Sina Weibo Research Articles

Effects of heuristic and systematic cues on perceived content credibility of Sina Weibo influencers: the moderating role of involvement

The present study enhances our understanding of followers’ perceptions of the information credibility of Chinese social media influencers. Employing the heuristic-systematic model, we examined the influence of source credibility and argument quality on the content credibility of micro-influencers and their relational impact on followers’ attitudes and behavioural decisions, with involvement as a moderator. Chinese respondents who follow beauty influencers on Sina Weibo were targeted. The respondents were contacted by posting a web link on WeChat to the survey created on Sojump. Structural equation modelling was used to examine the relationship between variables. The results revealed that argument quality (i.e. systematic cue) and source credibility (i.e. heuristic cue) are significantly affect the information credibility perceived by consumers. The findings also indicate that perceived information credibility has a significant impact on brand/video attitude and purchase intention. There are notable theoretical extensions to the literature on information processing, attitude and consumer behaviour.

The evolution of online public opinion on earthquakes: a system dynamics approach

Earthquakes endanger lives and properties and trigger widespread online public opinion. If the government fails to implement emergency measures quickly, the risks associated with this online public opinion may escalate and present significant challenges in managing it. To solve this issue, this paper investigates the M5.7 earthquake in Yibin, China, as a case study, drawing data from China’s Sina Weibo. It also develops an evolutionary model of earthquake online public opinion using the life cycle theory of public opinion dissemination, public opinion crisis management theory, and system dynamics methods. This study chooses a system dynamic model because it can capture overall trends and feedback cycles. This approach provides a comprehensive view of the evolution of the whole system’s behavior and public opinion during the crisis. This model analyzes the evolution of the online public opinion of the earthquake from the perspective of the earthquake, netizens (social media users), media, and government. This study explores the differences between online public opinion by adjusting the size of different variables in the model. This provides an entry point for better control of the development and communication of earthquake online public opinion. The findings indicate that, following the earthquake, the media exerted the most significant influence on public opinion, mainly through media participation, diffusion, and impact, demonstrating a significantly positive correlation with the magnitude of public opinion. Moreover, the simulation revealed that the government is leading in preventing and controlling online public opinion. This study presents the following innovations: (1) It builds a comprehensive online public opinion model for earthquakes, which provides insights for studying online public opinion regarding other sudden natural disasters. (2) It employs system dynamics to simulate earthquake online public opinion, providing countermeasures and recommendations for the government to manage and regulate this discourse effectively.

Promotion of the 2022 Olympic Winter Games on Chinese and Western social media

PurposeThe 2022 Olympic Winter Games in Beijing were the first sporting mega-event held in a country that limits access to popular Western social networks. Since both domestic and international audiences were crucial for the organizers, the purpose of this study was to identify similarities and differences in content published and engagement generated on the event’s official social media accounts in the months preceding the Olympics.Design/methodology/approachFor 1,200 consecutive original posts published on Facebook, Sina Weibo and Twitter, we coded the time of publication, motivation targeted by the message according to Raney’s typology of motivations for sport spectatorship, multimedia additions and number of likes/reactions, comments and shares.FindingsAll accounts were dominated by posts targeting cognitive motivations. The Weibo profile was much more active and followed. There, 71.5% of content conveyed learning, while the most engaging content provided entertainment. Content on Western networks was more varied but virtually duplicated across networks. The most engaging posts targeted euphoric stress, escape and aesthetic pleasure. Comparable content elicited different engagement on Chinese and Western social media. Graphics and video were the most engaging features on all networks; polls were the least engaging.Research limitations/implicationsAgenda-setting effects of social media content differ across cultures and are co-created by user comments.Originality/valueTo the best of the authors’ knowledge, this is the first cross-cultural comparison of pre-event social media promotion by non-Western sporting mega-event hosts. Organizers diversified content strategies to cater to different audiences and pursue different policy goals. We proved cultural differences in content preference and engagement and showed the relevance of Raney’s typology of sport spectatorship for social media.

Emoji multimodal microblog sentiment analysis based on mutual attention mechanism.

Emojis, utilizing visual means, mimic human facial expressions and postures to convey emotions and opinions. They are widely used in social media platforms such as Sina Weibo, and have become a crucial feature for sentiment analysis. However, existing approaches often treat emojis as special symbols or convert them into text labels, thereby neglecting the rich visual information of emojis. We propose a novel multimodal information integration model for emoji microblog sentiment analysis. To effectively leverage the emoji visual information, the model employs a text-emoji visual mutual attention mechanism. Experiments on a manually annotated microblog dataset show that compared to the baseline models without incorporating emoji visual information, the proposed model achieves improvements of 1.37% in macro F1 score and 2.30% in accuracy, respectively. To facilitate the related research, our corpus will be publicly available at https://github.com/yx100/Emojis/blob/main/weibo-emojis-annotation .

Constraining of homogenized information on online spreading

The spread of homogenized information on social networks can create certain positive promotional effects. However, if it is excessively spread, it may lead to constraining effects and even cause negative impacts. In this paper, a spreading model called C-SIRI is proposed, which conducts an in-depth study on the constraining mechanism in the process of information spreading, as well as the rules and characteristics affecting its impact. Taking the COVID-19 epidemic information spreading on Sina Weibo social network from March 2020 to May 2022 as an example, the reliability of this model is verified. The results show that content homogeneity, participation degree and false proportion have a significant influence on the constraining mechanism of social network information spreading, and the threshold of information spreading constraining is closely related to the spreading times. This research helps to understand the role of constraining mechanism in information spreading and promote the application of social networks in information spreading prediction and control.

Navigating the COVID-19 Health Crisis: A Content Analysis of Organizational Communication Strategies on Social Media

The COVID-19 pandemic has posed an unprecedented threat to global health and well-being, with social media emerging as a pivotal platform for organizations to disseminate outbreak-related information swiftly, thereby shaping public risk perceptions and influencing preventive behaviours. Guided by the Crisis and Emergency Risk Communication (CERC) model, this study examines COVID-19-related content posted by organizations on Sina Weibo, focusing on the communication strategies employed and their impact on public engagement. Using content analysis and Semantic Network Analysis (SNA), a dataset of 10,996 salient Weibo posts was collected and analysed. Findings reveal that word prevalence, inter-word relationships, and the use of CERC model elements varied across different crisis stages and organization types. The analysis indicates that posts predominantly emphasized case reports, responders, governmental interventions, and positive outcomes. Furthermore, in terms of public engagement, certain content dimensions—such as requests for feedback, expressions of appreciation, and provision of detailed informational resources—were particularly effective in drawing public attention.

Using online negative emotions to predict risk-coping behaviors in the relocation of Beijing municipal government

This article explores the use of online negative emotions to predict public risk-coping behaviors during urban relocation. Through a literature review, the paper proposes hypotheses that anticipate advanced prediction of public risk-coping behaviors based on online negative emotions. The study’s empirical focus is on the relocation of the Beijing municipal government, using time series data for Granger causality analysis in EViews 10.0 software. Data on online negative emotions is sourced from Sina Weibo. After data cleaning, 1420 pieces of data related to the relocation policy of the Beijing Municipal Government within the period from June 9, 2015 to April 28, 2019 are retained. while risk-coping behaviors are measured through public information search behaviors and the incidence of violent crimes, the data coverage is also from June 9, 2015 to April 28, 2019. The results indicated that: (1) Online negative emotions regarding the relocation policy predict public risk-coping behaviors in advance. (2) Negative comments are more effective predictors than negative feelings; (3) Negative emotions about relocation policy formulation predict risk-coping behaviors better than those related to policy effectiveness and implementation; (4) Negative emotions from individuals better predict public risk-coping behaviors than those from institutions; (5) Negative emotions from key stakeholders better predict public risk-coping behaviors than those from non-key or marginal stakeholders. It is recommended that relevant departments establish a real-time monitoring system to track negative public opinions and emotions expressed online, adopt a stakeholder-centric approach to facilitate communication, and promote transparency and educational campaigns to address the challenges of urban relocation. In future studies, methods such as expanding the sample size and adding indicators will be used to address the limitations of potential bias in sample data.

Will Public Health Emergencies Affect Compensatory Consumption Behavior? Evidence from Emotional Eating Perspective.

This research examines the correlation between the COVID-19 pandemic and the desire to engage in compensatory consuming behaviors, specifically emphasizing emotional eating as a psychological coping strategy, particularly with respect to snacks and sweets. Conducting sentiment analysis by using a Natural Language Processing (NLP) method on posts from Sina Weibo, a leading Chinese social media platform, the research identifies three distinct phases of consumer behavior during the pandemic: anxiety, escapism, and compensatory periods. These stages are marked by varying degrees of emotional eating tendencies, illustrating a psychological trajectory from initial shock to seeking comfort through food as a means of regaining a sense of normalcy and control. The analysis reveals a notable increase in posts expressing a desire for compensatory consumption of snacks and sweets in 2020 compared to 2019, indicating a significant shift towards emotional eating amid the pandemic. This shift reflects the broader psychological impacts of the crisis, offering insights into consumer behavior and the role of digital platforms in capturing public sentiment during global crises. The findings have implications for policymakers, health professionals, and the food industry, suggesting the need for strategies to address the psychological and behavioral effects of natural disasters.

Evolution and response of flood disaster network public opinion based on an event logic graph: A case study of rainstorms in Henan, China

Social media takes the center stage as the primary information and opinion-sharing platform for the public during disaster events. Related topics quickly spread, and multiple public opinion events exhibit clear coupling phenomena, leading to the formation of network public opinion. By creating a network public opinion event logic graph, our study illustrates the development and evolution of network public opinion during disaster events and provides the government with a crucial foundation for action. For this study, we selected tweets on Sina Weibo regarding the "Henan rainstorm" between July 19, 2021, and August 2, 2021. To construct a network public opinion event logic graph, pattern matching was used to discover event relationships and extract causal event pairs and sequential event pairs. Moreover, the term frequency-inverse document frequency+K-means clustering algorithm (TF-IDF+K-Means) clustering algorithm was used to group event pairs with high similarity into one category. The critical nodes and evolution pathways in the event logic graph were then thoroughly examined. Our findings demonstrated that the topic evolution of network public opinion was multidirectional in nature, covering topics such as disaster events, emergency rescue, public sentiment, media opinion, and livelihood-related events. The two main components of the evolutionary pathway of network public opinion are the evolution of the disaster itself and its evolution in the social sphere. Our study provides a technique for determining the dynamic evolution of network public opinion and provides useful consultation for the government's upcoming response to network public opinion during disaster events.

Multi-media Textual Information, COVID-19 Sentiment and Bond Spread

Few studies examine how sentiment across multimedia platforms affects corporate bond prices. Using eight million pieces of textual information from multi-media platforms, including official news media, the Sina Weibo blog, and WeChat, we compute COVID-19 sentiment indices. We find that an increase in negative sentiment significantly raises corporate bond spread, which cannot be explained by changes in fundamentals. Moreover, we highlight the role of consistency of sentiment across platforms. An increase in consistency of sentiment reduces corporate bond credit spreads but amplifies the impact of investor sentiment on bond spreads.

Mapping perceived sentiments in university campuses with varied landscape metrics and climatic conditions.

A sustainable university campus should accommodate students to experience positive emotions, which can be evoked by sustainable landscape with green and blue spaces (GBS). This effect is location-dependent because local vegetative type is climate-determinative, but evidence is not sufficient for sentiments of people experiencing campus landscapes. Forty-seven university campuses were selected along a latitudinal gradient in mainland China, and 100 subjects were chosen per campus (50 indoor and 50 outdoor). Photos of the subjects' faces on Sina Weibo were collected. Facial expressions were assigned happy, sad, and neutral scores (n = 4,334). The average temperature (AveT) and blue space area (BlueA) showed negative relationships with latitude, thereby generating neutral emotion scores for subjects at indoor and outdoor locations. The ratio of green space area to host campus was the only landscape metric that depressed the presentation of happiness and enhanced sadness levels. Large water bodies should be built on campuses to induce calmness, and a high ratio of green spaces should be avoided. Mapping results show that campuses in eastern regions (Beijing and Liaoning) tend to elicit positive sentiments more frequently.

Creative appropriation of offline linguistic landscapes on Chinese social media: the case of playful mockery of universities on Sina Weibo

ABSTRACT This paper investigates the appropriation of offline linguistic landscapes (LLs) on Chinese social media, with a particular focus on how these LLs are creatively deployed for playful mockery and disruption of existing social order. Specifically, we present detailed analyses of a blog post and its comments to illustrate how Weibo users creatively appropriate offline LLs to playfully mock universities. Drawing on frameworks of Geosemiotics and “ambient affiliation”, we examine the semiotic mechanisms through which the playful mockery is realized and how such mockery is perceived by the audience. The findings show that the creative appropriation of offline LLs is realized via three strategies – resemiotization, emplacement, and homonym. Some users align with the “mockery” bond by showing their amusement, whereas others promote the bond by posting similar cases of mockery to engage potential readers in the digital practice. We argue that these creative semiotic practices not only reflect users’ agency of exploiting the playful potential of social media for expressing their sentiments, but may also deconstruct the general portrayal of universities as the “ivory tower” of talent cultivation and knowledge production. This may, in turn, diminish the prestige of universities and risk creating an opposition between the public and these institutions.

Identity Construction in Fan Community: An Analysis of Online Discourse among (G)I-DLE Fans in Mainland China on Sina Weibo

Fandom has become a social and cultural phenomenon reflecting practical activities in the fan community. The prevalence of social media and online fan communities has facilitated the transnational industries of Korean popular culture (K-pop) in mainland China. Fans are enthusiastic about creating posts dedicated to their idol(s) or music bands to broaden the influence of the celebrities and establish personal connections with them. The unique interaction patterns in the K-pop fan community reflect the process of fans’ identity identification and construction. This article explores how mainland fans employ discursive strategies to construct their identity and legitimize their behaviors on social media. A total of 80 posts in (G)I-DLE Super Topic made by mainland China netizens have been collected from Sina Weibo. The findings illustrate the construction of both individual and collective identities among fans, which are shaped by various contexts and different communicative purposes. Further analysis explores the potential impact of fans' identities within online communities on the K-pop market in mainland China.

Identity Construction in Fan Community: An Analysis of Online Discourse among (G)I-DLE Fans in Mainland China on Sina Weibo

Fandom has become a social and cultural phenomenon reflecting practical activities in the fan community. The prevalence of social media and online fan communities has facilitated the transnational industries of Korean popular culture (K-pop) in mainland China. Fans are enthusiastic about creating posts dedicated to their idol(s) or music bands to broaden the influence of the celebrities and establish personal connections with them. The unique interaction patterns in the K-pop fan community reflect the process of fans’ identity identification and construction. This article explores how mainland fans employ discursive strategies to construct their identity and legitimize their behaviors on social media. A total of 80 posts in (G)I-DLE Super Topic made by mainland China netizens have been collected from Sina Weibo. The findings illustrate the construction of both individual and collective identities among fans, which are shaped by various contexts and different communicative purposes. Further analysis explores the potential impact of fans' identities within online communities on the K-pop market in mainland China.

Attitudes and preferences of the Chinese public towards products made from recycled materials: A text mining approach

Products made from recycled materials, as an environmentally friendly option, have attracted public attention. Understanding public attitudes and preferences towards such products is crucial for their market development. In order to explore the Chinese residents’ attitude and emotional tendency, we analyzed public discussions and topics on products made from recycled materials from Sina Weibo. Meanwhile, LDA and BERT deep learning models were employed to assess the level of public attention, hot topics, and sentiment tendencies in China. Results show a significant upward trend in public interest in products made from recycled materials. Although the fact that most people have a positive attitude towards the environmental concept of products made from recycled materials and support such products, there is not a significant demand for products made from recycled materials, i.e., there is a green attitude-behaviour gap. Results further reveal that factors such as the price of products made from recycled materials, branding, and public skepticism about the sustainability of the products are the main reasons for the gap between environmental awareness and consumer behaviour. By analyzing the public's cognitive structure and psychological characteristics regarding products made from recycled materials through social media data, this paper can help recycling manufacturers understand consumers' needs and expectations, as well as assist policymakers in formulating better circular economy policies.

Feasibility of Emergency Flood Traffic Road Damage Assessment by Integrating Remote Sensing Images and Social Media Information

In the context of global climate change, the frequency of sudden natural disasters is increasing. Assessing traffic road damage post-disaster is crucial for emergency decision-making and disaster management. Traditional ground observation methods for evaluating traffic road damage are limited by the timeliness and coverage of data updates. Relying solely on these methods does not adequately support rapid assessment and emergency management during extreme natural disasters. Social media, a major source of big data, can effectively address these limitations by providing more timely and comprehensive disaster information. Motivated by this, we utilized multi-source heterogeneous data to assess the damage to traffic roads under extreme conditions and established a new framework for evaluating traffic roads in cities prone to flood disasters caused by rainstorms. The approach involves several steps: First, the surface area affected by precipitation is extracted using a threshold method constrained by confidence intervals derived from microwave remote sensing images. Second, disaster information is collected from the Sina Weibo platform, where social media information is screened and cleaned. A quantification table for road traffic loss assessment was defined, and a social media disaster information classification model combining text convolutional neural networks and attention mechanisms (TextCNN-Attention disaster information classification) was proposed. Finally, traffic road information on social media is matched with basic geographic data, the classification of traffic road disaster risk levels is visualized, and the assessment of traffic road disaster levels is completed based on multi-source heterogeneous data. Using the “7.20” rainstorm event in Henan Province as an example, this research categorizes the disaster’s impact on traffic roads into five levels—particularly severe, severe, moderate, mild, and minimal—as derived from remote sensing image monitoring and social media information analysis. The evaluation framework for flood disaster traffic roads based on multi-source heterogeneous data provides important data support and methodological support for enhancing disaster management capabilities and systems.

Depression Detection on Mandarin Text through Bert Model

Depression is currently one of the most prevalent mental disorders and its incidence has been rising significantly in Malaysia amid the Covid-19 pandemic. While previous studies have demonstrated the potential of artificial intelligence technology in analysing social media texts to detect signs of depression, most of these studies have focused on English textual content. Considering that Mandarin is the second most widely spoken language worldwide, it is worthwhile to explore depression detection techniques specifically tailored for Mandarin textual content. This research aims to examine the effectiveness of the BERT model in text classification, particularly for detecting depression in Mandarin. The study proposes the utilization of the BERT model to analyse social media posts related to depression. The model is trained using the WU3D dataset, which comprises a collection of over 2 million text data sourced from Sina Weibo, a prominent Chinese social media platform. Given the dataset's inherent imbalance, text augmentation techniques were employed to assess whether they contribute to improved model performance. The findings suggest that the BERT model trained on the original dataset outperformed the model trained on the augmented dataset. This implies that the BERT model is well-equipped to handle imbalanced datasets effectively. Furthermore, it is speculated that the augmented dataset did not introduce novel information or knowledge during the model training process. Notably, the highest-performing model achieved an impressive accuracy rate of 88% on the testing dataset.

The discursive construction of tradition and modernity in counter-homophobic discourses in China

ABSTRACT This study investigates the counter-homophobic discursive construction of tradition and modernity in China, focusing on Sina Weibo users’ comments on anti-same-sex marriage arguments. The study reveals how proponents of same-sex marriage in China actively imagine and project a China that is inclusive of gay rights by (re)constructing China’s tradition as tolerant of homosexuality and positioning China’s pursuit of development and modernity as entailing gay rights development. Yet, analysis shows that the discursive construction of tradition may potentially romanticize China’s tradition as if it were devoid of homophobia, and the discursive construction of modernity, by representing China’s gay rights development as merely playing catch-up with the purportedly already modernized West, sometimes implicitly produces a homonationalist binary of modernism versus traditionalism. However, from a postcolonial transnational perspective, the paper argues that contrary to the homocolonialist representation of China as essentially homophobic, these discursive practices may work partially to confront and contest Western exceptionalism.

Use of Offensive Animal Metaphor as an Interactional Activity in Online Forum Discussions

ABSTRACT This paper investigates offensive animal metaphors in blog comments about the management of donations of money and medical relief during the coronavirus pandemic in China. Rather than understanding the metaphorical usage of language as a cognitive process, we consider its situational usage as a social action and invoke insights from Conversation Analysis. Based on data retrieved from Sina Weibo, we show how discussants use animal metaphors to accomplish varying actions and construct intelligibility between themselves and others about the relationship between actions, moral accountability, and description of social events. We therefore argue that CA with its focus on topicality, action and societal membership can complement the cognitive understanding of the proximity between the source and the target domains.

The Relationships between Influencers and Followers Who Troll and Proactive‐Reactive Trolling

ABSTRACTLittle is known about the differences between trolling by influencers and followers, the difference between proactive and reactive trolling, and the relationship between them. Based on a content analysis of 1,386 comments on 160 posts of 19 influencers during a Sina Weibo trolling event, we found that influencers troll more frequently than their followers and they troll proactively more often than reactively, while followers troll reactively more often than proactively. Influencers generated less but more impactful content compared with their followers. In both proactive and reactive trolling, influencers derailed the discussion while followers provoked through their proactive trolling and insulted through their reactive trolling. These findings extend the research scope of trolling asymmetry and establish theoretical connections between social media roles and proactive‐reactive trolling.