Understanding the Social Mechanism of Cancer Misinformation Spread on YouTube and Lessons Learned: Infodemiological Study

Abstract

Background A knowledge gap exists between the list of required actions and the action plan for countering cancer misinformation on social media. Little attention has been paid to a social media strategy for disseminating factual information while also disrupting misinformation on social media networks. Objective The aim of this study was to, first, identify the spread structure of cancer misinformation on YouTube. We asked the question, “How do YouTube videos play an important role in spreading information about the self-administration of anthelmintics for dogs as a cancer medicine for humans?” Second, the study aimed to suggest an action strategy for disrupting misinformation diffusion on YouTube by exploiting the network logic of YouTube information flow and the recommendation system. We asked the question, “What would be a feasible and effective strategy to block cancer misinformation diffusion on YouTube?” Methods The study used the YouTube case of the self-administration of anthelmintics for dogs as an alternative cancer medicine in South Korea. We gathered Korean YouTube videos about the self-administration of fenbendazole. Using the YouTube application programming interface for the query “fenbendazole,” 702 videos from 227 channels were compiled. Then, videos with at least 50,000 views, uploaded between September 2019 and September 2020, were selected from the collection, resulting in 90 videos. Finally, 10 recommended videos for each of the 90 videos were compiled, totaling 573 videos. Social network visualization for the recommended videos was used to identify three intervention strategies for disrupting the YouTube misinformation network. Results The study found evidence of complex contagion by human and machine recommendation systems. By exposing stakeholders to multiple information sources on fenbendazole self-administration and by linking them through a recommendation algorithm, YouTube has become the perfect infrastructure for reinforcing the belief that fenbendazole can cure cancer, despite government warnings about the risks and dangers of self-administration. Conclusions Health authorities should upload pertinent information through multiple channels and should exploit the existing YouTube recommendation algorithm to disrupt the misinformation network. Considering the viewing habits of patients and caregivers, the direct use of YouTube hospital channels is more effective than the indirect use of YouTube news media channels or government channels that report public announcements and statements. Reinforcing through multiple channels is the key.

Evaluating the Effectiveness of Artificial Intelligence–powered Large Language Models Application in Disseminating Appropriate and Readable Health Information in Urology

“…These 3 categories were chosen for comparison, as while a significant portion of urological conditions is benign in nature, all 3 categories encompass pathologies commonly seen by urologists, and the implications of access to information about them on patient outcomes differ. 12 For example, cancer misinformation could potentially impact the survival rate for cancer patients, 13 and online health information often changes patients’ decisions to seek emergency care. 14 Each patient question was inputted into a new ChatGPT chat box by one of us (RD) consecutively on February 28, 2023, and the first outputs generated were recorded for analysis (Supplemental Table 1, https://www.jurology.com).…”

Investigation of the Misinformation about COVID-19 on YouTube Using Topic Modeling, Sentiment Analysis, and Language Analysis

“…YouTube's recommendations may be the most vital part of the misinformation, as many studies found that videos containing misinformation were easily recommended and that 70% of the viewership came from recommendations [51]. Yoon et al [52] reviewed videos with a 9-day crowd-sourced audit that suggested fenbendazole could cure cancer, and they found that the information was spread via both human and algorithm recommendations.…”

Investigating Misinformation about COVID-19 on YouTube using Topic Modeling, Sentiment Analysis, and Language Analysis

“…YouTube's recommendations may be the most vital part of the misinformation, as many studies found that videos containing misinformation were easily recommended and that 70% of the viewership came from recommendations [51]. Yoon et al [52] reviewed videos with a 9-day crowd-sourced audit that suggested fenbendazole could cure cancer, and they found that the information was spread via both human and algorithm recommendations.…”