Social Media based Substance Use Detection

Shit, they got me. (I think they follow me on Twitter, too. Damn. [Not really.]) Table 6 is hilarious:

In this paper, we demonstrate how the state-of-the-art machine learning and text mining techniques can be used to build effective social media-based substance use detection systems. Since a substance use ground truth is difficult to obtain on a large scale, to maximize system performance, we explore different feature learning methods to take advantage of a large amount of unsupervised social media data.

We also demonstrate the benefit of using multi-view unsupervised feature learning to combine heterogeneous user information such as Facebook “likes” and “status updates” to enhance system performance. Based on our evaluation, our best models achieved 86% AUC for predicting tobacco use, 81% for alcohol use and 84% for drug use, all of which significantly outperformed existing methods. Our investigation has also uncovered interesting relations between a user’s social media behavior (e.g., word usage) and substance use.

Paper: Social Media-based Substance Use Prediction
Netzpolitik: Mit Facebook-Daten vorhersagen, wer Alkohol oder Drogen nimmt
TechReview: How Data Mining Facebook Messages Can Reveal Substance Abusers

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