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Codeveloping and Evaluating a Campaign to Reduce Dementia Misconceptions on Twitter: Machine Learning Study

Half of the categories (3/6, 50%) were agreed upon as different forms of misconceptions and the other half (3/6, 50%) were agreed as different forms of neutral tweets (further detail reported in the study by Hudson and Jansli [6]). We therefore could take each half of the categories and use this set of data as tweets rated as misconceptions, neutral, or neither for the purpose of this study.

Sinan Erturk, Georgie Hudson, Sonja M Jansli, Daniel Morris, Clarissa M Odoi, Emma Wilson, Angela Clayton-Turner, Vanessa Bray, Gill Yourston, Andrew Cornwall, Nicholas Cummins, Til Wykes, Sagar Jilka

JMIR Infodemiology 2022;2(2):e36871

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Comparing Professional and Consumer Ratings of Mental Health Apps: Mixed Methods Study

An alternative approach is that used by the M-Health Index & Navigation Database, which presents each app characteristic or feature as a separate filter [44]. This is beneficial in that it allows consumers to decide which characteristics or features matter to them but is challenging as multiple fields and filters exist.

Georgie Hudson, Esther Negbenose, Martha Neary, Sonja M Jansli, Stephen M Schueller, Til Wykes, Sagar Jilka