Social Media-based Substance Use Prediction
Table 4: SLE: Prediction Results
['Method', 'Tobacco', 'Alcohol', 'Drug']
It significantly outperformed the unigram baseline that does not use any unsupervised data (p<0.01 based on t-tests). It also significantly outperformed all the traditional feature learning method such as SVD and LDA (The Kosinski model used SVD for feature learning) (p<0.01 based on t-tests). Between the two document vector-based methods D-DM and D-DBOW, D-DBOW outperformed D-DM. We think this is due to the fact that D-DBOW does not use local context window, thus is not sensitive to the positions of LEs in a document. Since LE positions are randomly decided, D-DBOW seems to be a better fit for the like data.