User-level prediction

Preoţiuc-Pietro, Daniel, Vasileios Lampos, and Nikolaos Aletras. An analysis of the user occupational class through Twitter content In ACL., 2015. AbstractPDFSlides

Social media content can be used as a complementary source to the traditional methods for extracting and studying collective social attributes. This study focuses on the prediction of the occupational class for a public user profile. Our analysis is conducted on a new annotated corpus of Twitter users, their respective job titles, posted textual content and platform-related attributes. We frame our task as classification using latent feature representations such as word clusters and embeddings. The employed linear and, especially, non-linear methods can predict a user’s occupational class with strong accuracy for the coarsest level of a standard occupation taxonomy which includes nine classes. Combined with a qualitative assessment, the derived results confirm the feasibility of our approach in inferring a new user attribute that can be embedded in a multitude of downstream applications.

Preotiuc-Pietro, Daniel, Johannes Eichstaedt, Gregory Park, Maarten Sap, Laura Smith, Victoria Tobolsky, Andrew H. Schwartz, and Lyle Ungar. The Role of Personality, Age and Gender in Tweeting about Mental Illnesses In Workshop on Computational Linguistics and Clinical Psychology: From Linguistic Signal to Clinical Reality (CLPsych). NAACL, 2015. AbstractPDFSlides

Mental illnesses, such as depression and post traumatic stress disorder (PTSD), are highly underdiagnosed globally. Populations sharing similar demographics and personality traits are known to be more at risk than others. In this study, we characterise the language use of users disclosing their mental illness on Twitter. Language-derived personality and demographic estimates show surprisingly strong performance in distinguishing users that tweet a diagnosis of depression or PTSD from random controls, reaching an area under the receiver operating characteristic curve – AUC – of around .8 in all our binary classification tasks. In fact, when distinguishing users disclosing depression from those disclosing PTSD, the single feature of estimated age shows nearly as strong performance (AUC = .806) as using thousands of topics (AUC = .819) or tens of thousands of n-grams (AUC = .812). We also find that differential language analyses, controlled for demographics, recover many symptoms associated with the mental illnesses in the clinical literature.

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.

Lampos, Vasileios, Nikolaos Aletras, Daniel Preoţiuc-Pietro, and Trevor Cohn. Predicting and characterising user impact on Twitter. EACL., 2014. AbstractPDFPoster

The open structure of online social networks and their uncurated nature give rise to problems of user credibility and influence. In this paper, we address the task of predicting the impact of Twitter users based only on features under their direct control, such as usage statistics and the text posted in their tweets.We approach the problem as regression and apply linear as well as nonlinear learning methods to predict a user impact score, estimated by combining the numbers of the user’s followers, followees and listings. The experimental results point out that a strong prediction performance is achieved, especially for models based on the Gaussian Processes framework. Hence, we can interpret various modelling components, transforming them into indirect ‘suggestions’ for impact boosting.

Rout, Dominic, Daniel Preoţiuc-Pietro, Bontcheva Kalina, and Trevor Cohn. Where's @wally: A classification approach to geolocating users based on their social ties. HT., 2013. AbstractPDF

This paper presents an approach to geolocating users of online social networks, based solely on their ‘friendship’ connections. We observe that users interact more regularly with those closer to themselves and hypothesise that, in many cases, a person’s social network is sufficient to reveal their location. The geolocation problem is formulated as a classification task, where the most likely city for a user without an explicit location is chosen amongst the known locations of their social ties. Our method uses an SVM classifier and a number of features that reflect different aspects and characteristics of Twitter user networks. The SVM classifier is trained and evaluated on a dataset of Twitter users with known locations. Our method outperforms a state-of-the-art method for geolocating users based on their social ties