Social Media Discourse Research Articles

Authoritarian strange bedfellows? European radical right parties’ positions on China: An analysis of roll-call votes in the european parliament

This paper examines European Radical Right Parties’ (RRPs) positions on China using 209 roll-call votes from the VoteWatch Europe Dataset. While previous research suggests RRPs focus on economic issues, this study finds their interest lies in foreign and security matters. RRPs exhibit strong voting cohesion on “Support for Democratic Values” and “International Relations and Sanctions,” but are divided on “Trade and Economic Investment” and do not demonstrate a consistent voting pattern on “Global Cooperation and Development.” Overall, RRPs maintain a somewhat neutral to somewhat negative position, adopting a cherry-picking approach driven more by interests than ideology. Discrepancies between their rhetoric and voting behavior reveal a complex relationship with China, highlighting the need for further comparative studies on their social media discourse and legislative actions.

From tragedy to advocacy: how celebrity suicides transform mental health conversations on Facebook

ABSTRACT Understanding the dynamics of social media discourse is critical for advancing mental health advocacy and support. While prior research has examined increases in online search activity following celebrity suicides, little is known about how mental health conversations evolve on social media in response to these events. This study addresses that gap by analysing shifts in public discourse on Facebook before and after the suicides of four high-profile celebrities from diverse entertainment sectors. Analysing public posts from Facebook Pages and Groups, we found that personal storytelling and coping strategies emerged as the most prevalent themes, highlighting the central role of lived experiences in online mental health discourse. Moreover, these narratives were positively associated with higher levels of user engagement, suggesting that peer-driven discussions resonate strongly with audiences. We also observed notable shifts in topic salience following celebrity suicides, including increased attention to personal stories and legislative discussions. Furthermore, topics, such as education and societal challenges, served as bridges between other discussions, revealing the intersection of mental health with broader societal and structural concerns. Implications for mental health advocacy, digital engagement strategies, and policy development are discussed.

Can customers be hygiene inspectors? Leveraging social media to support health authorities in food safety monitoring

PurposeSome restaurant customers who contract foodborne illnesses do not contact public health authorities but instead post online reviews to social media. By monitoring social media discourse, health authorities can gather information updates about restaurants’ hygiene deficiencies and thereby identify potential venues for outbreaks of foodborne illness. This study proposes a social media analytics framework to analyze the associations among negative hygiene aspects mentioned in customers’ reviews and use those associations to predict restaurants’ food safety.Design/methodology/approachThis study analyzes customer reviews of restaurants and identifies the co-occurrence patterns of hygiene-related keywords. To assess the extent to which the word co-occurrences are effective in preventing foodborne illnesses, classification models were constructed to use those co-occurrences as inputs to predict restaurants’ food safety risk.FindingsThis study obtains 20 association rules that reveal the co-occurrences of hygiene-related keywords. Using those co-occurrences as inputs, our best-performing model can detect 87.58% of high-risk restaurants.Practical implicationsWhen monitoring social media, health authorities can focus on a group of keywords and deploy our model to identify restaurants that are likely to contribute to foodborne illnesses.Originality/valueThrough the lens of signaling theory, this study is a pioneering work to reduce the dimensionality of social media data to a few meaningful hygiene-related keywords, filtering out irrelevant signals that disturb the signaling process. Social media data, after being processed by appropriate machine learning algorithms, become credible signals for risk prediction.

Gaining insights into pet owner understanding/lived experience of canine chronic kidney disease using survey and social media data

IntroductionChronic kidney disease (CKD) is a common and progressive condition in dogs characterized by irreversible damage to one or both kidneys over an extended period leading to gradual decline in kidney function. Early diagnosis is crucial to improve quality of life and increase survival through medical interventions.MethodsThis study investigated pet owner understanding of this condition using insights gained by comparing pet owner survey responses with bulk harvested social media discussions on canine CKD. We combined structured survey data (n = 132) with social media analysis spanning multiple platforms to understand owner perceptions of disease characteristics, clinical sign reporting, and pet owner experiences.ResultsBoth data sources highlighted increased urination and water consumption as primary pet owner concerns, with these clinical signs showing moderate positive correlation (Pearson correlation coefficient of r = 0.66). Although not explicitly investigated within the survey, social media data demonstrated pain as a significant concerning clinical sign and revealed the emotional toll of end-of-life care considerations. Further analysis also demonstrated significant associations between CKD diagnosis in dogs and both animal age (p < 0.001) and female gender (p = 0.006), while breed group and weight showed no significant correlations.DiscussionThe complementary nature of structured surveys and social media analysis provided richer understanding of pet owner experiences, understanding and management of CKD. This combined methodological approach offers a model for investigating other chronic conditions in veterinary medicine.

A Morphological Analysis of Abbreviated Neologisms of Social Media Discourses: A Case of Kenyans on X

In recent years, the rise of social media platforms has dramatically transformed communication patterns and language use across the globe. Among these platforms, X (formerly known as Twitter) stands out as one of the most influential, having witnessed the emergence and widespread adoption of abbreviated neologisms. This paper presents the morphological analysis of abbreviated neologisms used by Kenyans on X. The objective was to analyse these abbreviated neologisms within social media discourses using the natural morphology framework (NMT) propounded by Dressler (1985). NMT is a functionalist theory that accounts for morphological preferences based on extra-linguistic motivations. Data for the study was purposively collected from Kenyans on X. The abbreviated neologisms were identified, and their meanings were determined through contextual analysis. The data was then classified based on the morphological structure of the neologisms, including the type of abbreviation, the source words or roots, and other morphemes. This provided insights on the interaction between technology and the morphological evolution of language as evidenced by the use of abbreviated neologisms by KOX. The findings reveal significant insights into the morphological features of abbreviated neologisms, shedding light on the innovative linguistic practices employed by Kenyan X users. The study highlights the role of social media platforms in language evolution, demonstrating how technology influences word formation processes. These findings underscore the broader understanding of language variation and change facilitated by social media platforms. The abbreviated neologisms are formed through initialisms, clipping, and contraction.

Public Perception And Sentiment On Social Media X Towards The Interest In Adopting Bitcoin As A Digital Asset

This study analyzes the relationship between public sentiment on the social media platform X and Bitcoin's global price volatility from January to October 2024. Using sentiment analysis supported by the BERT machine learning model and the Support Vector Machine (SVM) algorithm, relevant tweets were classified into positive, neutral, and negative sentiments. Model evaluation demonstrated excellent performance, with precision, recall, and F1-score for positive sentiment reaching 95.52%, 93.57%, and 94.53%, respectively. Neutral sentiment achieved precision of 88.61%, recall of 92.11%, and an F1-score of 90.32%. Negative sentiment yielded precision of 92.02%, recall of 91.05%, and an F1-score of 91.53%. The results indicate a significant correlation between public sentiment and Bitcoin price movements, where positive sentiment drives price increases while negative sentiment often triggers sell-offs. Moreover, the intensity of social media discussions significantly impacts market dynamics, as evidenced by a spike in activity in March 2024 coinciding with Bitcoin's price peak during the study period. These findings provide insights for investors, market analysts, and regulators to understand the role of social media as a market sentiment indicator influencing digital asset volatility.

Quantifying Formality in Economic Texts: A Novel Adaptation of the Formality Formula for Economic Data Analysis

Automatic text summarization and formality analysis have been extensively studied in linguistics and natural language processing (NLP). However, their application to economic data remains underexplored. Economic texts, such as policy documents, financial reports, and news articles, often exhibit varying levels of formality that influence decision-making and communication. This study introduces an innovative adaptation of a formality formula tailored specifically for economic data. Our modified formula incorporates numerical values, domain-specific keywords, and weighted grammatical features to quantify the formality of economic texts. By applying this formula to diverse datasets, including central bank policy statements and social media discussions, we demonstrate its effectiveness in identifying formal and informal tones. Statistical validation reveals that our approach achieves significant improvements in distinguishing formal texts from informal ones, with applications in sentiment analysis, readability assessment, and document classification. The findings underscore the importance of adapting linguistic tools to specialized domains like economics, paving the way for more nuanced text analysis in financial and policy contexts.

From Policy to Platforms: Analysing Public Engagement with Singapore's Smart Nation Initiative through Social Media Discourse

From Policy to Platforms: Analysing Public Engagement with Singapore's Smart Nation Initiative through Social Media Discourse

Sociolinguistic Features ofPoliteness inUzbek Social Media Discourse

This scholarly paper explores the sociolinguistic dimensions of politeness as manifested in contemporary Uzbek social media discourse. In light of the rapid digitalization and the growing prevalence of platforms such as Telegram, Facebook, and Instagram in Uzbekistan, the study investigates how conventional norms of politeness are recontextualized within virtual communicative domains. Anchored in foundational politeness theories and informed by sociolinguistic perspectives, the analysis delves into the nuanced employment of linguistic forms, address terms, honorifics, emoji usage, and indirect speech mechanisms. The paper foregrounds the persistent influence of socio-cultural variables—namely age, gender, and social status—on digital language practices, while also illuminating the transformative impact of global digital trends on indigenous communicative conventions. Findings suggest a dynamic interrelation between continuity and innovation, wherein traditional values are both preserved and renegotiated in response to the evolving affordances of digital interaction.

Research Advice for Early Career Transplant Infectious Disease Clinicians.

As part of an ongoing series of social media discussions, the Transplant Infectious Diseases Early Career Network hosted an open forum for the transplant infectious disease community to discuss the development of research careers for junior faculty. Topics discussed included opportunities for research, identifying potential research questions, institutional support, grant funding, common barriers to research, and trainee involvement. The forum highlighted symbiotic relationships between junior faculty and trainees. The insights from the forum provide a valuable resource for early-career transplant infectious diseases (TID) researchers.

The Impact of ESG Practices on the Valuation of Related Party M&A Assets: The Moderating Role of Digital Economy

The overvaluation of merger and acquisition (M&A) assets can lead to a decline in the performance of listed firms, an increase in the risk of goodwill impairment, and harm to the rights of minority shareholders, as well as to the sustainable development of firms. Based on stakeholder theory, this article constructs models to examine the impact of environmental, social, and governance (ESG) practices on the valuation of related party M&A assets and conducts an empirical analysis. We find that ESG practices significantly inhibit the overvaluation of related party M&A assets, and the digital economy can enhance this negative relationship. Mechanism analysis shows that this negative relationship is mediated through setting up stock performance compensation, reducing performance commitment growth rate, selecting reputable asset appraisal institutions and financial advisors, increasing analyst following and social media discussions, and reducing agency costs. Heterogeneity analysis shows that the inhibitory effect of ESG practices on the overvaluation of related party M&A assets is more obvious in non-horizontal M&A and non-state-owned enterprises. Furthermore, ESG practices can alleviate the stock price crash risk by reducing the overvaluation of related party M&A assets. The research conclusions provide a reference for ESG practices to better serve M&A activities and alleviate asset overvaluation in the digital economy era.

The Influence of Motivation and Engagement on Knowledge and Behavioral Intentions: Information Learning on Social Media During Early COVID-19 Outbreak in China

ABSTRACT Guided by the Cognitive Mediation Model (CMM), this study examined how motivations (surveillance, anticipated interaction, guidance) and engagement (attention, elaboration, discussion) with COVID-19 information on social media were associated with Chinese netizens’ knowledge acquisition and preventive behavioral intentions during the initial COVID-19 outbreak in early 2020. A survey of 1,300 respondents from five major Chinese cities revealed that guidance motivation was positively associated with elaboration, which in turn facilitated learning and preventive intentions. While surveillance and anticipated interaction motivations were significantly linked to attention, attention did not contribute to learning or intention changes. Anticipated interaction was positively associated with social media discussions, whereas surveillance motivation showed a negative association. Notably, social media discussions negatively contributed to both knowledge acquisition and intentions, suggesting their potential to impede learning China’s unique social media context during a global crisis. These findings challenge the assumption that all forms of media engagement enhance learning and decision-making, highlighting the need to consider the unique characteristics of the information environment, including platform-specific features and sociocultural factors. The study offers theoretical and practical insights into the complexities of social media engagement and its effects on public health outcomes during global crises.

Individual protective actions with social support seeking in an online health community: two observational cross-sectional studies during public health emergencies

In public health emergencies, there is a critical need for accurate informational and emotional support to counteract misinformation and trauma. Online Health Communities (OHCs) serve as essential resources for real-time health counseling and support. This study investigates how OHCs facilitate the acquisition of informational and emotional support, crucial for guiding informed protective decisions. By integrating the Protective Action Decision Model (PADM) with social support theory, the research examines the impact of disaster-related information on patients’ decision-making within OHCs, aiming to optimize these platforms for public health response and preparedness. The study utilizes a dataset comprising 602 doctor-patient consultation dialogues from a Chinese OHC. Through text and sentiment analysis, the study quantifies the volume of information and sentiment, which serve as indicators of the level of informational and emotional support sought by patients. Environmental and social cues related to emergency situations are measured using disaster early forecast information and the volume of social media discussions on the emergency. Multiple linear regression models are employed to analyze the impact of these cues on patients’ behaviors, specifically their informational-seeking and emotional-seeking actions. It indicates that social cues have an impact on patients’ seeking informational support, while only in the high-uncertainty public health emergency, environmental cues are positively correlated with patients’ seeking both emotional and informational support. Additionally, stakeholder actions in the context of OHCs positively moderate the influence of environmental and social cues on individual protective actions to some extent. This study advances the understanding of OHCs by applying and empirically testing the PADM in a digital health context. It also explores the varying impacts of different types of public health emergencies on patient behavior within OHCs. The findings can guide healthcare providers and OHC administrators in enhancing support mechanisms, particularly during public health emergencies.

“You were elected to lead the living, not the dead”: A computational analysis of social media discourse about Nigerian farmer-herder conflicts

“You were elected to lead the living, not the dead”: A computational analysis of social media discourse about Nigerian farmer-herder conflicts

Mind the gap: examining policy and social media discourse on Long COVID in children and young people in the UK

BackgroundLong COVID in children and young people (CYP) has posed significant challenges for health systems worldwide. Despite its impact on well-being and development, policies addressing the needs of CYP remain underdeveloped. This study examines UK Long COVID policies using ethical frameworks, integrating policy and social media analyses to explore public and professional concerns.MethodsA mixed-methods approach was applied. Policy documents were reviewed using Thompson et al.'s pandemic preparedness framework and Campbell and Carnevale’s child-inclusive ethical model. Social media discourse (12,650 posts) was analysed using Brandwatch™ to identify key themes around CYP and Long COVID policies. Data was collected and triangulated through the LISTEN method, which integrates policy analysis with social media discourse to ensure a holistic understanding of systemic gaps and public perceptions.ResultsAnalysis highlighted gaps in accountability, inclusiveness, and transparency in policy development. Social media data reflected significant public dissatisfaction, primarily critiquing government accountability (90% of posts) and delayed policy responsiveness (29% of posts). Key ethical challenges included limited CYP representation and unequal access to services.ConclusionsRecommendations include improving transparency, incorporating CYP perspectives in policymaking, and ensuring equitable access to care. These findings provide a foundation for ethically sound and inclusive policies addressing Long COVID in CYP.

Assessing Menstrual Stigma: A Content Analysis of Menstrual Product Posts on Two Chinese Social Media Platforms

Menstruation in China is plagued by persistent misconceptions and stigmatization. Although social media serve as a crucial information source that shapes public views and challenges misunderstandings or reinforces existing stigma, research has not yet considered how this takes place. This study compares the prevalence of menstrual stigma in menstrual product advertisements on two Chinese social media platforms, Sina Weibo and Little Red Book (Xiaohongshu), through a content analysis of 600 posts. Our findings reveal that menstrual stigma is perpetuated through various themes, textual and visual elements, celebrity endorsements, and user engagement patterns, highlighting its ongoing presence in social media discourse.

An updated social media users’ crisis response framework

PurposeThis exploratory study aims to investigate social media users’ reactions via user-generated content (UGC) to crisis-affected supply chains, through the lens of Situational Crisis Communication Theory, to: (1) ascertain whether the Social Media Users' Crisis Response (SMUCR) Framework is applicable to a prolonged, multi-brand, global crisis and (2) whether there is any evidence of online brand advocacy (OBA) and online brand detraction (OBD).Design/methodology/approachNatural language processing (NLP) with Leximancer, enabled thematic and sentiment analyses of 295,024 X (Twitter) posts extracted over a three-year period.FindingsThis study found that there were nine stages in social media users’ response to a long, global supply chain crisis. It also found sentiment coupling as positivity and negativity were not mutually exclusive and co-appeared in the UGC throughout the 3 years. However, not all positive sentiment demonstrated OBA dimensions and not all negative sentiment mirrored OBD at various stages of the crisis.Research limitations/implicationsThis study enhances the SMUCR Framework by incorporating the evolving role of social media users in shaping brand narratives during crises through OBA and OBD. It highlights the fluctuating nature of public sentiment, showing how consumer voices influence brand perception online over time. This study updates the SMUCR Framework from four to nine social media users’ crisis response stages. It sheds new light on the role which social media users play in crisis evolution and management online.Practical implicationsThe updated SMUCR Framework will enable industry practitioners to better anticipate, manage and respond to an elongated, global crisis which evidences itself via social media UGC. A fresh perspective is provided on crisis management, stressing the need to monitor and adapt to changing social media discourse to sustain brand resilience to a crisis.Originality/valueThis paper extends the original SMUCR Framework beyond a one-brand, short-term crisis scenario through a multi-brand, longitudinal, global crisis lens and evolves the Framework from four to nine stages.

Insights on the Side Effects of Female Contraceptive Products From Online Drug Reviews: Natural Language Processing-Based Content Analysis.

Most online and social media discussions about birth control methods for women center on side effects, highlighting a demand for shared experiences with these products. Online user reviews and ratings of birth control products offer a largely untapped supplementary resource that could assist women and their partners in making informed contraception choices. This study sought to analyze women's online ratings and reviews of various birth control methods, focusing on side effects linked to low product ratings. Using natural language processing (NLP) for topic modeling and descriptive statistics, this study analyzes 19,506 unique reviews of female contraceptive products posted on the website Drugs.com. Ratings vary widely across contraception types. Hormonal contraceptives with high systemic absorption, such as progestin-only pills and extended-cycle pills, received more unfavorable reviews than other methods and women frequently described menstrual irregularities, continuous bleeding, and weight gain associated with their administration. Intrauterine devices were generally rated more positively, although about 1 in 10 users reported severe cramps and pain, which were linked to very poor ratings. While exploratory, this study highlights the potential of NLP in analyzing extensive online reviews to reveal insights into women's experiences with contraceptives and the impact of side effects on their overall well-being. In addition to results from clinical studies, NLP-derived insights from online reviews can provide complementary information for women and health care providers, despite possible biases in online reviews. The findings suggest a need for further research to validate links between specific side effects, contraceptive methods, and women's overall well-being.

Beyond the Posts: Analyzing Breast Implant Illness Discourse With Natural Language Processing and Deep Learning.

Breast Implant Illness (BII) is a spectrum of symptoms some people attribute to breast implants. While causality remains unproven, patient interest has grown significantly. Understanding patient perceptions of BII on social media is crucial as these platforms increasingly influence healthcare decisions. The purpose of this study is to analyze patient perceptions and emotional responses to BII on social media using RoBERTa, a natural processing model trained on 124 million X posts. Posts mentioning BII from 2014-2023 were analyzed using two NLP models: one for sentiment (positive/negative) and another for emotions (fear, sadness, anger, disgust, neutral, surprise, and joy). Posts were then classified by their highest-scoring emotion. Results were compared over across 2014-2018 and 2019-2023, with correlation analysis (Pearson correlation coefficient) between published implant explantation and augmentation data. Analysis of 6,099 posts over 10 years showed 75.4% were negative, with monthly averages of 50.85 peaking at 213 in March 2019. Fear and neutral emotions dominated, representing 35.9% and 35.6% respectively. The strongest emotions were neutral and fear, with an average score of 0.293 and 0.286 per post, respectively. Fear scores increased from 0.219 (2014-2018) to 0.303 (2019-2023). Strong positive correlations (r>0.70) existed between annual explantation rates/explantation-to-augmentation ratios and total, negative, neutral, and fear posts. BII discourse on X peaked in 2019, characterized predominantly by negative sentiment and fear. The strong correlation between fear/negative-based posts and explantation rates suggests social media discourse significantly influences patient decisions regarding breast implant removal.

Social Media Communication in Kazakhstan: Linguistic and Stylistic Aspect

This article focuses on the study of the linguistic and stylistic features of social media communication in Kazakhstan. The research examines the functional styles used in social networks and their influence on the development of the Kazakh language. The rapid expansion of social media has significantly impacted information and cultural processes in society, leading to the emergence of new linguistic and stylistic forms and tools. Social networks provide new opportunities for both verbal and non-verbal communication. Moreover, the article analyzes the functional styles used in social networks, as well as linguistic features such as slang, abbreviations, emojis, stickers, and other linguistic tools. Social media has become an important platform for developing the use of the Kazakh language. This research helps identify the direction of the Kazakh language’s development in new communication environments by describing the linguistic characteristics of social networks. The study also explores the role of social media communication in modern society, its impact on spoken culture, and the formation of social values. The main objective of the research is to identify the stylistic, lexical, and grammatical features of Kazakh-language social media discourse and analyze their influence on the communication process. Furthermore, the article discusses the unique communicative culture and characteristics of Kazakh-language social networks, as well as how linguistic abbreviations and transformations affect general language usage in society. Social networks are shaping new linguistic and stylistic structures that influence changes in language norms. The frequency of using new linguistic tools among young people and their cultural impact are particularly significant areas of study. This research aims to demonstrate the dynamics of language and style used in social media while analyzing their influence on the development of the Kazakh language. The findings of this study allow researchers to determine how contemporary linguistic changes and new lexical tools affect the linguistic community in Kazakhstan and their contribution to the development of social and cultural norms.