Imagem do corpo, perturbações alimentares e traços de personalidade

Este estudo tem como objetivo investigar a relação entre a imagem do corpo, perturbações alimentares e traços de personalidade. 254 estudantes do ensino superior, 191 raparigas (Idade média = 21,53) e 63 rapazes (Idade média = 22,87) responderam a: Questionário sobre a Imagem do Corpo (Bruchon-Schweitzer, 1992), Eating Disorder lnventory (Garner, Olmstead & Polivy, 1983) e o NEO-FFI-20 (Bertoquini & Ribeiro, 2006). Os resultados obtidos revelaram uma correlação positiva entre a imagem corporal e o comportamento alimentar. As dimensões de personalidade Extroversão e Conscienciosidade estão correlacionadas positivamente com a imagem corporal, enquanto que a dimensão Abertura à Experiência correlaciona-se de modo negativo com a imagem corporal e com o comportamento alimentar. /ABSTRACT: This study aims to investigate the relationship between body image, eating disorders and personality traits. 254 higher education students, 191 girls (mean age = 21.53) and 63 boys (mean age = 22.87) answered: Questionnaire on Body lmage (Bruchon-Schweitzer, 1992), Eating Disorder lnventory (Garner, Olmstead & Polivy, 1983) and the NEO-FFI-20 (Bertoquini & Ribeiro, 2006). The results revealed a positive correlation between body image and eating behaviors. The personality dimensions Extraversion and Conscientiousness was positively correlated with body image, while the dimension Openness to Experience correlated negatively with body image and eating behavior.

Multilingual author profiling using svms and linguistic features

This paper describes various experiments done to investigate author profiling of tweets in 4 different languages – English, Dutch, Italian, and Spanish. Profiling consists of age and gender classification, as well as regression on 5 different person- ality dimensions – extroversion, stability, agreeableness, open- ness, and conscientiousness. Different sets of features were tested – bag-of-words, word ngrams, POS ngrams, and average of word embeddings. SVM was used as the classifier. Tfidf worked best for most English tasks while for most of the tasks from the other languages, the combination of the best features worked better.