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Preotiuc-Pietro, Daniel, Maarten Sap, Andrew H. Schwartz, and Lyle Ungar. Mental Illness Detection at the World Well-Being Project for the CLPsych 2015 Shared Task In Workshop on Computational Linguistics and Clinical Psychology: From Linguistic Signal to Clinical Reality (CLPysch). NAACL, 2015. AbstractPDF

This article is a system description and report on the submission of the World Well-Being Project from the University of Pennsylvania in the `CLPsych 2015' shared task. The goal of the shared task was to automatically determine Twitter users who self-reported having one of two mental illnesses: post traumatic stress disorder (PTSD) and depression. Our system employs user metadata and textual features derived from Twitter posts. To reduce the feature space and avoid data sparsity, we consider several word clustering approaches. We explore the use of linear classifiers based on different feature sets as well as a combination use a linear ensemble. This method is agnostic of illness specific features, such as lists of medicines, thus making it readily applicable in other scenarios. Our approach ranked second in all tasks on average precision and showed best results at .1 false positive rates.

Preoţiuc-Pietro, Daniel, and Trevor Cohn. Mining user behaviours: A study of check-in patterns in Location Based Social Networks. WebSci., 2013. AbstractPDFPoster

Understanding the patterns underlying human mobility is of an essential importance to applications like recommender systems. In this paper we investigate the behaviour of around 10,000 frequent users of Location Based Social Networks (LBSNs) making use of their full movement patterns. We analyse the metadata associated with the whereabouts of the users, with emphasis on the type of places and their evolution over time. We uncover patterns across different temporal scales for venue category usage. Then, focusing on individual users, we apply this knowledge in two tasks: 1) clustering users based on their behaviour and 2) predicting users’ future movements. By this, we demonstrate both qualitatively and quantitatively that incorporating temporal regularities is beneficial for making better sense of user behaviour.

Preoţiuc-Pietro, Daniel, Andrew H. Schwartz, Gregory Park, Johannes Eichstaedt, Margaret Kern, Lyle Ungar, and Elisabeth Shulman. Modelling Valence and Arousal in Facebook posts In Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis (WASSA). NAACL, 2016. AbstractPDFSlides

Access to expressions of subjective personal posts increased with the popularity of Social Media. However, most of the work in sentiment analysis focuses on predicting only valence from text and usually targeted at a product, rather than affective states. In this paper, we introduce a new data set of 2895 Social Media posts rated by two psychologically-trained annotators on two separate ordinal nine-point scales. These scales represent valence (or sentiment) and arousal (or intensity), which defines each post’s position on the circumplex model of affect, a well-established system for describing emotional states (Russell, 1980; Posner et al., 2005). The data set is used to train prediction models for each of the two dimensions from text which achieve high predictive accuracy – correlated at r = :65 with valence and r = :85 with arousal annotations. Our data set offers a building block to a deeper study of personal affect as expressed in social media. This can be used in applications such as mental illness detection or in automated large-scale psychological studies.