Depression and anorexia detection in social media as a one-class classification problem

摘要

Taking advantage of the increasing amount of user-generated content in social media, some computational methods have already been proposed for detecting people suffering from depression and anorexia. Such complex tasks have been tackled as a binary classification problem using, in most cases, automatically generated training data. Despite its promising results, this approach has some important drawbacks, namely: it suffers from a severely skewed class distribution, the negative class is very diverse since it attempts to model all kinds of healthy users, and, above all, there is not a complete certainty about annotations, especially for the negative cases (i.e., healthy users). Motivated by these issues, in this paper, we propose to face the detection of these disorders following a one-class classification (OCC) approach. Particularly, we introduce two new instance-based OCC methods especially suited to manage the high diversity of content from social media documents. Taking up ideas from the gravitational attraction force, these methods evaluate the relation of documents by their strengths, considering their distances as well as their masses (relevance) with respect to the target task. Experiments were conducted on depression and anorexia benchmark datasets. The obtained results are encouraging; the overall performance was better than the results from other standard OCC methods, and competitive with regard to state-of-the-art results from binary classification approaches.