The Role of Collective Group Orientation and Social Norms on Physical Distancing Behaviors for Disease Prevention

To reduce the impact of communicable diseases like COVID-19, collective action is required and likely to be susceptible to normative influence as well as whether people are more or less collectively oriented. We extend the theory of normative social behavior (TNSB) to account for group orientation and predict the relationships between social norms and physical distancing behaviors. Using a rolling cross-sectional design during 17 weeks of the pandemic, a national sample of US residents from 20 states (N = 8,778) participated in the study. The findings show that perceived descriptive norms, injunctive norms, and group orientation are significantly associated with physical distancing. The descriptive norm-behavior relationship and injunctive norm-behavior relationship are moderated by group orientation and the other predicted moderators in the TNSB. The findings extend the TNSB and highlight the need to understand social norms and group orientation in formative research for health communication campaigns designed to promote prevention behaviors.

Speaking of Values: Value-Expressive Communication and Exercise Intentions

This study introduces the concept of value-expressive communication and examines its relationship with behavioral intent. Value-expressive communication is conceptualized as the verbal output of a value-expressive attitude. Value-expressive communication about exercise is examined in relationship to strength of religious faith, exercise attitudes, communication frequency, and intentions to exercise among a sample of self-identified Christians. The data indicate a significant interaction between value-expressive communication and communication frequency explains significant variance in exercise intentions. Interact to and exercise attitudes is significantly associated with intentions to exercise. Suggestions for using value-expressive communication in health communication research and practice are discussed.

Dynamics of the social construction of knowledge: an empirical study of Zhihu in China

This study examined how the knowledge market promotes knowledge construction on question-and-answer (Q&A) websites. Data were collected from Zhihu, one of the largest Q&A sites in China. Hierarchical linear modeling was employed to estimate the dynamics of information accumulation, that is, the provision of informative content as factual construction. By employing information accumulation as the objective measure of knowledge construction, we determined that online knowledge construction was facilitated by a competitive marketplace of ideas. In addition, participation, temporal, and discourse features affected the dynamics of information accumulation. In specific, active users contributed significantly less to information accumulation than did ordinary users. Information accumulation shows a naturally decaying process represented as a function of answer order. The time interval between answers at the two preceding time points reduced the informativeness of answers at the subsequent time point. Answers with a higher readability score reduced the informativeness of subsequent answers. The results indicate that knowledge construction on Q&A sites unfolds as a process of mass collaboration among users.

SUPPLEMENTARY INFORMATION

The online version contains supplementary material available at 10.1140/epjds/s13688-022-00346-6.

Detecting Sentiment toward Emerging Infectious Diseases on Social Media: A Validity Evaluation of Dictionary-Based Sentiment Analysis

Despite the popularity and efficiency of dictionary-based sentiment analysis (DSA) for public health research, limited empirical evidence has been produced about the validity of DSA and potential harms to the validity of DSA. A random sample of a second-hand Ebola tweet dataset was used to evaluate the validity of DSA compared to the manual coding approach and examine the influences of textual features on the validity of DSA. The results revealed substantial inconsistency between DSA and the manual coding approach. The presence of certain textual features such as negation can partially account for the inconsistency between DSA and manual coding. The findings imply that scholars should be careful and critical about findings in disease-related public health research that use DSA. Certain textual features should be more carefully addressed in DSA.

Seek for Success: A Visualization Approach for Understanding the Dynamics of Academic Careers

How to achieve academic career success has been a long-standing research question in social science research. With the growing availability of large-scale well-documented academic profiles and career trajectories, scholarly interest in career success has been reinvigorated, which has emerged to be an active research domain called the Science of Science (i.e., SciSci). In this study, we adopt an innovative dynamic perspective to examine how individual and social factors will influence career success over time. We propose ACSeeker, an interactive visual analytics approach to explore the potential factors of success and how the influence of multiple factors changes at different stages of academic careers. We first applied a Multi-factor Impact Analysis framework to estimate the effect of different factors on academic career success over time. We then developed a visual analytics system to understand the dynamic effects interactively. A novel timeline is designed to reveal and compare the factor impacts based on the whole population. A customized career line showing the individual career development is provided to allow a detailed inspection. To validate the effectiveness and usability of ACSeeker, we report two case studies and interviews with a social scientist and general researchers.

When Public Health Research Meets Social Media: Knowledge Mapping From 2000 to 2018

BACKGROUND

Social media has substantially changed how people confront health issues. However, a comprehensive understanding of how social media has altered the foci and methods in public health research remains lacking.

OBJECTIVE

This study aims to examine research themes, the role of social media, and research methods in social media-based public health research published from 2000 to 2018.

METHODS

A dataset of 3419 valid studies was developed by searching a list of relevant keywords in the Web of Science and PubMed databases. In addition, this study employs an unsupervised text-mining technique and topic modeling to extract research themes of the published studies. Moreover, the role of social media and research methods adopted in those studies were analyzed.

RESULTS

This study identifies 25 research themes, covering different diseases, various population groups, physical and mental health, and other significant issues. Social media assumes two major roles in public health research: produce substantial research interest for public health research and furnish a research context for public health research. Social media provides substantial research interest for public health research when used for health intervention, human-computer interaction, as a platform of social influence, and for disease surveillance, risk assessment, or prevention. Social media acts as a research context for public health research when it is mere reference, used as a platform to recruit participants, and as a platform for data collection. While both qualitative and quantitative methods are frequently used in this emerging area, cutting edge computational methods play a marginal role.

CONCLUSIONS

Social media enables scholars to study new phenomena and propose new research questions in public health research. Meanwhile, the methodological potential of social media in public health research needs to be further explored.

Who is Tracking Health on Mobile Devices: Behavioral Logfile Analysis in Hong Kong

Background

Health apps on mobile devices provide an unprecedented opportunity for ordinary people to develop social connections revolving around health issues. With increasing penetration of mobile devices and well-recorded behavioral data on such devices, it is desirable to employ digital traces on mobile devices rather than self-reported measures to capture the behavioral patterns underlying the use of mobile health (mHealth) apps in a more direct and valid way.

Objective

The objectives of this study were to (1) assess the demographic predictors of the adoption of mHealth apps; (2) investigate the temporal pattern underlying the use of mHealth apps; and (3) explore the impacts of demographic variables, temporal features, and app genres on the use of mHealth apps.

Methods

Logfile data of mobile devices were collected from a representative panel of about 2500 users in Hong Kong. Users’ mHealth app activities were analyzed. We first conducted a binary logistic regression analysis to uncover demographic predictors of users’ adoption status. Then we utilized a multilevel negative binomial regression to examine the impacts of demographic characteristics, temporal features, and app genres on mHealth app use.

Results

It was found that 27.5% of mobile device users in Hong Kong adopt at least one genre of mHealth app. Adopters of mHealth apps tend to be female and better educated. However, demographic characteristics did not showcase the predictive powers on the use of mHealth apps, except for the gender effect (B_female vs B_male=–0.18; P=.006). The use of mHealth apps demonstrates a significant temporal pattern, which is found to be moderately active during daytime and intensifying at weekends and at night. Such temporal patterns in mHealth apps use are moderated by individuals’ demographic characteristics. Finally, demographic characteristics were also found to condition the use of different genres of mHealth apps.

Conclusions

Our findings suggest the importance of dynamic perspective in understanding users’ mHealth app activities. mHealth app developers should consider more the demographic differences in temporal patterns of mHealth apps in the development of mHealth apps. Furthermore, our research also contributes to the promotion of mHealth apps by emphasizing the differences of usage needs for various groups of users.

Nature and Diffusion of Gynecologic Cancer-Related Misinformation on Social Media: Analysis of Tweets

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.

Citizens' Use of the Internet and Public Service Delivery

This article examines citizens' use of the Internet as a popular feedback mechanism, and argues that it can help improve institutional performance. Specifically, it assesses the relationship between Internet penetration rate and public service delivery across 31 first-level administrative divisions in People's Republic of China from 1997 to 2014. A hierarchical linear modelling was conducted using secondary data released by the National Bureau of Statistics and the China Internet Network Information Centre. The result shows a positive relationship between Internet penetration rate and public service delivery when controlling for GDP per capita and education level. The positive relationship increases over time in general. The implications of Internet use for performance-based legitimacy and its impact on political change (or stability) in the authoritarian context are further discussed.

SocialWave

Rapid advancement of social media tremendously facilitates and accelerates the information diffusion among users around the world. How and to what extent will the information on social media achieve widespread diffusion across the world? How can we quantify the interaction between users from different geolocations in the diffusion process? How will the spatial patterns of information diffusion change over time? To address these questions, a dynamic social gravity model (SGM) is proposed to quantify the dynamic spatial interaction behavior among social media users in information diffusion. The dynamic SGM includes three factors that are theoretically significant to the spatial diffusion of information: geographic distance, cultural proximity, and linguistic similarity. Temporal dimension is also taken into account to help detect recency effect, and ground-truth data is integrated into the model to help measure the diffusion power. Furthermore, SocialWave, a visual analytic system, is developed to support both spatial and temporal investigative tasks. SocialWave provides a temporal visualization that allows users to quickly identify the overall temporal diffusion patterns, which reflect the spatial characteristics of the diffusion network. When a meaningful temporal pattern is identified, SocialWave utilizes a new occlusion-free spatial visualization, which integrates a node-link diagram into a circular cartogram for further analysis. Moreover, we propose a set of rich user interactions that enable in-depth, multi-faceted analysis of the diffusion on social media. The effectiveness and efficiency of the mathematical model and visualization system are evaluated with two datasets on social media, namely, Ebola Epidemics and Ferguson Unrest.

Interplay between Public Attention and Public Emotion toward Multiple Social Issues on Twitter

This study aims to elucidate the intricate interplay between public attention and public emotion toward multiple social issues. A theoretical framework is developed based on three perspectives including endogenous affect hypothesis, affect transfer hypothesis, and affective intelligence theory. Large-scale longitudinal data with 265 million tweets on five social issues are analyzed using a time series analytical approach. Public attention on social issues can influence public emotion on the issue per se. Social issues interact with one another to attract public attention in both cooperative and competitive ways. Instead of a direct transfer from public emotion to public attention, the public emotion toward a social issue moderates the interaction between the issue and other issue(s).

An Examination of Users' Influence in Online HIV/AIDS Communities

A network perspective was adopted in this study to identify influential users in an online HIV community in China. Specifically, the indegree centrality, outdegree centrality, betweenness centrality, eigenvector centrality, and clustering coefficient of individuals were evaluated to measure the user influence in such a community. Moreover, this study examined how the digital divide, which is presently one of the major social equity issues in the information society, is associated with an individual's influence within the community. Two networks were formed on the basis of the behavioral data retrieved from the HIV community: the follower-followee network and the post-reply network. In the follower-followee network, members from areas with well-developed technologies demonstrated more connections, received more attention, and secured more critical positions in the network than their counterparts. However, such differences were insignificant in the post-reply network.

Understanding interactions in virtual HIV communities: a social network analysis approach

This study investigated the driving mechanism of building interaction ties among the people living with HIV/AIDS in one of the largest virtual HIV communities in China using social network analysis. Specifically, we explained the probability of forming interaction ties with homophily and popularity characteristics. The exponential random graph modeling results showed that members in this community tend to form homophilous ties in terms of shared location and interests. Moreover, we found a tendency away from popularity effect. This suggests that in this community, resources and information were not disproportionally received by a few of members, which could be beneficial to the overall community.

EvoRiver: Visual Analysis of Topic Coopetition on Social Media

Cooperation and competition (jointly called "coopetition") are two modes of interactions among a set of concurrent topics on social media. How do topics cooperate or compete with each other to gain public attention? Which topics tend to cooperate or compete with one another? Who plays the key role in coopetition-related interactions? We answer these intricate questions by proposing a visual analytics system that facilitates the in-depth analysis of topic coopetition on social media. We model the complex interactions among topics as a combination of carry-over, coopetition recruitment, and coopetition distraction effects. This model provides a close functional approximation of the coopetition process by depicting how different groups of influential users (i.e., "topic leaders") affect coopetition. We also design EvoRiver, a time-based visualization, that allows users to explore coopetition-related interactions and to detect dynamically evolving patterns, as well as their major causes. We test our model and demonstrate the usefulness of our system based on two Twitter data sets (social topics data and business topics data).

Visual analysis of topic competition on social media

How do various topics compete for public attention when they are spreading on social media? What roles do opinion leaders play in the rise and fall of competitiveness of various topics? In this study, we propose an expanded topic competition model to characterize the competition for public attention on multiple topics promoted by various opinion leaders on social media. To allow an intuitive understanding of the estimated measures, we present a timeline visualization through a metaphoric interpretation of the results. The visual design features both topical and social aspects of the information diffusion process by compositing ThemeRiver with storyline style visualization. ThemeRiver shows the increase and decrease of competitiveness of each topic. Opinion leaders are drawn as threads that converge or diverge with regard to their roles in influencing the public agenda change over time. To validate the effectiveness of the visual analysis techniques, we report the insights gained on two collections of Tweets: the 2012 United States presidential election and the Occupy Wall Street movement.

Cohort trends in perceived Internet influence on political efficacy in Hong Kong

The study explored overall and cohort-specific trends in Internet political efficacy from an age-period-cohort approach with a cross-sequential design. Perceived Internet influence on political efficacy is found to increase with age. Significant difference between Internet users and nonusers is also found in some cohorts. Online news reading and online chats/discussions have a positive impact in some cohorts.

Effect of losartan and captopril on expression of cardiac angiotensin II AT1 receptor mRNA in rats following myocardial infarction

AIM

To study the effects of losartan (Los) and captopril (Cap) treatment on expression of cardiac angiotensin II (Ang) AT1 receptor mRNA in rats following myocardial infarction (MI).

METHODS

Twenty-four rats with MI after coronary ligation for 7 d were randomly divided into 4 groups: A) Cap in drinking water, ad lib (2 g.L-1), B) i.g. Los 10 mg.kg-1.d-1, C) i.g. Los 30 mg.kg-1.d-1, and D) placebo for 6 wk. Sham-ligation rats (group E) served as controls. The levels of cardiac Ang AT1 receptor mRNA expression in each group (n = 6) were examined by Dot blot using digoxigenin-labeled cDNA probes.

RESULTS

Comparing with reflected peak areas of hybridization positive signals in group D (2640 +/- 201 micron 2), the expression of the cardiac Ang AT1 receptor mRNA was much lower in the 3 treated groups (group A 1360 +/- 134 micron 2, group B 1430 +/- 244 micron 2, group C 1310 +/- 95 micron 2) (P < 0.01). But no difference was found between the 3 treated groups and sham-ligation group (1230 +/- 233 micron 2) (P > 0.05).

CONCLUSION

Los and Cap attenuated the increase of cardiac Ang AT1 receptor mRNA expression in rats following MI.

[Effect of Astragalus membranaceus on Ca2+ influx and coxsackie virus B3 RNA replication in cultured neonatal rat heart cells]

The effect of Astragalus membranaceus (AM) on Ca2+ influx across the myocardial plasma membrane and coxsackie virus B3(CVB3)-RNA replication in cultured neonatal rat heart cells infected with CVB3 was investigated. It was found that the Ca2+ influx could be inhibited significantly (P < 0.01) by AM after infection of heart cells for 48 h. In addition, when the cultured heart cells infected with CVB3 and treated with AM for 48 h, the Ca2+ influx of infected heart cells also could be inhibited by AM (P < 0.05) and the amounts of CVB3-RNA in myocytes were significantly decreased than that in infected control group (P < 0.001). These phenomena suggested that AM could exert the effects of decreasing the secondary Ca2+ damages, and improving the abnormal myocardial electric activity, and inhibiting replication of CVB3-RNA in myocardium. Thus, it is a rational choice to treat patients with AM in viral myocarditis.

[Effect and mechanism of Astragalus membranaceus on coxsackie B3 virus RNA in mice]

Using Balb/c mice infected with Coxsackie virus B3 (CVB3) as a model, the effect of Astragalus membranaceus (AM) on CVB3-RNA has been observed in myocardial tissues of mice by RNA-RNA in situ hybridization with negative-strand RNA probes leballing with 35S and quantitative imaging analysis of positive hybridization signals. The mechanism of its effect on CVB3-RNA has also been investigated by induction with AM and detection of beta-interferon (beta-IFN). Results showed that the copy numbers of CVB3-RNA as well as the histologic necrotic sizes in myocardial tissues of AM treated infected mice were significantly smaller than that in infected normal saline treated mice (P < 0.01, P < 0.05) respectively, suggesting that AM could inhibit the replication of CVB3-RNA, but its effect on CVB3-RNA was not correlated with induction of beta-IFN.