Anxious Depression Prediction in Real-time Social Data

7 Pages Posted: 7 May 2019

See all articles by Akshi Kumar

Akshi Kumar

Delhi Technological University

Aditi Sharma

Delhi Technological University

Anshika Arora

Delhi Technological University

Date Written: March 14, 2019

Abstract

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.

Keywords: Depression, Anxiety, Social media, Machine learning

Suggested Citation

Kumar, Akshi and Sharma, Aditi and Arora, Anshika, Anxious Depression Prediction in Real-time Social Data (March 14, 2019). Available at SSRN: https://ssrn.com/abstract=3383359 or http://dx.doi.org/10.2139/ssrn.3383359

Akshi Kumar (Contact Author)

Delhi Technological University ( email )

Shahbad Daulatpur,
Main Bawana Road,
Delhi, Delhi 110042
India

Aditi Sharma

Delhi Technological University ( email )

Shahbad Daulatpur,
Main Bawana Road,
Delhi, Delhi 110042
India

Anshika Arora

Delhi Technological University ( email )

Bawana Road
Delhi, 110042
India

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