History of mental illness is a major factor behind suicide risk and ideation. However research efforts toward characterizing and forecasting this risk is limited due to the paucity of information regarding suicide ideation, exacerbated by the stigma of mental illness. This paper fills gaps in the literature by developing a statistical methodology to infer which individuals could undergo transitions from mental health discourse to suicidal ideation. We utilize semi-anonymous support communities on Reddit as unobtrusive data sources to infer the likelihood of these shifts. We develop language and interactional measures for this purpose, as well as a propensity score matching based statistical approach. Our approach allows us to derive distinct markers of shifts to suicidal ideation. These markers can be modeled in a prediction framework to identify individuals likely to engage in suicidal ideation in the future. We discuss societal and ethical implications of this research.

Existing models for social media per-sonal analytics assume access to thou-sands of messages per user, even though most users author content only sporadi-cally over time. Given this sparsity, we: (i) leverage content from the local neigh-borhood of a user; (ii) evaluate batch mod-els as a function of size and the amount of messages in various types of neighbor-hoods; and (iii) estimate the amount of time and tweets required for a dynamic model to predict user preferences. We show that even when limited or no self-authored data is available, language from friend, retweet and user mention commu-nications provide sufficient evidence for prediction. When updating models over time based on Twitter, we find that polit-ical preference can be often be predicted using roughly 100 tweets, depending on the context of user selection, where this could mean hours, or weeks, based on the author’s tweeting frequency. 1

In this paper we propose a discriminative feature extraction method, DFE, to address the increasing number of features in language identification (LID) tasks. Similar to linear discriminant analysis (LDA), it extracts the most discriminative features through the maximization of an “approximated” mutual information I(C; Y ) between the class labels C and the projected data Y. Compared with other feature extraction methods, experiments done on the CallFriend corpus shows DFE could handle high-dimensional dataset with ease. Furthermore, this feature extraction shows improvements on the LID task over standard feature extraction methods (LDA and principal components analysis).

In many inference and learning tasks, collecting large amounts of labeled training data is time consuming and expensive, and oftentimes impractical. Thus, being able to efficiently use small amounts of labeled data with an abundance of unlabeled data-the topic of semi-supervised learning (SSL) [1]-has garnered much attention. In this paper, we look at the problem of choosing these small amounts of labeled data, the first step in a bootstrapping paradigm. Contrary to traditional active learning where an initial trained model is employed to select the unlabeled data points which would be most informative if labeled, our selection has to be done in an unsupervised way, as we do not even have labeled data to train an initial model. We propose using unsupervised clustering algorithms, in particular integrated sensing and processing decision trees (ISPDTs) [2], to select small amounts of data to label and subsequently use in SSL (e.g. transductive SVMs). In a language identification task on the CallFriend1 and 2003 NIST Language Recognition Evaluation corpora [3], we demonstrate that the proposed method results in significantly improved performance over random selection of equivalently sized training data.