A Text Classification Framework for Simple and Effective Early Depression Detection Over Social Media Streams
arxiv(2019)
摘要
With the rise of the Internet, there is a growing need to build intelligent
systems that are capable of efficiently dealing with early risk detection (ERD)
problems on social media, such as early depression detection, early rumor
detection or identification of sexual predators. These systems, nowadays mostly
based on machine learning techniques, must be able to deal with data streams
since users provide their data over time. In addition, these systems must be
able to decide when the processed data is sufficient to actually classify
users. Moreover, since ERD tasks involve risky decisions by which people's
lives could be affected, such systems must also be able to justify their
decisions. However, most standard and state-of-the-art supervised machine
learning models are not well suited to deal with this scenario. This is due to
the fact that they either act as black boxes or do not support incremental
classification/learning. In this paper we introduce SS3, a novel supervised
learning model for text classification that naturally supports these aspects.
SS3 was designed to be used as a general framework to deal with ERD problems.
We evaluated our model on the CLEF's eRisk2017 pilot task on early depression
detection. Most of the 30 contributions submitted to this competition used
state-of-the-art methods. Experimental results show that our classifier was
able to outperform these models and standard classifiers, despite being less
computationally expensive and having the ability to explain its rationale.
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