Investigating Online Discussions About Cancer Screening on Twitter (Subsequently Rebranded as X): Corpus Analysis

Vollständiger Abstract

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Abstract Background While cancer screening is proven to be effective in the early detection of the disease and early detection enables better treatment options, screening uptake has been declining. Research shows that online health information helps people to make health-related decisions. However, not all online health information is credible, and misinformation might play a role in people’s choice to take part in screening. Objective This study aimed to analyze online discussions about cancer screening programs using corpus analysis. Specifically, we aimed to investigate the full dataset through corpus analysis and misinformation in a manually coded subset. This enabled us to study naturalistic discussions about cancer screening over time, what information people share, and how prevalent misinformation is in these discussions. We differentiated tweets on Twitter (subsequently rebranded as X) for cervical, breast, colorectal, and general screening. Methods We extracted a corpus of 55,403 tweets from 2011 to 2023, tweeted by 22,493 users from a database containing over 5.9 billion tweets. We used specific search strings corresponding to different types of screening to gather our corpus. The corpus consisted of tweets, timestamps, hashtags, and shared URLs. We used a machine learning classifier trained on another dataset of tweets about cancer screening to automatically code whether a tweet fell within the scope of the study. We manually coded a randomly drawn stratified subset of 1200 tweets representative of the full corpus regarding year and screening program for the presence of misinformation. Results Tweets were not uniformly distributed across different screening programs and over time ( χ ² 36 =4045.99, n=55,403 ; P