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

doi:10.2196/36871

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

JMIR Infodemiology. 2022 Nov 22;2(2):e36871. doi: 10.2196/36871. eCollection 2022 Jul-Dec.

Authors

Sinan Erturk^{1

2}, Georgie Hudson^{1

2}, Sonja M Jansli^{1

2}, Daniel Morris^{1

2}, Clarissa M Odoi^{1

2}, Emma Wilson^{1

2}, Angela Clayton-Turner¹, Vanessa Bray¹, Gill Yourston¹, Andrew Cornwall¹, Nicholas Cummins¹, Til Wykes^{1

2}, Sagar Jilka^{1

2

3}

Affiliations

¹ Institute of Psychiatry, Psychology & Neuroscience King's College London London United Kingdom.
² South London and Maudsley NHS Foundation Trust London United Kingdom.
³ Warwick Medical School University of Warwick Coventry United Kingdom.

PMID: 37113444
PMCID: PMC9987190
DOI: 10.2196/36871

Abstract

Background: Dementia misconceptions on Twitter can have detrimental or harmful effects. Machine learning (ML) models codeveloped with carers provide a method to identify these and help in evaluating awareness campaigns.

Objective: This study aimed to develop an ML model to distinguish between misconceptions and neutral tweets and to develop, deploy, and evaluate an awareness campaign to tackle dementia misconceptions.

Methods: Taking 1414 tweets rated by carers from our previous work, we built 4 ML models. Using a 5-fold cross-validation, we evaluated them and performed a further blind validation with carers for the best 2 ML models; from this blind validation, we selected the best model overall. We codeveloped an awareness campaign and collected pre-post campaign tweets (N=4880), classifying them with our model as misconceptions or not. We analyzed dementia tweets from the United Kingdom across the campaign period (N=7124) to investigate how current events influenced misconception prevalence during this time.

Results: A random forest model best identified misconceptions with an accuracy of 82% from blind validation and found that 37% of the UK tweets (N=7124) about dementia across the campaign period were misconceptions. From this, we could track how the prevalence of misconceptions changed in response to top news stories in the United Kingdom. Misconceptions significantly rose around political topics and were highest (22/28, 79% of the dementia tweets) when there was controversy over the UK government allowing to continue hunting during the COVID-19 pandemic. After our campaign, there was no significant change in the prevalence of misconceptions.

Conclusions: Through codevelopment with carers, we developed an accurate ML model to predict misconceptions in dementia tweets. Our awareness campaign was ineffective, but similar campaigns could be enhanced through ML to respond to current events that affect misconceptions in real time.

Keywords: Twitter; codevelopment; machine learning; misconceptions; patient and public involvement; social media; stigma.

©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. Originally published in JMIR Infodemiology (https://infodemiology.jmir.org), 22.11.2022.