Datafied Modelling of Self-Disclosure in Online Health Communication

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Author(s): Adamkolo Mohammed Ibrahim (University of Maiduguri, Nigeria), Hajara Umar Sanda (Bayero University, Kano, Nigeria), Nassir Abba-Aji (University of Maiduguri, Nigeria), Md Salleh Hassan (Universiti Putra Malaysia, Malaysia)and Phuong Thi Vi (Thai Nguyen University, Vietnam)
Copyright: 2023
Pages: 16
Source title: Encyclopedia of Data Science and Machine Learning
Source Author(s)/Editor(s): John Wang (Montclair State University, USA)
DOI: 10.4018/978-1-7998-9220-5.ch026

Keywords: Data Analysis and Statistics / Information Science Reference / Knowledge Management and Library Science / Machine Learning

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Abstract

Online health support communities play an important role in the exchange of social support and the improvement of health. There is a dearth of theoretical frameworks dedicated to a better understanding of self-disclosure and supporting exchange behaviour in the online health support community literature based on a data-driven research approach. Using a critical review of the literature, this chapter seeks to define the relationship between user support exchange behaviour and self-disclosure intention by modifying the theory of reasoned action (TRA) whilst introducing a new concept called ‘altruistic user role', which describes a user's proclivity to provide or seek social support. A combination of methods, including manual coding and machine learning algorithms, was employed. To help improve the design of online health support communities, this article discusses the implications of the proposed modified TRA.

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Datafied Modelling of Self-Disclosure in Online Health Communication

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