Chinese Weibo Research Articles

Where neoliberalism shapes Confucian notions of child rearing: Influencers, experts and discourses of intensive parenting on Chinese Weibo

In this paper, we use Multimodal Critical Discourse Analysis to look at the posts of influencers using parenting hashtags on Chinese Weibo. It has been observed that motherhood more widely has become a site of discursive tension, often played out online. In this case, we find such tension between former, more Confucian roles of motherhood, related to duty and knowing one’s place, and a more recently arrived influence of neoliberalism. On the one hand, the hashtags provide a sense of community for stay-at-home mothers who have given up their careers, but on the other, the influencers configure this duty and obligation into something based on striving, individual success and self-management. They appear to play a role in helping this ideology enter into the family.

Sentiment classification of Chinese Weibo based on extended sentiment dictionary and organisational structure of comments

Sentiment classification can provide the decision support of social applications such as trend judgment, public opinion monitoring, etc. However, the accuracy of sentiment classification for Chinese Weibo is still not satisfactory due to the complexity of Chinese. In addition, affected by the different organisational structure levels, the sentiment tendency of fewer Weibo Comments may be judged to be the opposite. To solve the problem above, this paper presents a Chinese sentiment classification model based on extended sentiment dictionary and organisational structure of comments. First, the sentiment dictionary can be extended by using seven dictionaries, which include the base sentiment dictionary and six additional dictionaries. Then, the sets of rules are constructed, which include inter-sentence rules and organisational structure rules. Finally, comments on three hot topics are crawled and used to make the data sets for sentiment calculation. Accordingly, based on the result of sentiment calculation, sentiment classification is completed. The effectiveness of the proposed model is verified through comparison experiments, and the experimental results are also discussed.

Longitudinal Change of Mental Health among Active Social Media Users in China during the COVID-19 Outbreak.

During the COVID-19 pandemic, every day, updated case numbers and the lasting time of the pandemic became major concerns of people. We collected the online data (28 January to 7 March 2020 during the COVID-19 outbreak) of 16,453 social media users living in mainland China. Computerized machine learning models were developed to estimate their daily scores of the nine dimensions of the Symptom Checklist—90 (SCL-90). Repeated measures analysis of variance (ANOVA) was used to compare the SCL-90 dimension scores between Wuhan and non-Wuhan residents. Fixed effect models were used to analyze the relation of the estimated SCL-90 scores with the daily reported cumulative case numbers and lasting time of the epidemic among Wuhan and non-Wuhan users. In non-Wuhan users, the estimated scores for all the SCL-90 dimensions significantly increased with the lasting time of the epidemic and the accumulation of cases, except for the interpersonal sensitivity dimension. In Wuhan users, although the estimated scores for all nine SCL-90 dimensions significantly increased with the cumulative case numbers, the magnitude of the changes was generally smaller than that in non-Wuhan users. The mental health of Chinese Weibo users was affected by the daily updated information on case numbers and the lasting time of the COVID-19 outbreak.

Exploring the Changes of Suicide Probability During COVID-19 Among Chinese Weibo Users.

Background: Coronavirus disease 2019 (COVID-19) threatens people's physical and mental health, globally, and it may even trigger suicide ideation and suicidal behavior. Aims: We aimed to examine the impact of COVID-19 on suicide risk by sampling Chinese Weibo users and analyzing their social media messages. Method: We predicted the probability of suicide (including hopelessness, suicidal ideation, negative self-evaluation, and hostility) of Weibo users in order to assess the changes in suicide probability at different times. Repeated-measures ANOVA was performed to examine the differences in suicide probability in different regions during different periods. Results: There was no significant difference in suicide probability between profoundly infected areas (PIAs) and less infected areas (LIAs) before the outbreak of COVID-19. LIAs had an increase in hopelessness during the COVID-19 growth period, while hopelessness and hostility in PIA increased during the COVID-19 decline period, indicating potential suicide probability. Limitations: Results should be interpreted with caution, and cross-cultural research may be considered in the future. Conclusion: COVID-19 has a dynamic impact on suicide probability. Using data from online social networks may help to understand the impact pattern of COVID-19 on people's suicide probability.

Predicting Regional Variations in Nationalism With Online Expression of Disgust in China.

Disgust is one of the basic emotions and is part of the behavioral immune system, which evolutionarily protects humans from toxic substances as well as from contamination threats by outgroup members. Previous works reveal that disgust not only activates humans’ defense against potential individual and collective threats, but also leads to severe moral judgments, negative intergroup attitudes, and even conservative political orientations. As is already known, nationalism is an ideology that features both negative feelings toward outgroups and beliefs about native superiority or privileges. Evidence from previous studies suggests that disgust is related to nationalism’s several components but lacks direct research on nationalism and disgust. The current study examines the relationship between disgust and nationalism in China at both individual and regional levels. In study 1, participants temporally induced disgust (vs. control) increasing the adoption of nationalism. In Study 2, we analyzed covariation in disgust expression in the Chinese micro-blog Weibo and the nationalism index as part of an online large-scale political survey http://zuobiao.me/ at the province level across Mainland China. The results show that online expression of disgust positively predicts nationalistic orientation at the regional level. Finally, we discuss how the findings shed light on research concerning online emotion expression and potential future directions.

Women and fitness on Weibo: the neoliberalism solution to the obligations of Confucianism

ABSTRACT This paper uses a social semiotic approach to analyse the posts of women fitness experts/influencers on Chinese Weibo. On the posts, we find a harmonized world where all parts of life can be managed by making the right choices and by having a striving attitude. Here, success and happiness become tasks to be worked on. Yet this is a decontextualized world, where there is no room for actual situations and dispositions. We discuss how these representations can be related to rising neoliberal ideas, values, and identities among the new Chinese middle classes, which, in these instances, are used to create a rather overdetermined stance against more traditional Confucian women’s roles with an emphasis on caring, knowing one’s position, and kinship obligations. But the one-size-fits-all rhetoric of empowerment, getting-ahead and choice leaves little room for sharing the actual restrictions, conflicts, and struggles faced by these women.

Concerns Discussed on Chinese and French Social Media During the COVID-19 Lockdown: Comparative Infodemiology Study Based on Topic Modeling.

BackgroundDuring the COVID-19 pandemic, numerous countries, including China and France, have implemented lockdown measures that have been effective in controlling the epidemic. However, little is known about the impact of these measures on the population as expressed on social media from different cultural contexts.ObjectiveThis study aims to assess and compare the evolution of the topics discussed on Chinese and French social media during the COVID-19 lockdown.MethodsWe extracted posts containing COVID-19–related or lockdown-related keywords in the most commonly used microblogging social media platforms (ie, Weibo in China and Twitter in France) from 1 week before lockdown to the lifting of the lockdown. A topic model was applied independently for three periods (prelockdown, early lockdown, and mid to late lockdown) to assess the evolution of the topics discussed on Chinese and French social media.ResultsA total of 6395; 23,422; and 141,643 Chinese Weibo messages, and 34,327; 119,919; and 282,965 French tweets were extracted in the prelockdown, early lockdown, and mid to late lockdown periods, respectively, in China and France. Four categories of topics were discussed in a continuously evolving way in all three periods: epidemic news and everyday life, scientific information, public measures, and solidarity and encouragement. The most represented category over all periods in both countries was epidemic news and everyday life. Scientific information was far more discussed on Weibo than in French tweets. Misinformation circulated through social media in both countries; however, it was more concerned with the virus and epidemic in China, whereas it was more concerned with the lockdown measures in France. Regarding public measures, more criticisms were identified in French tweets than on Weibo. Advantages and data privacy concerns regarding tracing apps were also addressed in French tweets. All these differences were explained by the different uses of social media, the different timelines of the epidemic, and the different cultural contexts in these two countries.ConclusionsThis study is the first to compare the social media content in eastern and western countries during the unprecedented COVID-19 lockdown. Using general COVID-19–related social media data, our results describe common and different public reactions, behaviors, and concerns in China and France, even covering the topics identified in prior studies focusing on specific interests. We believe our study can help characterize country-specific public needs and appropriately address them during an outbreak.

Factors Affecting Social Media Users' Emotions Regarding Food Safety Issues: Content Analysis of a Debate among Chinese Weibo Users on Genetically Modified Food Security.

Social media is gradually building an online information environment regarding health. This environment is filled with many types of users’ emotions regarding food safety, especially negative emotions that can easily cause panic or anger among the population. However, the mechanisms of how it affects users’ emotions have not been fully studied. Therefore, from the perspective of communication and social psychology, this study uses the content analysis method to analyze factors affecting social media users’ emotions regarding food safety issues. In total, 371 tweet samples of genetically modified food security in Sina Weibo (similar to Twitter) were encoded, measured, and analyzed. The major findings are as follows: (1) Tweet account type, tweet topic, and emotion object were all significantly related to emotion type. Tweet depth and objectivity were both positively affected by emotion type, and objectivity had a greater impact. (2) Account type, tweet topic, and emotion object were all significantly related to emotion intensity. When the depths were the same, emotion intensity became stronger with the decrease in objectivity. (3) Account type, tweet topic, emotion object, and emotion type were all significantly related to a user’s emotion communication capacity. Tweet depth, objectivity, and user’s emotion intensity were positively correlated with emotion communication capacity. Positive emotions had stronger communication capacities than negative ones, which is not consistent with previous studies. These findings help us to understand both theoretically and practically the changes and dissemination of user’s emotions in a food safety and health information environment.

A novel emergency situation awareness machine learning approach to assess flood disaster risk based on Chinese Weibo

Social media emerged as an important resource of information to improve the emergency situation awareness of flooding disasters. However, the online microblog text stream is unstructured and unbalanced obviously. Given the big, real-time, and noisy flood disaster microblog text flow, a new regional emergency situation awareness model to automatic assess flood disaster risk is proposed. Firstly, according to the established online disaster event-meta frame, a multi-label classification algorithm for the flood microbloggings is constructed based on the historical dataset. This algorithm helps to assign the relevant event-meta tags to each situation microbloggings. Second, a new machine learning method for dynamic assessment of flood risk for online microbloggings is developed. The flood event-metas are considered to be feature vectors, and the four different levels of flood risk are considered to be four classes. Then, the flood risk assessment task is innovatively transformed into a multi-classification task. By the logistic regression ordered multi-classification algorithm, the dynamic quantitative evaluation of event-meta, users and regional risks is realized. Finally, the proposed model is applied in the case of the Yuyao Flood. The results of the case study show that the Yuyao Flood’s online quantitative risk assessment results are consistent with real accumulated precipitation data, which illustrate that the proposed machine learning model could realize the bottom-up automatic disaster information collecting by processing victim user-generated content effectively. Social media is proven to supplement the deficiencies of traditional disaster statistics and provide real-time, scientific information support for the implementation of flood emergency processes.

Personality expression and recognition in Chinese language usage

Personality plays a pivotal role at work. Many scholars have investigated the association between personality and language usage habits in the English corpus. Given that the Chinese language has the largest number of native speakers in the world, it is essential to analyze the pattern of personality expression in Chinese, which has garnered less attention. In this study, we used the TextMind system to examine the correlation between word categories and personality traits based on Chinese Weibo content. We also compared the results with previous studies to demonstrate the similarities and differences of personality expression between English and Chinese. Additionally, this paper established a prediction model based on machine learning methods to recognize personality. Results showed that language features were powerful indicators of personality. Finally, we made recommendations for using personality expression in the recruitment and selection.

Peer-to-peer interactions in the sharing economy: Exploring the role of reciprocity within a Chinese social network

Peer-to-peer interactions play an important role in the sharing economy and more specifically within Internet social networking sites (SNSs). However, little is known about the nature of these interactions and the role of reciprocity in the peer-to-peer exchanges facilitated by SNSs. We use the context of the Chinese Weibo SNS to explore this issue. Our research develops a conceptual model and hypotheses to examine the effects of reputation and emotion on reciprocal behaviours in the Chinese Weibo SNS. We conduct a study to test the influence of reputation on reciprocity and emotion in these peer-to-peer interactions. Our study shows that bridging behaviours, which create social status across heterogeneous groups, generate increased reciprocation as a direct effect. A second indicator of reputation, bonding behaviours that promote social resources within homogenous groups, do not have significant direct effects on intention to reciprocate. Both bridging and bonding behaviors create direct effects on evoked emotions, and emotions in turn have significant effects on the intention to reciprocate. We found that evoked emotions are an important mediator between bridging and bonding actions by one party in the sharing economy and the resulting intention to reciprocate by another party. Our findings contribute to developing a theoretical understanding of the role of reciprocity in peer-to-peer interactions in the sharing economy and more specifically in virtual environments. We show the functioning and appeal in the Chinese Weibo SNS depend heavily on reciprocal user behaviors including the exchange of emotional support, information and knowledge. Our findings show peer-to-peer interaction in SNSs create emotional responses, and these emotions mediate reciprocation. Actions by users with stronger reputations and higher status have stronger effects, which is consistent with the importance of social standing in China for fostering reciprocity in non-virtual business interactions. This suggests that offline habits are being emulated on the Internet.

“Mouren” (“Somebody”) can be you-know-who: A case study of mock referential vagueness in Chinese Weibo posts

This case study explores how and why the vague, indefinite pronoun “mouren” (literally means “somebody”) is deliberately adopted by an interlocutor to refer to a specific, definite person on Chinese social media. This interactional phenomenon, which we term “mock referential vagueness” (MRV), has been overlooked in the literature. Through analyzing the MRV use of “mouren” (herein equating to “you-know-who”) on Weibo, a Chinese social media platform, the study suggests that Chinese posters use “mouren” in a MRV manner to manage their rapport by either conveying a close relationship or avoiding direct offense. These interpersonal functions are made possible by the cancelable anonymity of “mouren”.

Inspecting Vulnerability to Depression From Social Media Affect.

Affect describes a person’s feelings or emotions in reaction to stimuli, and affective expressions were found to be related to depression in social media. This study examined the longitudinal pattern of affect on a popular Chinese social media platform: Weibo. We collected 1,664 Chinese Weibo users’ self-reported CES-D scores via surveys and 3 years’ worth of Weibo posts preceding the surveys. First, we visualized participants’ social media affect and found evidence of cognitive vulnerability indicated by affect patterns: Users with high depression symptoms tended to use not only more negative affective words but also more positive affective words long before they developed early depression symptoms. Second, to identify the type of language that is directly predictive of depression symptoms, we observed ruminations from users who experienced specific life events close to the time of survey completion, and we found that: increased use of negative affective words on social media posts, together with the presence of specific stressful life events, increased a person’s risk of developing high depression symptoms; and meanwhile, though tending to focus on negative attributes, participants also incorporated problem-solving skills in their ruminations. These findings expand our understanding of social media affect and its relationship with individuals’ risks of developing depression symptoms.

Multinational Luxury Brands’ Communication Strategies on International and Local Social Media: Comparing Twitter and Weibo

The age of social media requires multinational brands to master both global platforms where various cultures coexist, and local platforms dominated by a specific culture. Guided by the theories of high- vs. low-context culture, individualism vs. collectivism, and indulgence vs. restraint, this study examined multinational luxury brands’ communication strategies on the global platform Twitter, and the local Chinese platform Weibo. Several strategic aspects were investigated, including visual elements, creative strategy, cultural values, indulgence and restraint communication, and user generated content. The surprising results revealed the evident impact of culture and the distinct characteristics of the luxury industry.

CERG: Chinese Emotional Response Generator with Retrieval Method.

The dialogue system has always been one of the important topics in the domain of artificial intelligence. So far, most of the mature dialogue systems are task-oriented based, while non-task-oriented dialogue systems still have a lot of room for improvement. We propose a data-driven non-task-oriented dialogue generator “CERG” based on neural networks. This model has the emotion recognition capability and can generate corresponding responses. The data set we adopt comes from the NTCIR-14 STC-3 CECG subtask, which contains more than 1.7 million Chinese Weibo post-response pairs and 6 emotion categories. We try to concatenate the post and the response with the emotion, then mask the response part of the input text character by character to emulate the encoder-decoder framework. We use the improved transformer blocks as the core to build the model and add regularization methods to alleviate the problems of overcorrection and exposure bias. We introduce the retrieval method to the inference process to improve the semantic relevance of generated responses. The results of the manual evaluation show that our proposed model can make different responses to different emotions to improve the human-computer interaction experience. This model can be applied to lots of domains, such as automatic reply robots of social application.

A linguistic study of Chinese Weibo users who lost their only child

The loss of an only child is one of the most painful life events and creates tremendous change in its parents’ lives. Analyzing parents’ online language use may help to better understand their loss, especially their psychological process. This study compared the online word use of 228 lost-only-child (LOC) parents to that of their peers. We also tracked the change in word use for a subset of these parents (n = 36) quarterly during the first 2 years following their bereavement. The implications of the word use of Chinese LOC parents for mood, parent–child bond, and lifestyle are then discussed.

Recommendation of online Products Using Microblogging Information in Social Media

(Please read carefully abstract of the template). With the usage of social network like Facebook or Twitter sign-in’s as e-commerce sign-in where user are enabled to post on micro blogs linking to the e-commerce webpages, this mechanism has weakened the restrictions between e-commerce and social networking. A process of referring products to users of social networking by E-commerce sites in Cold-Start situations which is very rare is proposed where the question for knowledge isolation becomes the issue. This problem can be overcome by linking social networking and E-commerce pages as a bridge using features signs and information from neural networks adopting an improved ascent helping Tree method to move social networking users as user embedding’s. This can be done by employing a matrix factorization and trial done on Chinese micro-blogging service SINA WEIBO and Chinese B2C E-commerce website JINGDONG show that the proposed method is efficient.

Exploring the effect of Weibo opinion leaders on the dynamics of public opinion in China: A revisit of the two-step flow of communication

Using a random sample of posts on Weibo, this study content analyzes the extent to which Weibo opinion leaders’ tweets are associated with the shifts and variations of public opinion—in terms of topic salience, emotional reactions, agent of responsibility, motivation for condemnation, rhetorical techniques, conviction of blame, and emotional intensity—in the case of the Tianjin Explosion. Building upon a burgeoning research agenda examining the role of Chinese Weibo in the context of two-step flow, this study found that opinion leaders generally reinforce Weibo users’ subjective assessment (e.g., attitudes and emotional responses) of the incident. An overwhelming sleeper effect is also observed in that most posts by the opinion leaders did not contribute to peaks until 2–3 days later. All opinion leaders achieved between one and three peaks.

Willingness to follow opinion leaders: A case study of Chinese Weibo

This study examined the effects of follower number and comment tone on judgements about potential opinion leaders and their pages on Weibo, a Chinese social media site. Two experiments differentiated different types of negative comments, uncivil, critical, neutral and mixed, to assess the effects of comment tone relative to previous research. The results suggest that these manipulations have minimal effects, and there is little evidence that the overall number of followers is influential. In contrast to prior research, the readers did not seem to rely upon social cues from either the number of followers or the tone of the comments to form judgements about potential opinion leaders and their pages. Mixed evidence indicates that uncivil comments may be distinguished from merely critical comments, but neither format definitively leads to more positive evaluations. The potential implications for opinion leadership in China and worldwide are considered.

Improving explicit term matching with implicit topic matching for short text conversation

ABSTRACTShort Text Conversation (STC) is the task of retrieving comments made to a post and reuse them as appropriate comments to a later post. Large vocabulary variations and very limited length of posts and comments pose great challenges to traditional retrieval techniques of matching terms. To address these problems, we propose a new technique of re‐ranking comments initially retrieved through explicit term matching by using evidence of implicit topic matching gained from topic models of large collections of posts and comments. The key of our technique lies in (1) combining each post with the comments responding to it into a longer pseudo‐document for topic modeling purposes, and (2) carrying out implicit topic matching by comparing the most probable topics contained in the topic composition of a comment and that of the query post. Experiments using the NTCIR‐13 STC Chinese WeiBo collection showed that with properly tuned parameters, our technique can lead to improved retrieval effectiveness. The paper analyzes and discusses the results and outlines future work.