Hudson, G; Jansli, SM; Erturk, S; Morris, D; Odoi, CM; Clayton-Turner, A; Bray, V; ... Jilka, S; + view all Hudson, G; Jansli, SM; Erturk, S; Morris, D; Odoi, CM; Clayton-Turner, A; Bray, V; Yourston, G; Clouden, D; Proudfoot, D; Cornwall, A; Waldron, C; Wykes, T; Jilka, S; - view fewer (2022) Investigation of Carers' Perspectives of Dementia Misconceptions on Twitter: Focus Group Study. JMIR Aging , 5 (1) , Article e30388. 10.2196/30388.

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Abstract

Background: Dementia misconceptions on social media are common, with negative effects on people with the condition, their carers, and those who know them. This study codeveloped a thematic framework with carers to understand the forms these misconceptions take on Twitter. Objective: The aim of this study is to identify and analyze types of dementia conversations on Twitter using participatory methods. Methods: A total of 3 focus groups with dementia carers were held to develop a framework of dementia misconceptions based on their experiences. Dementia-related tweets were collected from Twitter's official application programming interface using neutral and negative search terms defined by the literature and by carers (N=48,211). A sample of these tweets was selected with equal numbers of neutral and negative words (n=1497), which was validated in individual ratings by carers. We then used the framework to analyze, in detail, a sample of carer-rated negative tweets (n=863). Results: A total of 25.94% (12,507/48,211) of our tweet corpus contained negative search terms about dementia. The carers' framework had 3 negative and 3 neutral categories. Our thematic analysis of carer-rated negative tweets found 9 themes, including the use of weaponizing language to insult politicians (469/863, 54.3%), using dehumanizing or outdated words or statements about members of the public (n=143, 16.6%), unfounded claims about the cures or causes of dementia (n=11, 1.3%), or providing armchair diagnoses of dementia (n=21, 2.4%). Conclusions: This is the first study to use participatory methods to develop a framework that identifies dementia misconceptions on Twitter. We show that misconceptions and stigmatizing language are not rare. They manifest through minimizing and underestimating language. Web-based campaigns aiming to reduce discrimination and stigma about dementia could target those who use negative vocabulary and reduce the misconceptions that are being propagated, thus improving general awareness.

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