Social Media-based Substance Use Prediction
Table 3: SPE: Prediction Results
['Methods', 'Tobacco', 'Alcohol', 'Drug']
We evaluate the performance of our models using 10-fold cross validation. Among all the feature learning methods for Facebook status updates, User-D-DBOW performed the best. It significantly outperformed all the baseline systems that only rely on supervised training (p<0.01 based on t-tests). It also significantly outperformed all the traditional feature learning methods such as LDA and SVD (p<0.01 based on t-tests). Moreover, in terms of whether to treat all the posts by the same user as one big document or separate documents, LDA prefers one post one document (models with a “post” prefix) while all the document vector-based methods prefer one user one document (models with a “User” prefix). Moreover, to use post-level LDA to derive the SPE for a user, the document-based aggregation method (PostLDA_Doc) performed better than the word-based method (PostLDA_Word).