Nature and Diffusion of Gynecologic Cancer-Related Misinformation on Social Media: Analysis of Tweets

Abstract

Background: Over the last two decades, the incidence and mortality rates of gynecologic cancers have increased at a constant rate in China. Gynecologic cancers have become one of the most serious threats to women's health in China. With the widespread use of social media, an increasing number of individuals have employed social media to produce, seek, and share cancer-related information. However, health information on social media is not always accurate. Health, and especially cancer-related, misinformation has been widely spread on social media, which can affect individuals' attitudinal and behavioral responses to cancer.

Objective: The aim of this study was to examine the nature and diffusion of gynecologic cancer-related misinformation on Weibo, the Chinese equivalent of Twitter.

Methods: A total of 2691 tweets related to 2 gynecologic cancers-breast cancer and cervical cancer-posted on Weibo from June 2015 to June 2016 were extracted using the Python Web Crawler. Two medical school graduate students with expertise in gynecologic diseases were recruited to code the tweets to differentiate between true information and misinformation as well as to identify the types of falsehoods. The diffusion characteristics of gynecologic cancer-related misinformation were compared with those of the true information.

Results: While most of the gynecologic cancer-related tweets provided medically accurate information, approximately 30% of them were found to contain misinformation. Furthermore, it was found that tweets about cancer treatment contained a higher percentage of misinformation than prevention-related tweets. Nevertheless, the prevention-related misinformation diffused significantly more broadly and deeply than true information on social media.

Conclusions: The findings of this study suggest the need for controlling and reducing the cancer-related misinformation on social media with the efforts from both service providers and medical professionals. More specifically, it is important to correct falsehoods related to the prevention of gynecologic cancers on social media and increase individuals' capacity to assess the veracity of Web-based information to curb the spread and thus minimize the consequences of cancer-related misinformation.

Keywords: China; breast cancer; cervical cancer; diffusion; misinformation; social media.

Figure 1

An illustration of the retweet network: (A) the full retweet network of all true information (red) and misinformation (green); (B) the largest retweet network of true information; (C) the largest retweet network of misinformation.

Figure 2

Complementary cumulative distribution functions of true information and misinformation cascades (x-axis and y-axis are log-transformed).

Figure 3

Estimated diffusion characteristics adjusted by thematic category and information veracity. Means reported here are estimated marginal means of multivariate analysis of variance (MANOVA). Outliers, multivariate normality, linear relationships between dependent variables, and multicollinearity were checked before analysis. Scale of retweets, number of comments, number of likes were log-transformed to fit the assumption of normal distribution.