Analysis and Detection of Health-Related Misinformation on Chinese Social Media

Abstract

With the mobile Internet development, e-health has become increasingly connected with people's daily life. However, health information on Internet is severely corrupted by misinformation, especially for the aged. It is necessary to analyze the characteristics of health-related misinformation on Internet and to design automated detection tools. In this study, we focus on analyzing common characteristics of reliable and unreliable health-related information on Chinese online social media, and exploring possible detection method using machine learning algorithms. We first collect a dataset containing both reliable and unreliable health-related articles from multiple Chinese online social media sites, with 2,296 reliable and 2,085 unreliable included. Then we analyze their differences with respect to writing style, text topic and feature distribution by both intuitive and statistical analysis. We also manually select 104 linguistic and statistical features that are useful for machine learning classifiers. Lastly, we propose a Health-related Misinformation Detection framework (HMD) that includes a feature-based method and a text-based method for detecting unreliable health-related information. Experiments verifies the performance of our proposed HMD method. INDEX TERMS Misinformation detection, health-related information analysis, feature selection. I. INTRODUCTION Network has become an indispensable part of social infrastructure in the 21st century. Information content is generated and spread on Internet in a speed much faster than ever before. The popularity of online social networks and smart phones also becomes a booster for information explosion. It was reported in 2015 that on average, people spent around 1 hour and 40 minutes browsing social networks every day [1]. However, not all online information is real. Misinformation widely exists on many self-media and microblogging sites due to the anonymous property of such websites. People take no responsibility for spreading misinformation, all they have to do is clicking the ''repost'' button and expecting a ''thumbs-up'' for this astonishing fake news. What's more, people are easily influenced by political propaganda on social The associate editor coordinating the review of this manuscript and approving it for publication was Yongtao Hao.

Detecting Health-Related Rumors on Twitter using Machine Learning Methods

“…While, the studies in [13] on the importance of checking and verifying online perceptions and credibility of information by health professionals and physicians in domain, otherwise it will be harmful to user's health. In the other way, the authors in [29] [30] [32] presented rumor detection methods by detecting the health-related misinformation using extracting and identifying the fake features. In [30] and [32], Health-related Misinformation Detection framework was developed in order to detect unreliable and reliable health-related information.…”

What Does Fake Look Like? A Review of the Literature on Intentional Deception in the News and on Social Media

“…In general, the headlines of intentionally deceptive news items tend to be eyecatching, with a propensity for exaggeration, sensationalism, and scaremongering (Chen, Conroy, and Rubin 2015;Potthast et al 2016;Sahoo and Gupta 2021;Shu et al 2017;Tucker et al 2018). In addition, these headlines tend to be longer (Asubiaro and Rubin 2018 ;Liu et al 2019), using more capitalized words, proper nouns and verb phrases. By doing so, titles of fabricated news items try to get many points across, while titles of truthful texts most often opt for brief and general summary statements (Horne and Adali 2017, 764).…”

“…By doing so, titles of fabricated news items try to get many points across, while titles of truthful texts most often opt for brief and general summary statements (Horne and Adali 2017, 764). For example, Liu et al (2019) show in their study on health-related information on Chinese social media that the long headlines of fake news articles often displayed patterns of "click-baiting" measured through the use of imperative idioms such as "(you) must" or "never (do this)".…”

“…Results are mixed. There is some evidence that fake news articles use fewer punctuation (e.g., Horne and Adali 2017), but others find more use of punctuation, at least in the headlines (Asubiaro and Rubin 2018;Liu et al 2019). Further, there may be differences dependent on the specific punctuation signs that are used.…”

“…Further, there may be differences dependent on the specific punctuation signs that are used. For instance, Liu et al (2019) conclude in their study of health-related information on Chinese social media that fake news articles use more exclamation marks in their headlines, whereas real news articles utilize more question marks.…”