Online Self-Disclosure and Social Sharing of Emotions of Women with Breast Cancer Using Instagram–Qualitative Conventional Content Analysis

Abstract

Objectives Women with breast cancer share their experiences and emotions about the illness on Instagram – a tool understudied when it comes to chronic illness. The paper characterizes the women's activities, and both online self-disclosure and social sharing of emotions concepts are used to help explain the women's practices. Methods Using qualitative conventional content analysis, four profiles of women are manually analysed, with posts as the unit of analysis ( n = 811). Based on eleven subcategories, three broad analytical categories are introduced: self-disclosure of negative information (C1), self-disclosure of positive information (C2) and disclosure of information about other people (C3). Results The women post narratives about suffering resulting from cancer, and the fears they experience. Also, the posts relate to positive emotions, joyful life events, self-acceptance, or self-affirmation. While writing, the women share information about people supporting them on their way to recovery. Discussion The activity of women cannot be explained only by their online self-disclosure, where anonymity and lack of offline support are often emphasized. Women on Instagram are not anonymous and report on support by relatives and other people. Rimé's concept of social sharing of emotions is used to explain this phenomenon.

Cancer on Tiktok–evaluating Positive Culture and Online Self-Disclosure Using Directed Content Analysis and in-Depth Interviews

“…The research continues the work done by the first author (Pluta, 2021) on online selfdisclosure of women with breast cancer using Instagram, with self-disclosure understood as the willingness to provide intimate, private information about oneself (e.g., Barak & Suler, 2008). Previously, Pluta showed that Instagram posts are full of fears, anxiety, pain, weaknesses, and suffering associated with the treatment and body changes (C1: Category 1-self-disclosure of negative information).…”

“…Also, this approach is deductive and guided by a more structured process than a conventional approach. Thus, the codebook was developed based on prior research on Instagram (Pluta, 2021) with previously identified categories and subcategories (the three categories indicated above and 10 subcategories).…”

Studying and Eliciting Self-Disclosure: Interdisciplinary Review of Research Methodologies and Behavioural Paradigms

“…Qualitative methods, such as in-depth interviews and semi-structured interviews, can provide a rich and nuanced understanding of self-disclosure behaviour, including the context and emotions involved, and allows researchers to practice induction and theory building (e.g., Klim et al, 2021;Pluta, 2021). Differently from interview-based techniques, which are specifically designed as part of an experimental manipulation to elicit self-disclosure under controlled conditions focusing on the effects of specific variables, interviews in qualitative research serve as a qualitative instrument for data elicitation (Vondracek, 1969).…”

Building Long-Term Human–Robot Relationships: Examining Disclosure, Perception and Well-Being Across Time

“…Further analysis of the disclosure content through automated and manual text analysis methods could provide deeper insights into the emotional content conveyed in participants' disclosures to the robot. Conducting a more comprehensive textual analysis using advanced computational techniques [see 143,144] and manual evaluation [e.g., [145][146][147] could offer valuable insights and context to participants' self-disclosures to the robot [93]. Although the study did not utilise manual coding of self-disclosure, the behavioural paradigm employed was specifically designed to encourage self-disclosure by asking participants questions about personal matters.…”