, to write a free poem. Mrs V.suffered from breast cancer, and one breast was removed.She and her husband participated in this poetic writing protocol,separately. We analyzed the linguistic metaphors ofthe body by QSR Nvivo10 software.Results: Using this protocol, we showed discourse variationsof metaphors before and after the experience of writing.Patient V used “I feel like an alien” as a starting metaphorto describe her cancer experience and after poetic writingsessions, she used other bodily metaphors like “My body isa flower” and “My sensual and white flesh”.Conclusion: This poetic perspective promises a type of“perceptive-literary surgery”, characterized by a sensualinvestment process after remission: a poetic reconstructionof erotic body image.Les effets d'un protocole d'écriture poétique SANTEL sur l'image érotique du corps dans le traitement du cancer féminin : étude pilote The effects of poetry-writing SANTEL on erotic body image in remission of cancer in women: a pilot study A. Santarpia · J. Tellène · M. Carrier Résumé Objectif : Cette étude pilote de type qualitative et exploratoire vise à décrire les effets d'un nouveau protocole d'écriture poético-érotique (nommée SANTEL) sur la rééro-tisation de l'image du corps chez une femme, ayant vécu un cancer. Matériel et méthodes : Il s'agit d'un protocole composé de quatre étapes : une liste des phrases à caractères poétiques et érotiques à choisir, un texte à trous à remplir, des amorces de phrases ciblées sur le corps et en fin un poème libre. Madame V. a subi un cancer du sein nécessitant une ablation complète. Madame V. et son conjoint exécutent le protocole d'écriture séparément. Nous montrons les variations discursives des métaphores utilisées avant et après l'expérience de l'écriture, à travers le logiciel d'analyse qualitative QSR NVivo10. Résultats : Madame V. passera de la métaphore initiale « je me sens une extraterrestre » vers la plus atténuée « Non. Je me dis qu'extraterrestre c'était peut-être un peu énorme ». En plus, elle utilisera de nouvelles métaphores linguistiques du corps pour raconter son image du corps telles que « ce corps de chair blanche » et « une fleur qui s'ouvre délicatement ». Conclusion : Cet exercice spécifique d'écriture promet un type de « chirurgie perceptive-littéraire » dans le processus d'investissement sensuel et affectif après la rémission, une reconstruction perceptive et poétique de l'image érotique du corps. Mots clés Métaphores perceptives · Image du corps · Cancer féminin · Corps érotique · Écriture poétique · Chirurgie perceptive-littéraire · Logiciel QSR NVivo10. Abstract Aim: Our pilot study aims to describe the effects of a new specific and structured protocol focused on poetic/ erotic writing (named SANTEL) on the (re)sexualization of body image in women, who have experienced cancer. Procedure: The protocol consists of four steps: to choose a list of erotic verses focused on the body parts, to fill a semi-structured poetic text, to write sentences after target phrases on the body; and in the end, to write a free poem. Mrs V. suffered from breast cancer, and one breast was removed. She and her husband participated in this poetic writing protocol , separately. We analyzed the linguistic metaphors of the body by QSR Nvivo10 software. Results: Using this protocol, we showed discourse variations of metaphors before and after the experience of writing. Patient V used " I feel like an alien " as a starting metaphor to describe her cancer experience and after poetic writing sessions, she used other bodily metaphors like " My body is a flower " and " My sensual and white flesh ". Conclusion: This poetic perspective promises a type of " perceptive-literary surgery " , characterized by a sensual investment process after remission: a poetic reconstruction of erotic body image. Keywords Bodily metaphors · Body image · Feminine cancer · Erotic body · Poetry writing · Perceptive-literary surgery · QSR Nvivo10 software

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