Understanding health food messages on Twitter for health literacy promotion

The Information Sharing Behaviors of Dietitians and Twitter Users in the Nutrition and COVID-19 Infodemic: Content Analysis Study of Tweets

Background

The COVID-19 pandemic has generated an infodemic, an overabundance of online and offline information. In this context, accurate information as well as misinformation and disinformation about the links between nutrition and COVID-19 have circulated on Twitter since the onset of the pandemic.

Objective

The purpose of this study was to compare tweets on nutrition in times of COVID-19 published by 2 groups, namely, a preidentified group of dietitians and a group of general users of Twitter, in terms of themes, content accuracy, use of behavior change factors, and user engagement, in order to contrast their information sharing behaviors during the pandemic.

Methods

Public English-language tweets published between December 31, 2019, and December 31, 2020, by 625 dietitians from Canada and the United States, and Twitter users were collected using hashtags and keywords related to nutrition and COVID-19. After filtration, tweets were coded against an original codebook of themes and the Theoretical Domains Framework (TDF) for identifying behavior change factors, and were compared to reliable nutritional recommendations pertaining to COVID-19. The numbers of likes, replies, and retweets per tweet were also collected to determine user engagement.

Results

In total, 2886 tweets (dietitians, n=1417; public, n=1469) were included in the analyses. Differences in frequency between groups were found in 11 out of 15 themes. Grocery (271/1417, 19.1%), and diets and dietary patterns (n=507, 34.5%) were the most frequently addressed themes by dietitians and the public, respectively. For 9 out of 14 TDF domains, there were differences in the frequency of usage between groups. “Skills” was the most used domain by both groups, although they used it in different proportions (dietitians: 612/1417, 43.2% vs public: 529/1469, 36.0%; P<.001). A higher proportion of dietitians’ tweets were accurate compared with the public’s tweets (532/575, 92.5% vs 250/382, 65.5%; P<.001). The results for user engagement were mixed. While engagement by likes varied between groups according to the theme, engagement by replies and retweets was similar across themes but varied according to the group.

Conclusions

Differences in tweets between groups, notably ones related to content accuracy, themes, and engagement in the form of likes, shed light on potentially useful and relevant elements to include in timely social media interventions aiming at fighting the COVID-19–related infodemic or future infodemics.

Predicting ratings of social media feeds: combining latent-factors and emotional aspects for improving performance of different classifiers

Purpose

The widespread acceptance of various social platforms has increased the number of users posting about various services based on their experiences about the services. Finding out the intended ratings of social media (SM) posts is important for both organizations and prospective users since these posts can help in capturing the user’s perspectives. However, unlike merchant websites, the SM posts related to the service-experience cannot be rated unless explicitly mentioned in the comments. Additionally, predicting ratings can also help to build a database using recent comments for testing recommender algorithms in various scenarios.

Design/methodology/approach

In this study, the authors have predicted the ratings of SM posts using linear (Naïve Bayes, max-entropy) and non-linear (k-nearest neighbor, k-NN) classifiers utilizing combinations of different features, sentiment scores and emotion scores.

Findings

Overall, the results of this study reveal that the non-linear classifier (k-NN classifier) performed better than the linear classifiers (Naïve Bayes, Max-entropy classifier). Results also show an improvement of performance where the classifier was combined with sentiment and emotion scores. Introduction of the feature “factors of importance” or “the latent factors” also show an improvement of the classifier performance.

Originality/value

This study provides a new avenue of predicting ratings of SM feeds by the use of machine learning algorithms along with a combination of different features like emotional aspects and latent factors.

The Impact of Health Information Privacy Concerns on Engagement and Payment Behaviors in Online Health Communities

Online health communities (OHCs) have enjoyed increasing popularity in recent years, especially in the context of the COVID-19 pandemic. However, several concerns have been raised regarding the privacy of users’ personal information in OHCs. Considering that OHCs are a type of data-sharing or data-driven platform, it is crucial to determine whether users’ health information privacy concerns influence their behaviors in OHCs. Thus, by conducting a survey, this study explores the impact of users’ health information privacy concerns on their engagement and payment behavior (Paid) in OHCs. The empirical results show that users’ concerns about health information privacy reduce their Paid in OHCs by negatively influencing their OHC engagement. Further analysis reveals that if users have higher benefit appraisals (i.e., perceived informational and emotional support from OHCs) and lower threat appraisals (i.e., perceived severity and vulnerability of information disclosure from OHCs), the negative effect of health information privacy concerns on users’ OHC engagement will decrease.

Effects of brief exposure to misinformation about e-cigarette harms on twitter: a randomised controlled experiment

OBJECTIVES

To assess the effect of exposure to misinformation about e-cigarette harms found on Twitter on adult current smokers' intention to quit smoking cigarettes, intention to purchase e-cigarettes and perceived relative harm of e-cigarettes compared with regular cigarettes.

SETTING

An online randomised controlled experiment conducted in November 2019 among USA and UK current smokers.

PARTICIPANTS

2400 adult current smokers aged ≥18 years who were not current e-cigarette users recruited from an online panel. Participants' were randomised in a 1:1:1:1 ratio using a least-fill randomiser function.

INTERVENTIONS

Viewing 4 tweets in random order within one of four conditions: (1) e-cigarettes are just as or more harmful than smoking, (2) e-cigarettes are completely harmless, (3) e-cigarette harms are uncertain, and (4) a control condition of tweets about physical activity.

PRIMARY OUTCOMES MEASURES

Self-reported post-test intention to quit smoking cigarettes, intention to purchase e-cigarettes, and perceived relative harm of e-cigarettes compared with smoking.

RESULTS

Among US and UK participants, after controlling for baseline measures of the outcome, exposure to tweets that e-cigarettes are as or more harmful than smoking versus control was associated with lower post-test intention to purchase e-cigarettes (β=-0.339, 95% CI -0.487 to -0.191, p<0.001) and increased post-test perceived relative harm of e-cigarettes (β=0.341, 95% CI 0.273 to 0.410, p<0.001). Among US smokers, exposure to tweets that e-cigarettes are completely harmless was associated with higher post-test intention to purchase e-cigarettes (β=0.229, 95% CI 0.002 to 0.456, p=0.048) and lower post-test perceived relative harm of e-cigarettes (β=-0.154, 95% CI -0.258 to -0.050, p=0.004).

CONCLUSIONS

US and UK adult current smokers may be deterred from considering using e-cigarettes after brief exposure to tweets that e-cigarettes were just as or more harmful than smoking. Conversely, US adult current smokers may be encouraged to use e-cigarettes after exposure to tweets that e-cigarettes are completely harmless. These findings suggest that misinformation about e-cigarette harms may influence some adult smokers' decisions to consider using e-cigarettes.

TRIAL REGISTRATION NUMBER

ISRCTN16082420.

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.

Influencing Factors on Health Information to Improve Public Health Literacy in the Official WeChat Account of Guangzhou CDC

Background: Social media is used as a new channel for health information. In China, the official WeChat account is becoming the most popular platform for health information dissemination, which has created a good opportunity for the Centers for Disease Control and Prevention to facilitate health information online to improve emergency public health literacy. Methods: Data were collected from the Guangzhou CDC i-Health official WeChat account between April 1, 2018 and April 30, 2019. Descriptive analysis was performed for basic information about the followers and posts of the official WeChat account. Multiple logistic regression analysis was used to analyze the association among various factors of posts on engagement of followers of the official WeChat account. Results: Among 187,033 followers, the total numbers of post views, shares, likes, add to favorites, and comments for 213 posts were 1,147,308, 8,4671, and 5,535, respectively. Engagement of followers peaked on the dissemination date and gradually declined. The main post topics were health education posts and original posts. In the multiple logistic regression model, the number of post views was found to be significantly associated with infectious disease posts (AOR: 3.20, 95% CI: 1.16-8.81), original posts (AOR: 10.20, 95% CI: 1.17-89.28), and posts with title-reflected content (AOR: 2.93, 95% CI: 1.16-8.81). Conclusion: Our findings facilitate the government to formulate better strategies and improve the effectiveness of public information dissemination.

Healthy Food on the Twitter Social Network: Vegan, Homemade, and Organic Food

Online social networks have become an everyday aspect of many people’s lives. Users spend more and more time on these platforms and, through their interactions on social media platforms, they create active and passive digital footprints. These data have a strong potential in many research areas; indeed, understanding people’s communication on social media is essential for understanding their attitudes, experiences, behaviors and values. Researchers have found that the use of social networking sites impacts eating behavior; thus, analyzing social network data is important for understanding the meaning behind expressions used in the context of healthy food. This study performed a communication analysis of data from the social network Twitter, which included 666,178 messages posted by 168,134 individual users. These data comprised all tweets that used the #healthyfood hashtag between 2019 and 2020 on Twitter. The results revealed that users most commonly associate healthy food with a healthy lifestyle, diet, and fitness. Foods associated with this hashtag were vegan, homemade, and organic. Given that people change their behavior according to other people’s behavior on social networks, these data could be used to identify current and future associations with current and future perceptions of healthy food characteristics.

Factors Driving Citizen Engagement With Government TikTok Accounts During the COVID-19 Pandemic: Model Development and Analysis

Background

During the COVID-19 pandemic, growth in citizen engagement with social media platforms has enabled public health departments to accelerate and improve health information dissemination, developing transparency and trust between governments and citizens. In light of these benefits, it is imperative to learn the antecedents and underlying mechanisms for this to maintain and enhance engagement.

Objective

The aim of this study is to determine the factors and influencing mechanisms related to citizen engagement with the TikTok account of the National Health Commission of China during the COVID-19 pandemic.

Methods

Using a web crawler, 355 short videos were collected from the Healthy China account on TikTok (with more than 3 million followers throughout China), covering the period from January 21, 2020, to April 25, 2020. The title and video length, as well as the number of likes, shares, and comments were collected for each video. After classifying them using content analysis, a series of negative binomial regression analyses were completed.

Results

Among the 355 videos, 154 (43.4%) related to guidance for clinicians, patients, and ordinary citizens, followed by information concerning the government’s handling of the pandemic (n=100, 28.2%), the latest news about COVID-19 (n=61, 17.2%), and appreciation toward frontline emergency services (n=40, 11.3%). Video length, titles, dialogic loop, and content type all influenced the level of citizen engagement. Specifically, video length was negatively associated with the number of likes (incidence rate ratio [IRR]=0.19, P<.001) and comments (IRR=0.39, P<.001). Title length was positively related to the number of shares (IRR=24.25, P=.01), likes (IRR=8.50, P=.03), and comments (IRR=7.85, P=.02). Dialogic loop negatively predicted the number of shares (IRR=0.56, P=.03). In comparison to appreciative information, information about the government’s handling of the situation (IRR=5.16, P<.001) and guidelines information (IRR=7.31, P<.001) were positively correlated with the number of shares, while the latest news was negatively related to the number of likes received (IRR=0.46, P=.004). More importantly, the relationship between predictors and citizen engagement was moderated by the emotional valence of video titles. Longer videos with positive titles received a higher number of likes (IRR=21.72, P=.04) and comments (IRR=10.14, P=.047). Furthermore, for short videos related to government handling of the pandemic (IRR=14.48, P=.04) and guidance for stakeholders (IRR=7.59, P=.04), positive titles received a greater number of shares. Videos related to the latest news (IRR=66.69, P=.04) received more likes if the video title displayed higher levels of positive emotion.

Conclusions

During the COVID-19 pandemic, videos were frequently published on government social media platforms. Video length, title, dialogic loop, and content type significantly influenced the level of citizen engagement. These relationships were moderated by the emotional valence of the video’s title. Our findings have implications for maintaining and enhancing citizen engagement via government social media.

Vaccine Hesitancy on Social Media: Sentiment Analysis from June 2011 to April 2019

Vaccine hesitancy was one of the ten major threats to global health in 2019, according to the World Health Organisation. Nowadays, social media has an important role in the spread of information, misinformation, and disinformation about vaccines. Monitoring vaccine-related conversations on social media could help us to identify the factors that contribute to vaccine confidence in each historical period and geographical area. We used a hybrid approach to perform an opinion-mining analysis on 1,499,227 vaccine-related tweets published on Twitter from 1st June 2011 to 30th April 2019. Our algorithm classified 69.36% of the tweets as neutral, 21.78% as positive, and 8.86% as negative. The percentage of neutral tweets showed a decreasing tendency, while the proportion of positive and negative tweets increased over time. Peaks in positive tweets were observed every April. The proportion of positive tweets was significantly higher in the middle of the week and decreased during weekends. Negative tweets followed the opposite pattern. Among users with ≥2 tweets, 91.83% had a homogeneous polarised discourse. Positive tweets were more prevalent in Switzerland (71.43%). Negative tweets were most common in the Netherlands (15.53%), Canada (11.32%), Japan (10.74%), and the United States (10.49%). Opinion mining is potentially useful to monitor online vaccine-related concerns and adapt vaccine promotion strategies accordingly.

Improving cancer survivors' e-health literacy via online health communities (OHCs): a social support perspective

PURPOSE

Cancer survivors should have adequate e-health literacy to help them better use online health information. Online health communities (OHCs) can offer cancer survivors different types of social support that can represent another resource to improve health outcomes. However, there is little knowledge of how these OHC are directly related to a cancer survivors' e-health literacy. This study explores how different types of social support in OHCs are associated with cancer survivors' e-health literacy.

METHODS

A questionnaire was developed to collect data from two Chinese OHCs used by cancer survivors. The questionnaire is composed of two parts: six sociodemographic variables (i.e., gender, age, city, education, tenure, and prior Internet experience), two scales for informational support behaviors (i.e., health knowledge seeking and provision of health knowledge), a measure of emotional support within such a setting, and a measure of e-health literacy. Based on 162 complete samples, we determined the measurement properties of the scales used, provided descriptive statistics on major sociodemographic variables and conducted bivariate and multivariable hierarchical regression.

RESULTS

For cancer survivors, females demonstrate higher levels of e-health literacy. Higher education level was related to higher e-health literacy. Health knowledge seeking, contributing to health knowledge, and emotional support were all positively associated with e-health literacy. The interaction effect between health knowledge and emotional support is positively associated with e-health literacy.

CONCLUSIONS

Informational support and emotional support, as two major subtypes of social support within resources available in OHCs, are positively associated with e-health literacy among cancer survivors.

IMPLICATIONS FOR CANCER SURVIVORS

Cancer survivors might benefit from an active strategy for improving personal e-health literacy that includes more active informational involvement and emotional support rather than a passive lurking through e-health information and seeking and reading postings in OHCs.

Understanding the factors influencing health professionals' online voluntary behaviors: Evidence from YiXinLi, a Chinese online health community for mental health

BACKGROUND

Normal users' voluntary behaviors (e.g., knowledge sharing) in virtual communities (VCs) has been well investigated; however, research on health professionals' voluntary behaviors in online health communities (OHCs) is limited.

OBJECTIVE

This paper focuses on OHCs for mental health and aims to explore how intrinsic and extrinsic motivations influence mental health service providers' voluntary behaviors.

METHODS

Based on motivation theory and prior studies, we incorporated technical competence as intrinsic motivation and online reputation and economic rewards as extrinsic motivations, and proposed five hypotheses. We crawled objective data from YiXinLi, a Chinese OHC for mental health, and tested the hypotheses based on the Poisson regression model. All hypotheses are supported.

RESULTS

1) Technical competence, online reputation, and economic rewards positively influence mental health service providers' voluntary behaviors; 2) the interaction effect between technical competence and online reputation negatively influences mental health service providers' voluntary behaviors; 3) the interaction effect between technical competence and economic rewards negatively influences mental health service providers' voluntary behaviors.

CONCLUSIONS

Both intrinsic motivations and extrinsic motivations positively influence mental health service providers' voluntary behaviors, and their interaction effects negatively influence mental health service providers' voluntary behaviors. This study first contributes to the literature on health professionals' voluntary behaviors in OHCs by verifying the positive effect of economic rewards. It then contributes to motivation theory by incorporating a situation where intrinsic motivations and extrinsic motivations could negatively interact.