Social support and audience engagement of lupus-related posts on social networking sites in China.

Background: Lupus, known as a chronic multisystem autoimmune disease, has become more common in China currently. Above all, most Chinese Lupus patients haven't ample knowledge and adequate understanding of this complicated chronic disease. In recent years, social networking sites have created an interactive environment in which patients can obtain health information and also can exchange personal experiences with others having similar health concerns. Objective: The overall aim of this study is to develop a better understanding of the social support requested and received during the routine social media use of lupus activists and their referents. In other words. This paper seeks to explore whether the lupus-related posts disseminated on the Sina microblog platform can serve and satisfy the needs of this group. Methods: Content analysis and descriptive analysis were conducted to ascertain the core topics of lupus-related posts on the Sina microblog. Chi-square tests were performed to determine the differences in types of social support between provision and request groups, as well as engaged and non-engaged groups. Finally, negative binomial regression was undertaken to investigate which types of social support generated more audience engagement. Results: By analyzing 9822 lupus-related posts derived from the Sina microblog, disease description was the most prominent theme. Evidence is presented which shows that information support was requested and supplied more frequently than emotional and instrumental support. Specifically, information support was provided more than requested, while the instrumental and emotional support provisions were less numerous than the requests. Analysis revealed that posts containing information support provisions attracted more engagement than those with the other five types of social support. Conclusions: Social networking sites play a critical role on disseminating lupus-related information and provide an interactive space in which users can freely communicate their health conditions and seek peer support. However, health practitioners not only have to present more communication strategies to provide emotional and instrumental support through social media, but also have to boost audience engagement.

Use of Large Language Models to Assess the Likelihood of Epidemics From the Content of Tweets: Infodemiology Study.

BACKGROUND: Previous work suggests that Google searches could be useful in identifying conjunctivitis epidemics. Content-based assessment of social media content may provide additional value in serving as early indicators of conjunctivitis and other systemic infectious diseases. OBJECTIVE: We investigated whether large language models, specifically GPT-3.5 and GPT-4 (OpenAI), can provide probabilistic assessments of whether social media posts about conjunctivitis could indicate a regional outbreak. METHODS: A total of 12,194 conjunctivitis-related tweets were obtained using a targeted Boolean search in multiple languages from India, Guam (United States), Martinique (France), the Philippines, American Samoa (United States), Fiji, Costa Rica, Haiti, and the Bahamas, covering the time frame from January 1, 2012, to March 13, 2023. By providing these tweets via prompts to GPT-3.5 and GPT-4, we obtained probabilistic assessments that were validated by 2 human raters. We then calculated Pearson correlations of these time series with tweet volume and the occurrence of known outbreaks in these 9 locations, with time series bootstrap used to compute CIs. RESULTS: Probabilistic assessments derived from GPT-3.5 showed correlations of 0.60 (95% CI 0.47-0.70) and 0.53 (95% CI 0.40-0.65) with the 2 human raters, with higher results for GPT-4. The weekly averages of GPT-3.5 probabilities showed substantial correlations with weekly tweet volume for 44% (4/9) of the countries, with correlations ranging from 0.10 (95% CI 0.0-0.29) to 0.53 (95% CI 0.39-0.89), with larger correlations for GPT-4. More modest correlations were found for correlation with known epidemics, with substantial correlation only in American Samoa (0.40, 95% CI 0.16-0.81). CONCLUSIONS: These findings suggest that GPT prompting can efficiently assess the content of social media posts and indicate possible disease outbreaks to a degree of accuracy comparable to that of humans. Furthermore, we found that automated content analysis of tweets is related to tweet volume for conjunctivitis-related posts in some locations and to the occurrence of actual epidemics. Future work may improve the sensitivity and specificity of these methods for disease outbreak detection.

Factors influencing public participation behavior relating to government microblogs on COVID-19 updates.

Introduction: During the global COVID-19 pandemic, densely populated megacities engaged in active international exchanges have faced the most severe impacts from both the disease and the associated infodemic. This study examines the factors influencing public participation behavior on government microblogs in these megacities during the pandemic. It guides megacities in disseminating epidemic information, promoting knowledge on epidemic prevention, managing public opinion, and addressing related matters. Methods: Utilizing the elaboration likelihood model's central and peripheral routes, drawing on an empirical analysis of 6,677 epidemic-related microblogs from seven Chinese megacities, this study analyses the influence mechanisms influencing public participation behavior and reveals the regulatory role of confirmed case numbers. Meanwhile,a qualitative comparative analysis examines and discusses diferent confgurations of ixn fuential factors. Results: The study reveals that microblog content richness demonstrates a U-shaped impact on public participation behavior. Conversely, content interaction, content length, and the number of fans positively impact participation, while update frequency has a negative impact. Additionally, the number of new confrmed cases positively regulates the impact of microblog content and publisher characteristics on public participation behavior. Public participation behavior also varies based on publishing time and content semantic features. This study further revealed the different confgurations of influential factors by QCA method. Conclusion: This study reveals the impact mechanism of the microblog content and publisher characteristics on public participation behavior. It also demonstrates the regulatory role of newly confrmed cases in the way content and publishers' characteristics influence public participation behavior. This study is of great significance for the operation of government microblogs, the release of emergency information, and the promotion of public participation.

Public Opinion About COVID-19 on a Microblog Platform in China: Topic Modeling and Multidimensional Sentiment Analysis of Social Media.

BACKGROUND: The COVID-19 pandemic raised wide concern from all walks of life globally. Social media platforms became an important channel for information dissemination and an effective medium for public sentiment transmission during the COVID-19 pandemic. OBJECTIVE: Mining and analyzing social media text information can not only reflect the changes in public sentiment characteristics during the COVID-19 pandemic but also help the government understand the trends in public opinion and reasonably control public opinion. METHODS: First, this study collected microblog comments related to the COVID-19 pandemic as a data set. Second, sentiment analysis was carried out based on the topic modeling method combining latent Dirichlet allocation (LDA) and Bidirectional Encoder Representations from Transformers (BERT). Finally, a machine learning linear regression (ML-LR) model combined with a sparse matrix was proposed to explore the evolutionary trend in public opinion on social media and verify the high accuracy of the model. RESULTS: The experimental results show that, in different stages, the characteristics of public emotion are different, and the overall trend is from negative to positive. CONCLUSIONS: The proposed method can effectively reflect the characteristics of the different times and space of public opinion. The results provide theoretical support and practical reference in response to public health and safety events.

Patterns in negative emotions, sleep disorders, and temperature: Evidence from microblog big data.

Existing studies have shown that temperature is related to mental illness and sleep disorders. However, few studies have explored the relationship between temperature and microblog negative emotions (MNE) and the predictive effect of MNE on sleep disorders. The present study elucidating the temperature patterns of MNE and sleep disorders, examines the predictive capability of these adverse emotions in precipitating sleep disorders, and operating within the schema of "climate-psychology-behavior". A negative binomial regression model (NBR) was formulated, amalgamating Temperature data, negative affective information procured from microblog, and sleep disorder records. Temperature and Apparent Air Temperature (AAT) were found to have a non-linear association with microblog negative emotions and sleep disorders, exhibiting a modest effect within a specified range, while extreme temperatures (both high and low) demonstrated substantial effects. In the constructed model, gender serves as a moderating factor, with females being more susceptible to temperature and AAT effects on MNE and sleep disorders than their male counterparts. Interestingly, AAT surfaced as a superior predictor compared to actual temperature. MNE were effective predictors of sleep disorders. Employing social media-centric models, as showcased in this study, augments the identification and prevention strategies targeting disease symptoms or pathologies within mental and public health domains.

Sentiment analysis and prediction model based on Chinese government affairs microblogs.

Existing sentiment analysis research on Chinese government affairs microblogs primarily focuses on the task of sentiment classification on microblogs. There has been a lack of investigation into the correlation of each government affairs microblog with the sentiment values of the corresponding comments below it. This study constructs a large-scale government affairs microblog dataset and explore the correlation of each microblog with the sentiment values of the corresponding comments below it. We proposed a new framework that includes data collection, sentiment analysis and sentiment prediction model training. This sentiment analysis framework is crucial in the government's understanding of the public's real-time sentiments toward policies. It also helps monitor the Internet public sentiment and actively guide the Internet public opinion. We first analyzed the sentiment distribution of government affairs microblogs and the sentiment values on meaningful words. We also discussed the discrepancy in text similarity and sentiment values between microblogs. Furthermore, we investigated the extreme emotional content and discussed the factors influencing the sentiment values of comments. Finally, we designed a collaborative attention regression model to predict the sentiments of microblogs. The sentiment prediction model performed well in the sentiment prediction regression task. The sentiment analysis and the prediction framework for government affairs microblogs in this study can be used as a reference for government-related Internet opinion monitoring.

An intelligent film recommender system based on emotional analysis.

The existing personalized film recommendation methods take the user's historical rating as an important basis for recommendation. However, the user's rating standards are different, so it is difficult to mine the user's real preferences and form accurate push. Therefore, to achieve high-quality personalized recommendation of films, it is particularly important to mine the emotion of user reviews. In this article, a personalized recommendation method based on sentiment analysis of film reviews is proposed, where natural language processing technology is used to mine the emotional tendency of user reviews. The multi-modal emotional features are weighted and the weighted fusion feature vector after PSO is taken as the overall emotion vector, then the emotional similarity of weighted fusion is calculated by considering the time factor of content publishing and the average emotional tendency of users. By calculating the matching degree of emotional value between users and films, the top-N film recommendation for target users is given. The test results show that the effect of the personalized film recommendation system based on multimodality is superior to that of the comparison method, which effectively solves the problem of different user rating scales, and really increases users' interest in watching films.

Mental time travel ability influences the representation of events and emotional expressions: evidence from microblogs.

Mental time travel (MTT) ability allows people to project themselves mentally into the past and future. It is associated with people's mental representation of events and objects. Using text analysis methods, we explore the linguistic representation and emotional expression of people with various MTT abilities. In Study 1, we assessed the users' MTT distances, text lengths, visual perspectives, priming effects of temporal words, and emotional valences by analyzing 2973 users' microblog texts. From our statistical analysis findings, users with far MTT incorporated longer text length and more third-person pronouns in their microblogs and are more likely to relate the future and past with the present than people with near MTT. However, the study showed no significant difference in emotional valence between people with different MTT distances. In Study 2, we explored the relationship between emotional valence and MTT ability by analyzing the comments of 1112 users on "procrastination." We found the users with far MTT more positive toward procrastination than those with near MTT. By analyzing users' social media platform data, this study re-examined and verified previous findings indicating that users who mentally travel different temporal distances represent events and emotional expressions differently. This study serves as an important reference for MTT studies.

Extracting Useful Emergency Information from Social Media: A Method Integrating Machine Learning and Rule-Based Classification.

User-generated contents (UGCs) on social media are a valuable source of emergency information (EI) that can facilitate emergency responses. However, the tremendous amount and heterogeneous quality of social media UGCs make it difficult to extract truly useful EI, especially using pure machine learning methods. Hence, this study proposes a machine learning and rule-based integration method (MRIM) and evaluates its EI classification performance and determinants. Through comparative experiments on microblog data about the "July 20 heavy rainstorm in Zhengzhou" posted on China's largest social media platform, we find that the MRIM performs better than pure machine learning methods and pure rule-based methods, and that its performance is influenced by microblog characteristics such as the number of words, exact address and contact information, and users' attention. This study demonstrates the feasibility of integrating machine learning and rule-based methods to mine the text of social media UGCs and provides actionable suggestions for emergency information management practitioners.

What Do I Suggest You Focus on in My Photo Story? The Effect of User Personality on the Position Significance of Jiugong Grid Images in Microblog.

Jiugong grid is one of the most used forms of multiple images posting in social media, with nine images arranged in three rows and three columns to present a related topic story from one microblog. The purpose of this study is to explore the relationship between the big five personality traits and the sequence position of Jiugong grid images. Two hundred thirty-seven volunteers completed a survey on the big five personality traits, and their 4,671 Jiugong grid microblogs with 42,039 photos were also obtained and analyzed. The results showed that users with varied kinds of personality traits could apply the significant position of Jiugong grid to emphasize certain content among multiple photos for a more attractable "story telling" in microblog. Compared with the image sequence position from the perspective of reading order, user personality traits had more relationship with that from the perspective of attention. This study is one of the first investigating the Jiugong grid image sharing behavior, which could theoretically enrich the social media image research from cognitive view and practically reveal the motivation of multiple images usage in social media, such as interface design and marketing purpose.

Signaling effect in social network and charity crowdfunding: Empirical analysis of charity crowdfunding of Sina MicroBlog in China.

With the increasing number of online charity donations, research on the influencing factors of individual donation behavior has become an important topic. Social interaction information in crowdfunding has become an essential basis for potential backers to make decisions. It provides new research space for charity crowdfunding and social capital theory. The primary purpose of this study is to explore the influence of social capital, social recommendation, and other signals on charity crowdfunding performance. We obtain 4,780 project information on the charity crowdfunding of Sina MicroBlog through data collection procedures. Our research found that both external social capital and internal capital can significantly improve the fundraising performance of crowdfunding projects. Projects with more social recommendations are more likely to obtain financial support. In the case of Medical aid crowdfunding projects, the positive promotion effect of social recommendations on project fundraising ability is enhanced. To get more effective support for crowdfunding projects, it is necessary to pay attention to the construction of social capital and the cultivation of its reputation to obtain the recognition of potential backers.

Investigation of the determinants for misinformation correction effectiveness on social media during COVID-19 pandemic.

The rapid dissemination of misinformation in social media during the COVID-19 pandemic triggers panic and threatens the pandemic preparedness and control. Correction is a crucial countermeasure to debunk misperceptions. However, the effective mechanism of correction on social media is not fully verified. Previous works focus on psychological theories and experimental studies, while the applicability of conclusions to the actual social media is unclear. This study explores determinants governing the effectiveness of misinformation corrections on social media with a combination of a data-driven approach and related theories on psychology and communication. Specifically, referring to the Backfire Effect, Source Credibility, and Audience's role in dissemination theories, we propose five hypotheses containing seven potential factors (regarding correction content and publishers' influence), e.g., the proportion of original misinformation and warnings of misinformation. Then, we obtain 1487 significant COVID-19 related corrections on Microblog between January 1st, 2020 and April 30th, 2020, and conduct annotations, which characterize each piece of correction based on the aforementioned factors. We demonstrate several promising conclusions through a comprehensive analysis of the dataset. For example, mentioning excessive original misinformation in corrections would not undermine people's believability within a short period after reading; warnings of misinformation in a demanding tone make correction worse; determinants of correction effectiveness vary among different topics of misinformation. Finally, we build a regression model to predict correction effectiveness. These results provide practical suggestions on misinformation correction on social media, and a tool to guide practitioners to revise corrections before publishing, leading to ideal efficacies.

Framing Public Opinion on Physician-Patient Conflicts on Microblog: A Comparative Content Analysis.

Introduction: Physician-patient conflicts in China have increased more than ten times from the 2000s to the 2020 and arouse heated discussions on microblog. The outbreak of the COVID-19 pandemic is believed to have brought a turnaround in the physician-patient relationship. However, little is known about the similarities and differences among the views of opinion leaders from the general public, physicians, and media regarding physician-patient conflict incidents on microblog, and whether the outbreak had an impact on this. Objective: This study aims to explore how opinion leaders from the physicians, general public, and media framed posts on major physician-patient conflict incidents on microblog, and compare the microblog post frames before and after the COVID-19 pandemic. The findings will provide more objective evidence of the attitudes and perspectives of the health professionals, general public, and media on physician-patient conflicts, and the influence of pandemics on physician-patient relationship. Methods: A comparative content analysis was conducted to examine the posts (n = 941) of microblog opinion leaders regarding major physician-patient conflicts in China from 2012 to 2020. Results: Post-pandemic microblog posts used more cooperation, positive and negative frames, but mentioned less health-related knowledge; no difference was found in the use of conflict and attribution frames. Results on the use of frames by opinion leaders from different communities found that the media used more conflict, cooperation, attribution, and positive frames, but used fewer negative frames and mentioned less health-related knowledge than general public and physicians. Results on the use of frames for different incidents found that incidents of violence against physicians used more cooperation, positive and negative frames and mentioned less health-related knowledge; in the contract, incidents of patient death used more attribution frames and mentioned more health-related knowledge. Conclusion: The physician and general public opinion leaders share some similarities in their post frames, implying that no fundamental discrepancy between them regarding physician-patient conflict incidents. However, the imbalanced use of frames by media microblogger would cultivate and reinforce the public perception of physician-patient contradictions. After the COVID-19 pandemic, more cooperation and positive frames were used in the posts, indicating an improvement in the physician-patient relationship in China.

Modeling and simulation of microblog-based public health emergency-associated public opinion communication.

With the advent of the era of "we media," many people's opinions have become easily accessible. Public health emergencies have always been an important aspect of public opinion exchange and emotional communication. In view of this sudden group panic, public opinion cannot be effectively monitored, controlled or guided. This makes it easy to amplify the beliefs and irrationality of social emotions, that threaten social security and stability. Considering the important role of opinion leaders in micro-blogs and users' interest in micro-blog information, a SIR model of public opinion propagation is constructed based on the novel coronavirus pneumonia model and micro-blog's public health emergencies information. The parameters of the model are calculated by combining the actual crawl data from the novel coronavirus pneumonia epidemic period, and the trends in the evolution of public opinion are simulated by MATLAB. The simulation results are consistent with the actual development of public opinion dissemination, which shows the effectiveness of the model. These research findings can help the government understand the principles that guide the propagation of public opinion and advise an appropriate time to control and correctly guide public opinion.

The role of proactive behavior on COVID-19 infordemic in the Chinese Sina-Microblog: a modeling study.

In order to avoid forming an information cocoon, the information propagation of COVID-19 is usually created through the action of "proactive search", an important behavior other than "reactive follow". This behavior has been largely ignored in modeling information dynamics. Here, we propose to fill in this gap by proposing a proactive-reactive susceptible-discussing-immune (PR-SFI) model to describe the patterns of co-propagation on social networks. This model is based on the forwarding quantity and takes into account both proactive search and reactive follow behaviors. The PR-SFI model is parameterized by data fitting using real data of COVID-19 related topics in the Chinese Sina-Microblog, and the model is calibrated and validated using the prediction accuracy of the accumulated forwarding users. Our sensitivity analysis and numerical experiments provide insights about optimal strategies for public health emergency information dissemination.

Microblog Topic-Words Detection Model for Earthquake Emergency Responses Based on Information Classification Hierarchy.

Social media data are constantly updated, numerous, and characteristically prominent. To quickly extract the needed information from the data to address earthquake emergencies, a topic-words detection model of earthquake emergency microblog messages is studied. First, a case analysis method is used to analyze microblog information after earthquake events. An earthquake emergency information classification hierarchy is constructed based on public demand. Then, subject sets of different granularities of earthquake emergency information classification are generated through the classification hierarchy. A detection model of new topic-words is studied to improve and perfect the sets of topic-words. Furthermore, the validity, timeliness, and completeness of the topic-words detection model are verified using 2201 messages obtained after the 2014 Ludian earthquake. The results show that the information acquisition time of the model is short. The validity of the whole set is 96.96%, and the average and maximum validity of single words are 78% and 100%, respectively. In the Ludian and Jiuzhaigou earthquake cases, new topic-words added to different earthquakes only reach single digits in validity. Therefore, the experiments show that the proposed model can quickly obtain effective and pertinent information after an earthquake, and the complete performance of the earthquake emergency information classification hierarchy can meet the needs of other earthquake emergencies.

COVID-19 information contact and participation analysis and dynamic prediction in the Chinese Sina-microblog.

The outbreak of a novel coronavirus (COVID-19) aroused great public opinion in the Chinese Sina-microblog. To help in designing effective communication strategies during a major public health emergency, we analyze the real data of COVID-19 information and propose a comprehensive susceptible-reading-forwarding-immune (SRFI) model to understand the patterns of key information propagation considering both public contact and participation. We develop the SRFI model, based on the public reading quantity and forwarding quantity that denote contact and participation respectively, and take into account the behavior that users may re-enter another related topic during the attention phase or the participation phase freely. Data fitting using the real data of both reading quantity and forwarding quantity obtained from Chinese Sina-microblog can parameterize the model to make an accurate prediction of the COVID-19 public opinion trend until the next major news item occurs, and the sensitivity analysis provides the basic strategies for communication.

Quantifying the Influence of Delay in Opinion Transmission of COVID-19 Information Propagation: Modeling Study.

BACKGROUND: In a fast-evolving public health crisis such as the COVID-19 pandemic, multiple pieces of relevant information can be posted sequentially on a social media platform. The interval between subsequent posting times may have a different impact on the transmission and cross-propagation of the old and new information that results in a different peak value and a final size of forwarding users of the new information, depending on the content correlation and whether the new information is posted during the outbreak or quasi-steady-state phase of the old information. OBJECTIVE: This study aims to help in designing effective communication strategies to ensure information is delivered to the maximal number of users. METHODS: We developed and analyzed two classes of susceptible-forwarding-immune information propagation models with delay in transmission to describe the cross-propagation process of relevant information. A total of 28,661 retweets of typical information were posted frequently by each opinion leader related to COVID-19 with high influence (data acquisition up to February 19, 2020). The information was processed into discrete points with a frequency of 10 minutes, and the real data were fitted by the model numerical simulation. Furthermore, the influence of parameters on information dissemination and the design of a publishing strategy were analyzed. RESULTS: The current epidemic outbreak situation, epidemic prevention, and other related authoritative information cannot be timely and effectively browsed by the public. The ingenious use of information release intervals can effectively enhance the interaction between information and realize the effective diffusion of information. We parameterized our models using real data from Sina Microblog and used the parameterized models to define and evaluate mutual attractiveness indexes, and we used these indexes and parameter sensitivity analyses to inform optimal strategies for new information to be effectively propagated in the microblog. The results of the parameter analysis showed that using different attractiveness indexes as the key parameters can control the information transmission with different release intervals, so it is considered as a key link in the design of an information communication strategy. At the same time, the dynamic process of information was analyzed through index evaluation. CONCLUSIONS: Our model can carry out an accurate numerical simulation of information at different release intervals and achieve a dynamic evaluation of information transmission by constructing an indicator system so as to provide theoretical support and strategic suggestions for government decision making. This study optimizes information posting strategies to maximize communication efforts for delivering key public health messages to the public for better outcomes of public health emergency management.

Tracking and Analyzing Public Emotion Evolutions During COVID-19: A Case Study from the Event-Driven Perspective on Microblogs.

OBJECTIVE: Coronavirus disease 2019 (COVID-19) has caused substantial panic worldwide since its outbreak in December 2019. This study uses social networks to track the evolution of public emotion during COVID-19 in China and analyzes the root causes of these public emotions from an event-driven perspective. METHODS: A dataset was constructed using microblogs (n = 125,672) labeled with COVID-19-related super topics (n = 680) from 40,891 users from 1 December 2019 to 17 February 2020. Based on the skeleton and key change points of COVID-19 extracted from microblogging contents, we tracked the public's emotional evolution modes (accumulated emotions, emotion covariances, and emotion transitions) by time phase and further extracted the details of dominant social events. RESULTS: Public emotions showed different evolution modes during different phases of COVID-19. Events about the development of COVID-19 remained hot, but generally declined, and public attention shifted to other aspects of the epidemic (e.g., encouragement, support, and treatment). CONCLUSIONS: These findings suggest that the public's feedback on COVID-19 predated official accounts on the microblog platform. There were clear differences in the trending events that large users (users with many fans and readings) and common users paid attention to during each phase of COVID-19.

Incorporating message format into user evaluation of microblog information credibility: A nonlinear perspective.

The spreading of misinformation and disinformation is a great problem on microblogs, leading user evaluation of information credibility a critical issue. This study incorporates two message format factors related to multimedia usage on microblogs (vividness and multimedia diagnosticity) with two well-discussed factors for information credibility (i.e., argument quality and source credibility) as a holistic framework to investigate user evaluation of microblog information credibility. Further, the study draws on two-factor theory and its variant three-factor lens to explain the nonlinear effects of the above factors on microblog information credibility. An online survey was conducted to test the proposed framework by collecting data from microblog users. The research findings reveal that for the effects on microblog information credibility: (1) argument quality (a hygiene factor) exerts a decreasing incremental effect; (2) source credibility (a bivalent factor) exerts only a linear effect; and (3) multimedia diagnosticity (a motivating factor) exerts an increasing incremental effect. This study adds to current knowledge about information credibility by proposing an insightful framework to understand the key predictors of microblog information credibility and further examining the nonlinear effects of these predictors.