Automatic detection of depression symptoms in twitter using multimodal analysis
- 9 September 2021
- journal article
- research article
- Published by Springer Science and Business Media LLC in The Journal of Supercomputing
- Vol. 78 (4), 4709-4744
- https://doi.org/10.1007/s11227-021-04040-8
Abstract
Depression is the most prevalent mental disorder that can lead to suicide. Due to the tendency of people to share their thoughts on social platforms, social data contain valuable information that can be used to identify user’s psychological states. In this paper, we provide an automated approach to collect and evaluate tweets based on self-reported statements and present a novel multimodal framework to predict depression symptoms from user profiles. We used n-gram language models, LIWC dictionaries, automatic image tagging, and bag-of-visual-words. We consider the correlation-based feature selection and nine different classifiers with standard evaluation metrics to assess the effectiveness of the method. Based on the analysis, the tweets and bio-text alone showed 91% and 83% accuracy in predicting depressive symptoms, respectively, which seems to be an acceptable result. We also believe performance improvements can be achieved by limiting the user domain or presence of clinical information.Keywords
This publication has 60 references indexed in Scilit:
- #iamhappybecause: Gross National Happiness through Twitter analysis and big dataTechnological Forecasting and Social Change, 2015
- Identifying Chinese Microblog Users With High Suicide Probability Using Internet-Based Profile and Linguistic Features: Classification ModelJMIR Mental Health, 2015
- Recognizing Depression from Twitter ActivityPublished by Association for Computing Machinery (ACM) ,2015
- Affective and Content Analysis of Online Depression CommunitiesIEEE Transactions on Affective Computing, 2014
- Quantifying Mental Health Signals in TwitterPublished by Association for Computational Linguistics (ACL) ,2014
- CROWDSOURCING A WORD–EMOTION ASSOCIATION LEXICONComputational Intelligence, 2012
- Cancer statistics, 2012CA: A Cancer Journal for Clinicians, 2012
- Happiness Is Assortative in Online Social NetworksArtificial Life, 2011
- Sentiment strength detection in short informal textJournal of the American Society for Information Science and Technology, 2010
- Latent semantic analysisAnnual Review of Information Science and Technology, 2004