Twitter analysis for depression on social networks based on sentiment and stress

Tao, Xiaohui ORCID: https://orcid.org/0000-0002-0020-077X and Dharmalingam, Ravi and Zhang, Ji and Zhou, Xujuan and Li, Lin and Gururajan, Raj (2019) Twitter analysis for depression on social networks based on sentiment and stress. In: 6th International Conference on Behavioral, Economic and Socio-Cultural Computing (BESC 2019), 28-30 Oct 2019, Beijing, China.

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Abstract

Detecting words that express negativity in a social media message is one step towards detecting depressive moods. To understand if a Twitter user could exhibit depression over a period of time, we applied techniques in stages to discover words that are negative in expression. Existing methods either use a single step or a data subset, whereas we applied a multi-step approach which allowed us to identify potential users and then discover the words that expressed negativity by these users. We address some Twitter specific characteristics in our research. One of which is that Twitter data can be very large, hence our desire to be able to process the data efficiently. The other is that due to its enforced character limitation, the style of writing makes interpreting and obtaining the semantic meaning of the words more challenging. Results show that the sentiment of these words can be obtained and scored efficiently as the computation on these dataset were narrowed to only these selected users. We also obtained the stress scores which correlated well with negative sentiment expressed in the content. This work shows that by first identifying users and then using methods to discover words can be a very effective technique.


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Item Type: Conference or Workshop Item (Commonwealth Reporting Category E) (Paper)
Refereed: Yes
Item Status: Live Archive
Additional Information: © 2019 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
Faculty/School / Institute/Centre: Current - Faculty of Health, Engineering and Sciences - School of Sciences (6 Sept 2019 -)
Faculty/School / Institute/Centre: Current - Faculty of Business, Education, Law and Arts - School of Management and Enterprise (1 July 2013 -)
Date Deposited: 11 Mar 2020 04:48
Last Modified: 24 Jul 2020 04:55
Uncontrolled Keywords: Twitter, depression, sentiment, stress, topic model
Fields of Research (2008): 08 Information and Computing Sciences > 0807 Library and Information Studies > 080703 Human Information Behaviour
08 Information and Computing Sciences > 0807 Library and Information Studies > 080709 Social and Community Informatics
Socio-Economic Objectives (2008): E Expanding Knowledge > 97 Expanding Knowledge > 970108 Expanding Knowledge in the Information and Computing Sciences
Identification Number or DOI: 10.1109/BESC48373.2019.8963550
URI: http://eprints.usq.edu.au/id/eprint/38102

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