  Mental well-being and social media have been closely related domains of
study. In this research a novel model, AD prediction model, for anxious
depression prediction in real-time tweets is proposed. This mixed
anxiety-depressive disorder is a predominantly associated with erratic thought
process, restlessness and sleeplessness. Based on the linguistic cues and user
posting patterns, the feature set is defined using a 5-tuple vector <word,
timing, frequency, sentiment, contrast>. An anxiety-related lexicon is built to
detect the presence of anxiety indicators. Time and frequency of tweet is
analyzed for irregularities and opinion polarity analytics is done to find
inconsistencies in posting behaviour. The model is trained using three
classifiers (multinomial na\"ive bayes, gradient boosting, and random forest)
and majority voting using an ensemble voting classifier is done. Preliminary
results are evaluated for tweets of sampled 100 users and the proposed model
achieves a classification accuracy of 85.09%.
