Does KFC sell rat? Analysis of tweets in the wake of a rumor outbreak

Content characteristics predict the putative authenticity of COVID-19 rumors

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.

A rumor reversal model of online health information during the Covid-19 epidemic

The development of the Internet and social media has expanded the speed and scope of information dissemination, but not all widely disseminated information is true. Especially during the public health emergencies, the endogenous health information demand generated by the lack of scientific knowledge of health information among online users stimulates the dissemination of health information by mass media while providing opportunities for rumor mongers to publish and spread online rumors. Invalid scientific knowledge and rumors will have a serious negative impact and disrupt social order during epidemic outbreaks such as COVID-19. Therefore, it is extremely important to construct an effective online rumor reversal model. The purpose of this study is to build an online rumor reversal model to control the spread of online rumors and reduce their negative impact. From the perspective of internal and external factors, based on the SIR model, this study constructed a G-SCNDR online rumor reversal model by adopting scientific knowledge level theory and an external online rumor control strategy. In this study, the G-SCNDR model is simulated, and a sensitivity analysis of the important parameters of the model is performed. The reversal efficiency of the G-SCNDR model can be improved by properly adopting the isolation-conversion strategy as the external control approach to online rumors with improving the popularization rate of the level of users' scientific knowledge and accelerating the transformation efficiency of official nodes. This study can help provide a better understanding of the process of online rumor spreading and reversing, as well as offering ceritain guidance and countermeasures for online rumor control during public health emergencies.

Understanding citizen engagement and concerns about the COVID-19 pandemic in China: a thematic analysis of government social media

Purpose

In crises such as the coronavirus disease 2019 (COVID-19) pandemic, governments need to act in time to lead citizens toward rational reactions and disclose information effectively to the public. This paper aims to understand the content published by the government and identify how citizen engagement relates to content type and emotional valence.

Design/methodology/approach

The grounded theory approach was adopted and nine types of content posted by the government were observed. The data were obtained from “People's Daily”, an official Sina Weibo account representing the voice of the Chinese government, from January 3 to June 22 in 2020.

Findings

The analysis shows that information related to emotional support and social mobilization were the most reposted, while those mentioning immoral and illegal incidents were the most liked and commented. Also, it was found that positive posts tend to attract more likes, yet with fewer reposts than neutral posts.

Originality/value

The authors adopted thematic analysis and focused on the impact of post content and valence on user participation behavior. This study expands the existing literature. The government can improve crises management capability by learning about citizen engagement behaviors on social media.

Identifying features of health misinformation on social media sites: an exploratory analysis

Purpose

The purpose of this paper is to explore the features of health misinformation on social media sites (SMSs). The primary goal of the study is to investigate the salient features of health misinformation and to develop a tool of features to help users and social media companies identify health misinformation.

Design/methodology/approach

Empirical data include 1,168 pieces of health information that were collected from WeChat, a dominant SMS in China, and the obtained data were analyzed through a process of open coding, axial coding and selective coding. Then chi-square test and analysis of variance (ANOVA) were adopted to identify salient features of health misinformation.

Findings

The findings show that the features of health misinformation on SMSs involve surface features, semantic features and source features, and there are significant differences in the features of health misinformation between different topics. In addition, the list of features was developed to identify health misinformation on SMSs.

Practical implications

This study raises awareness of the key features of health misinformation on SMSs. It develops a list of features to help users distinguish health misinformation as well as help social media companies filter health misinformation.

Originality/value

Theoretically, this study contributes to the academic discourse on health misinformation on SMSs by exploring the features of health misinformation. Methodologically, the paper serves to enrich the literature around health misinformation and SMSs that have hitherto mostly drawn data from health websites.

Social media analytics for end-users' expectation management in information systems development projects

Purpose

This study, an exploratory research, aims to investigate social media users' expectations of information systems (IS) products that are conceived but not yet launched. It specifically analyses social media data from Twitter about forthcoming smartphones and smartwatches from Apple and Samsung, two firms known for their innovative gadgets.

Design/methodology/approach

Tweets related to the following four forthcoming IS products were retrieved from 1st January 2020 to 30th September 2020: (1) Apple iPhone 12 (6,125 tweets), (2) Apple Watch 6 (553 tweets), (3) Samsung Galaxy Z Flip 2 (923 tweets) and (4) Samsung Galaxy Watch Active 3 (207 tweets). These 7,808 tweets were analysed using a combination of the Natural Language Processing Toolkit (NLTK) and sentiment analysis (SentiWordNet).

Findings

The online community was quite vocal about topics such as design, camera and hardware specifications. For all the forthcoming gadgets, the proportion of positive tweets exceeded that of negative tweets. The most prevalent sentiment expressed in Apple-related tweets was neutral, but in Samsung-related tweets was positive. Additionally, it was found that the proportion of tweets echoing negative sentiment was lower for Apple compared with Samsung.

Originality/value

This paper is the earliest empirical work to examine the degree to which social media chatter can be used by project managers for IS development projects, specifically for the purpose of end-users' expectation management.

Echo Chamber Effect in Rumor Rebuttal Discussions About COVID-19 in China: Social Media Content and Network Analysis Study

Background

The dissemination of rumor rebuttal content on social media is vital for rumor control and disease containment during public health crises. Previous research on the effectiveness of rumor rebuttal, to a certain extent, ignored or simplified the structure of dissemination networks and users’ cognition as well as decision-making and interaction behaviors.

Objective

This study aimed to roughly evaluate the effectiveness of rumor rebuttal; dig deeply into the attitude-based echo chamber effect on users’ responses to rumor rebuttal under multiple topics on Weibo, a Chinese social media platform, in the early stage of the COVID-19 epidemic; and evaluate the echo chamber’s impact on the information characteristics of user interaction content.

Methods

We used Sina Weibo’s application programming interface to crawl rumor rebuttal content related to COVID-19 from 10 AM on January 23, 2020, to midnight on April 8, 2020. Using content analysis, sentiment analysis, social network analysis, and statistical analysis, we first analyzed whether and to what extent there was an echo chamber effect on the shaping of individuals’ attitudes when retweeting or commenting on others’ tweets. Then, we tested the heterogeneity of attitude distribution within communities and the homophily of interactions between communities. Based on the results at user and community levels, we made comprehensive judgments. Finally, we examined users’ interaction content from three dimensions—sentiment expression, information seeking and sharing, and civility—to test the impact of the echo chamber effect.

Results

Our results indicated that the retweeting mechanism played an essential role in promoting polarization, and the commenting mechanism played a role in consensus building. Our results showed that there might not be a significant echo chamber effect on community interactions and verified that, compared to like-minded interactions, cross-cutting interactions contained significantly more negative sentiment, information seeking and sharing, and incivility. We found that online users’ information-seeking behavior was accompanied by incivility, and information-sharing behavior was accompanied by more negative sentiment, which was often accompanied by incivility.

Conclusions

Our findings revealed the existence and degree of an echo chamber effect from multiple dimensions, such as topic, interaction mechanism, and interaction level, and its impact on interaction content. Based on these findings, we provide several suggestions for preventing or alleviating group polarization to achieve better rumor rebuttal.

Internet users beware, you follow online health rumors (more than counter-rumors) irrespective of risk propensity and prior endorsement

Purpose

The Internet is a breeding ground for rumors. A way to tackle the problem involves the use of counter-rumor messages that refute rumors. This paper analyzes users' intention to follow rumors and counter-rumors as a function of two factors: individuals' risk propensity and messages' prior endorsement.

Design/methodology/approach

The paper conducted an online experiment. Complete responses from 134 participants were analyzed statistically.

Findings

Risk-seeking users were keener to follow counter-rumors compared with risk-averse ones. No difference was detected in terms of their intention to follow rumors. Users' intention to follow rumors always exceeded their intention to follow counter-rumors regardless of whether prior endorsement was low or high.

Research limitations/implications

This paper contributes to the scholarly understanding of people's behavioral responses when, unknowingly, exposed to rumors and counter-rumors on the Internet. Moreover, it dovetails the literature by examining how risk-averse and risk-seeking individuals differ in terms of intention to follow rumors and counter-rumors. It also shows how prior endorsement of such messages drives their likelihood to be followed.

Originality/value

The paper explores the hitherto elusive question: When users are unknowingly exposed to both a rumor and its counter-rumor, which entry is likely to be followed more than the other? It also takes into consideration the roles played by individuals' risk propensity and messages' prior endorsement.