eprintid: 10179971
rev_number: 6
eprint_status: archive
userid: 699
dir: disk0/10/17/99/71
datestamp: 2023-10-27 09:52:35
lastmod: 2023-10-27 09:52:35
status_changed: 2023-10-27 09:52:35
type: article
metadata_visibility: show
sword_depositor: 699
creators_name: Erturk, Sinan
creators_name: Hudson, Georgie
creators_name: Jansli, Sonja M
creators_name: Morris, Daniel
creators_name: Odoi, Clarissa M
creators_name: Wilson, Emma
creators_name: Clayton-Turner, Angela
creators_name: Bray, Vanessa
creators_name: Yourston, Gill
creators_name: Cornwall, Andrew
creators_name: Cummins, Nicholas
creators_name: Wykes, Til
creators_name: Jilka, Sagar
title: Codeveloping and Evaluating a Campaign to Reduce Dementia Misconceptions on Twitter: Machine Learning Study
ispublished: pub
divisions: UCL
divisions: B02
divisions: C07
divisions: D79
keywords: Twitter, codevelopment, machine learning, misconceptions, patient and public involvement, social media, stigma
note: This is an open-access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work, first published in JMIR Infodemiology, is properly cited. The complete bibliographic information, a link to the original publication on https://infodemiology.jmir.org/, as well as this copyright and license information must be included.
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.
date: 2022-11-22
date_type: published
publisher: JMIR Publications Inc.
official_url: https://doi.org/10.2196/36871
oa_status: green
full_text_type: pub
language: eng
primo: open
primo_central: open_green
verified: verified_manual
elements_id: 1993771
doi: 10.2196/36871
medium: Electronic-eCollection
pii: v2i2e36871
lyricists_name: Hudson, Georgina
lyricists_id: GCHHU18
actors_name: Hudson, Georgina
actors_id: GCHHU18
actors_role: owner
full_text_status: public
publication: JMIR Infodemiology
volume: 2
number: 2
article_number: e36871
event_location: Canada
citation:        Erturk, Sinan;    Hudson, Georgie;    Jansli, Sonja M;    Morris, Daniel;    Odoi, Clarissa M;    Wilson, Emma;    Clayton-Turner, Angela;                         ... Jilka, Sagar; + view all <#>        Erturk, Sinan;  Hudson, Georgie;  Jansli, Sonja M;  Morris, Daniel;  Odoi, Clarissa M;  Wilson, Emma;  Clayton-Turner, Angela;  Bray, Vanessa;  Yourston, Gill;  Cornwall, Andrew;  Cummins, Nicholas;  Wykes, Til;  Jilka, Sagar;   - view fewer <#>    (2022)    Codeveloping and Evaluating a Campaign to Reduce Dementia Misconceptions on Twitter: Machine Learning Study.                   JMIR Infodemiology , 2  (2)    , Article e36871.  10.2196/36871 <https://doi.org/10.2196/36871>.       Green open access   
 
document_url: https://discovery.ucl.ac.uk/id/eprint/10179971/1/infodemiology_v2i2e36871.pdf