Online health information, as an emerging field in health communication research, has attracted close attention from researchers. To identify major determinants of why individuals seek health information online, we conducted a meta-analysis that systematically accumulates the existing research findings. To that end, by integrating three theories or models for examining information-seeking behavior, we developed a theoretical framework for the current meta-analysis that emphasizing psychosocial, instrumental, contextual, and demographic factors. By analyzing the effect sizes from 44 articles representing 54 empirical samples, we found that the quality, trustworthiness, and utility of online health information were the dominant predictors of seeking it and that instrumental factors were more important than psychological ones in determining whether individuals did so. Moreover, the development of information and communication technology, the sampling method, and the type of information sought significantly moderated pairwise relationships between determinants and seeking behavior, whereas culture did not. Herein, we discuss the theoretical implications of our findings as well as directions for future research.

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.

Purpose: The spread of rumors on social media has caused increasing concerns about an under-informed or even misinformed public when it comes to scientific issues. However, researchers have rarely investigated their diffusion in non-western contexts. This study aims to systematically examine the content and network structure of rumor-related discussions around genetically modified organisms (GMOs) on Chinese social media.Design/methodology/approach: This study identified 21,837 rumor-related posts of GMOs on Weibo, one of China's most popular social media platforms. An approach combining social network analysis and content analysis was employed to classify user attitudes toward rumors, measure the level of homophily of their attitudes and examine the nature of their interactions.Findings: Though a certain level of homophily existed in the interaction networks, referring to the observed echo chamber effect, Weibo also served as a public forum for GMO discussions in which cross-cutting ties between communities existed. A considerable amount of interactions emerged between the pro- and anti-GMO camps, and most of them involved providing or requesting information, which could mitigate the likelihood of opinion polarization. Moreover, this study revealed the declining role of traditional opinion leaders and pointed toward the need for alternative strategies for efficient fact-checking.Originality/value: In general, the findings of this study suggested that microblogging platforms such as Weibo can function as public forums for discussing GMOs that expose users to ideologically cross-cutting viewpoints. This study stands to provide important insights into the viral processes of scientific rumors on social media.

Following the COVID-19’s outbreak in China, wildlife-related issues such as wildlife management and conservation made headlines around the world due to the potential zoonotic nature of the newly discovered coronavirus. In our study, we examined the dynamic interaction of the news agenda and public agenda concerning wildlife-related issues on social media after COVID-19’s outbreak. Using big data analytics, we automatically extracted the agendas’ attributes and networks from 110,549 social media posts made from January 1 to April 8, 2020, and investigated the effect of second- and third-level dynamic agenda setting in a time series analysis. Our findings suggest that the agenda-setting effect of wildlife-related issues on social media was not a single-step, unidirectional action but a reciprocal, dynamic interaction constantly constructed by news outlets and the general public.

Given the increasing popularity of gamified crowdsourcing, the study reported here involved examining determinants of users' continuance intention toward crowdsourcing games, both with longitudinal data and reference to a revised unified theory of acceptance and use of technology (UTAUT). At three time points, data were collected from an online survey about playing crowdsourcing games. Time-lagged regression, cross-temporal correlation, and structural equation modeling were performed to examine determinants of the acceptance of crowdsourcing games. Results indicate that the revised UTAUT2 is applicable to explaining the acceptance of crowdsourcing games. Not only did effort expectancy, hedonic motivation, and social influence directly affect users’ continuance intention toward crowdsourcing games, but time-based variations also emerged in users’ perceptions and acceptance of the games and in how their perceptions affect their acceptance. The findings answer the call for a context-specific acceptance model and the identification of factors of adopting gamification.