Content characteristics predict the putative authenticity of COVID-19 rumors

Abstract

Rumors regarding COVID-19 have been prevalent on the Internet and affect the control of the COVID-19 pandemic. Using 1,296 COVID-19 rumors collected from an online platform (piyao.org.cn) in China, we found measurable differences in the content characteristics between true and false rumors. We revealed that the length of a rumor's headline is negatively related to the probability of a rumor being true [odds ratio (OR) = 0.37, 95% CI (0.30, 0.44)]. In contrast, the length of a rumor's statement is positively related to this probability [OR = 1.11, 95% CI (1.09, 1.13)]. In addition, we found that a rumor is more likely to be true if it contains concrete places [OR = 20.83, 95% CI (9.60, 48.98)] and it specifies the date or time of events [OR = 22.31, 95% CI (9.63, 57.92)]. The rumor is also likely to be true when it does not evoke positive or negative emotions [OR = 0.15, 95% CI (0.08, 0.29)] and does not include a call for action [OR = 0.06, 95% CI (0.02, 0.12)]. By contrast, the presence of source cues [OR = 0.64, 95% CI (0.31, 1.28)] and visuals [OR = 1.41, 95% CI (0.53, 3.73)] is related to this probability with limited significance. Our findings provide some clues for identifying COVID-19 rumors using their content characteristics.

Research on domain ontology construction based on the content features of online rumors

“…The content features of online rumors refer to the adaptive description of vocabulary, syntax and semantics in rumor texts. Fu et al 5 have made a linguistic analysis of COVID-19's online rumors from the perspectives of pragmatics, discourse analysis and syntax, and concluded that the source of information, the specific place and time of the event, the length of the title and statement, and the emotions aroused are the important characteristics to judge the authenticity of the rumors; Zhang et al 6 summarized the narrative theme, narrative characteristics, topic characteristics, language style and source characteristics of new media rumors; Li et al 7 found that rumors have authoritative blessing and fear appeal in headline rhetoric, and they use news and digital headlines extensively, and the topic construction mostly uses programmed fixed structure; Yu et al 8 analyzed and summarized the content distribution, narrative structure, topic scene construction and title characteristics of rumors in detail; Mourao et al 9 found that the language style of rumors is significantly different from that of real texts, and rumors tend to use simpler, more emotional and more radical discourse strategies; Zhou et al 10 analyzed the rumor text based on six analysis categories, such as content type, focus object and corroboration means, and found that the epidemic rumors were mostly "infectious" topics, with narrative expression being the most common, strong fear, and preference for exaggerated and polarized discourse style. Huang et al 11 conducted an empirical study based on WeChat rumors, and found that the "confirmation" means of rumors include data corroboration and specific information, hot events and authoritative release; Butt et al 12 analyzed the psycholinguistic features of rumors, and extracted four features from the rumor data set: LIWC, readability, senticnet and emotions.…”

Too Real to be Questioned: Analysis of the Factors Influencing the Spread of Online Scientific Rumors in China

“…Rumors may either be true or false.” In China, “piyao,” an official rumor-debunking platform (https://www.piyao.org.cn/2020yqpy/) sets the record straight on every rumor collected by rating it as “true,”“false,” or “undetermined” with the help of professionals, researchers, and reporters. Although most of them are false rumors, there are still some that are true (Zhao et al, 2022). For example, in December 2019, Dr. Li, an ophthalmologist at Wuhan Central Hospital, warned friends and colleagues about the possible identification of a novel and deadly virus in a private chat group on WeChat.…”

A comparative analysis of the COVID-19 Infodemic in English and Chinese: insights from social media textual data

“…Wan et al ( 11 ) described the prominent lexical and grammatical features of English COVID-19 misinformation, interpreted the underlying (psycho-)linguistic triggers, and studied the feature indexing for anti-infodemic modeling. Zhao et al ( 12 ) used 1,296 COVID-19 rumors collected from an online platform in China, and found measurable differences in the content characteristics between true and false rumors. Zhou et al ( 13 ) investigated both thematic and emotional characteristics of COVID-19 fake news at different levels and compared them in English and Chinese.…”