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