Analysis Of Social Media Data Research Articles

Lay Theories of Expertise: A Mixed‐Methods Exploration

AbstractFailure to engage with expertise underpins many organizational and societal problems. Despite decades of research on expertise, we still do not fully understand why such failures persist, and there is a sense that these failures are becoming a crisis of expertise. In this article, we highlight a person's system of beliefs about the meaning of expertise – what we term their lay theory of expertise (LTE) – as an important factor for understanding their engagement with expertise. Through a free response study (Study 1), analysis of social media data (Study 2), and word sorting study (Study 3), we first develop a taxonomy of common LTE elements. We then examine how LTEs affect expert recognition via two experiments with managers (Studies 4 and 5). Study 4 reveals that congruence between the most psychologically active element of a person's lay theory and expert conduct is conducive to expert recognition, while Study 5 highlights that (in)congruence between LTEs and expert conduct can alter how other mental representations (such as gender stereotypes) shape expert recognition. Our work provides a conceptual foundation for exploring variation in the subjective meaning of expertise in future research on expert recognition and engagement, both within and beyond organizations.

Women’s Microaggressions Scale (WoMenS): A Comprehensive Sexism Scale

Sexism is pervasive but often manifests as microaggressions against women, which are subtle, covert forms of gender discrimination. Thus, we developed the Women’s Microaggressions Scale (WoMenS) based on an existing theoretical taxonomy and content analysis of social media data. Two separate studies were conducted for exploratory factor analysis (EFA) and confirmatory factor analysis (CFA). EFA supported an eight-factor, correlated structure and CFA supported a bifactor model, with eight specific factors and one general WoMenS factor. Overall, reliability and validity of the WoMenS were mostly supported in two samples. Specifically, the WoMenS subscales and body surveillance were positively correlated; the general WoMenS was positively correlated with anxiety, depression, body surveillance, and another measure of sexism but not life satisfaction. Furthermore, general WoMenS explained variance in anxiety and body surveillance above and beyond that explained by an existing sexism measure.

Sentiment analysis of social media data: Business insights and consumer behavior trends in the USA

The digital age has reformed how organizations in the USA reach out to their consumers and has opened up more avenues for understanding consumer sentiment and behavior. This research explored consumer sentiment and behavior trends through social media data with particular emphasis on platforms popular in the USA. By analyzing various social media channels, the study aimed to determine leading trends that drive consumer perception and behavior in real-time. The present research focused on the main social media platforms used in the USA: X-Twitter, Facebook, Instagram, and TikTok. Sentiment analysis data was gathered using the usage of different social media platforms for their unique features and APIs. X-Twitter, being the most useful social media platform for real-time microblogging, provided a very strong API for the analyst to access the tweets, user profiles, and engagement metrics, which is very good for gathering public sentiment and trending topics. With the high volume of users, Facebook exposed the Graph API, which allowed fetching user interactions, comments, and reactions on public posts, giving insight into consumer opinions and brand perception. Also, Instagram's API enabled the collection of visual content along with captions and engagement data, enriching the analysis with multimodal sentiment insights. Three machine learning models were used, most notably, logistic regression, random forest classifier, and XG-Boost. Strategic metrics were used to evaluate the performance of the model: accuracy, precision, recall, and F1-score. With a perfect score for the two algorithms, XG-Boost and Logistic Regression were perfectly able to classify on all metrics, while Random Forest Classifier had high scores close to the other two models, though a little lower in some metrics than the other two. The results of sentiment analysis will provide actionable insights for businesses that want to improve their positioning in the market. By interpreting the data on sentiment, companies in the USA can identify strengths and weaknesses in their offerings and make targeted improvements. Sentiment analysis has proved a crucial tool for US businesses in further improving marketing and customer engagement. Sentiment analysis will help companies in the USA create a meaningful understanding of the perceptions of the general public about its products and services.

Social Media Data Analysis for Enhancing Student Evaluation of Teaching Styles

In the realm of education, understanding the impact of different teaching styles on student engagement and satisfaction is essential. Recent advancements in sentiment analysis provide new avenues for evaluating student feedback, particularly through informal channels such as social media. While formal student evaluations offer structured feedback on teaching styles, they may not fully capture the nuanced opinions and sentiments expressed by students in informal settings, such as social media. This research aims to address the gap by integrating sentiment analysis of social media data to evaluate teaching effectiveness across various styles and comparing it with formal evaluation results. This study employs sentiment analysis using the VADER (Valence Aware Dictionary and sEntiment Reasoner) tool to analyze student posts on social media platforms. The analysis includes the extraction of sentiment distributions, identification of common keywords, and tracking of sentiment trends over time. Additionally, formal student evaluations (Likert scale) are collected to offer a direct comparison. The teaching styles analyzed include lecture-based teaching, project-based learning, flipped classrooms, online learning, hybrid learning, and traditional exam-based learning. The findings demonstrate that student sentiment varies significantly across teaching styles. Flipped classrooms and project-based learning received the highest positive sentiment scores, while traditional exam-based teaching showed the most negative sentiment. Social media feedback tended to align with formal evaluations for certain teaching styles, such as the flipped classroom and hybrid learning but showed divergence in others, like online learning, which received higher sentiment in social media feedback. Trends over time reveal evolving sentiments, with fluctuating satisfaction as the academic semester progressed. The integration of social media sentiment analysis provides a more dynamic and real-time understanding of student experiences, offering deeper insights into teaching style effectiveness.

MMTF-DES: A fusion of multimodal transformer models for desire, emotion, and sentiment analysis of social media data

MMTF-DES: A fusion of multimodal transformer models for desire, emotion, and sentiment analysis of social media data

CLOUD-BASED AI AND MULTIVARIATE OPTIMIZATION METHODS FOR REAL-TIME SENTIMENT ANALYSIS ON SOCIAL MEDIA

Social media has emerged as a widely used platform for individuals and businesses to share updates, opinions, and emotions. Real-time sentiment analysis of social media data provides valuable insights, enabling organizations to make informed, data-driven decisions. However, analyzing vast amounts of social media data in real-time presents significant challenges, requiring high computational power and advanced analytical capabilities. This is where cloud-based AI and multivariate optimization techniques become essential. Cloud-based AI leverages the scalability and speed of cloud computing to process large volumes of data efficiently in real-time. The multivariate optimization model enhances the analysis by handling complex, diverse datasets and evaluating multiple variables simultaneously. This research focuses on delivering a unified framework that performs real-time sentiment analysis, and the system integrates cloud-based AI with multivariate optimization strategies to automatically collect, process, and analyze social media data in real-time, delivering actionable insights with improved accuracy and efficiency.

FORMATION OF MULTILAYER KNOWLEDGE GRAPHS BASED ON THEMATIC TEXTS MODELING

The paper considers the problem of forming multilayer knowledge graphs for use in intelligent analysis of social media data. The technology is based on the «soft» structuring of the existing document pool in accordance with the constructed topic model. Experimental testing of the technology and comparison of the formal properties of knowledge graph models obtained using topic modeling and the traditional method were conducted. The results of the comparison indicate the potential effectiveness of using multilayer knowledge graphs built on the basis of a topic model of texts when implementing intelligent data processing procedures in the conditions of multiple and dynamic subject areas.

A Mix-method Study Leveraging Social Media for Health Behavior Change: From Tweets to Transformation

Abstract Introduction: Social media platforms have emerged as powerful tools for disseminating public health messages and influencing health-related behaviors. This study aimed to explore the role of social media in public health communication and behavior change interventions. Methods: A mixed-methods approach was employed, combining quantitative analysis of social media data, online surveys, in-depth interviews, and focus group discussions. The social media data analysis included sentiment, network, and content analysis. The study analyzed 543 social media posts and 1190 comments, and surveyed 687 respondents. Results: Official accounts had higher reach (50,000 impressions per–post) compared to user-generated content (25,000 impressions) and influencer posts (30,000 impressions). However, user-generated and influencer content exhibited higher engagement (1500 likes, comments, shares per–post vs. 800 for official accounts). Sentiment analysis revealed polarized responses, with 40% of user-generated content expressing negative sentiments, particularly toward COVID-19 vaccination and mask mandates. Among 687 survey respondents, 65% reported that exposure to public health messaging influenced their health-related behaviors. Qualitative analysis identified effective strategies such as tailoring messages, collaborating with influencers, and employing interactive content formats, while also highlighting challenges including misinformation (reported by 62% of respondents), resource constraints, and public mistrust. Conclusion: The study highlights both the potential and challenges of leveraging social media for public health communication. Considering social media’s impact on promoting positive health-related behaviors, a comprehensive framework was proposed emphasizing robust monitoring systems, cross-sector collaborations, capacity building, data-driven approaches, and ethical guidelines.

A Comprehensive Approach to Bias Mitigation for Sentiment Analysis of Social Media Data

Sentiment analysis is a vital component of natural language processing (NLP), enabling the classification of text into positive, negative, or neutral sentiments. It is widely used in customer feedback analysis and social media monitoring but faces a significant challenge: bias. Biases, often introduced through imbalanced training datasets, can distort model predictions and result in unfair outcomes. To address this, we propose a bias-aware sentiment analysis framework leveraging Bias-BERT (Bidirectional Encoder Representations from Transformers), a customized classifier designed to balance accuracy and fairness. Our approach begins with adapting the Jigsaw Unintended Bias in Toxicity Classification dataset by converting toxicity scores into sentiment labels, making it suitable for sentiment analysis. This process includes data preparation steps like cleaning, tokenization, and feature extraction, all aimed at reducing bias. At the heart of our method is a novel loss function incorporating a bias-aware term based on the Kullback–Leibler (KL) divergence. This term guides the model toward fair predictions by penalizing biased outputs while maintaining robust classification performance. Ethical considerations are integral to our framework, ensuring the responsible deployment of AI models. This methodology highlights a pathway to equitable sentiment analysis by actively mitigating dataset biases and promoting fairness in NLP applications.

Identifying Factors Associated With Heightened Anxiety During Breast Cancer Diagnosis Through the Analysis of Social Media Data on Reddit: Mixed Methods Study.

More than 85% of patients report heightened levels of anxiety following breast cancer diagnosis. Anxiety may become amplified during the early stages of breast cancer diagnosis when ambiguity is high. High levels of anxiety can negatively impact patients by reducing their ability to function physically, make decisions, and adhere to treatment plans, with all these elements combined serving to diminish the quality of life. This study aimed to use individual social media posts about breast cancer experiences from Reddit (r/breastcancer) to understand the factors associated with breast cancer-related anxiety as individuals move from suspecting to confirming cancer diagnosis. We used a mixed method approach by combining natural language processing-based computational methods with descriptive analysis. Our team coded the entire corpus of 2170 unique posts from the r/breastcancer subreddit with respect to key variables, including whether the post was related to prediagnosis, diagnosis, or postdiagnosis concerns. We then used Linguistic Inquiry and Word Count (LIWC) to rank-order the codified posts as low, neutral, or high anxiety. High-anxiety posts were then retained for deep descriptive analysis to identify key themes relative to diagnostic progression. After several iterations of data analysis and classification through both descriptive and computational methods, we identified a total of 448 high-anxiety posts across the 3 diagnostic categories. Our analyses revealed that individuals experience higher anxiety before a confirmed cancer diagnosis. Analysis of the high-anxiety posts revealed that the factors associated with anxiety differed depending on an individual's stage in the diagnostic process. Prediagnosis anxiety was associated with physical symptoms, cancer-related risk factors, communication, and interpreting medical information. During the diagnosis period, high anxiety was associated with physical symptoms, cancer-related risk factors, communication, and difficulty navigating the health care system. Following diagnosis, high-anxiety posts generally discussed topics related to treatment options, physical symptoms, emotional distress, family, and financial issues. This study has practical, theoretical, and methodological implications for cancer research. Content analysis reveals several possible drivers of anxiety at each stage (prediagnosis, during diagnosis, and postdiagnosis) and provides key insights into how clinicians can help to alleviate anxiety at all stages of diagnosis. Findings provide insights into cancer-related anxiety as a process beginning before engagement with the health care system: when an individual first notices possible cancer symptoms. Uncertainty around physical symptoms and risk factors suggests the need for increased education and improved access to trained medical staff who can assist patients with questions and concerns during the diagnostic process. Assistance in understanding technical reports, scheduling, and patient-centric clinician behavior may pinpoint opportunities for improved communication between patients and providers.

Failure-Resistant Path Selection Considering Netizens’ Sentiment Orientation Under Typhoon Disasters

After a typhoon disaster, selecting effective paths is crucial for ensuring the efficiency of emergency rescue operations and the distribution of essential supplies, which are vital for sustainable disaster response and community resilience. Current research into disaster scenarios is less aligned with actual scenarios as road conditions are hard to predict. This paper, set against the backdrop of typhoon disasters, employs netizens’ sentiment data to indirectly assess post-disaster road conditions and refine the calculation formula for road failure probabilities. This approach aims to identify failure-resistant paths to guide disaster relief decisions, thereby supporting sustainable disaster relief operations and minimizing resource expenditure. First, an expression form for road segment failure probability is established, considering factors such as tree falls, landslides, and waterlogging. Second, negative sentiment coefficients, derived from social media data analysis, are used to adjust road failure probabilities, reflecting the sentiments of affected communities. Then, a failure-resistant path selection model based on these adjusted road failure probabilities is proposed to enhance the resilience and sustainability of emergency transport paths. Finally, the model’s effectiveness is validated using Typhoon “In-Fa” in Ningbo as a case study.

Review on Application of Chi-square Statistic in Text Classification in Recent Five Years

Abstract. The swift expansion of online textual data has rendered text classification increasingly vital in information management. Despite the prevalent usage of the chi-square test in text classification, there has been a scarcity of thorough research regarding its specific uses in recent years. Therefore, it is vital to encapsulate the research about the use of the chi-square test in text classification throughout the last five years. This report reviews the application of the chi-square statistic in Arabic text classification, social media data analysis, and medical literature classification and analyses its effectiveness in feature selection and enhancing classification performance. By reviewing and analyzing the academic literature, this report summarizes the application of improved chi-square feature selection methods to different text data types. It explores the effectiveness of these methods in improving classification accuracy. The findings indicate that chi-square has significant advantages in text classification in different domains, especially when dealing with complex linguistic texts and user-generated content.

Combining text mining of social media data and conjoint approach to investigate consumer choices on organic food

The growing interest in organic food is driven by consumer concerns about the impact of conventional agricultural practices on human health, and environment. It is crucial to understand the factors that influence consumer preference of organic food. Consumer preferences for organic food has been largely investigated using traditional methods which have limitations in terms of data validity, reliability, time, and cost. This study used social media data to investigate main factors influencing consumer perceptions and preferences of organic food using X’s API. The data were analysed by text mining, and topic modelling.The results reveal that organic production, sustainability, origin, shelf-life, and price are the five most important factors that influence consumers’ choices for organic food. Organic milk was used as the representative organic food product with attributes and levels established from the social media data analysis. A conjoint analysis was used with a sample of Vietnamese consumers. Sustainability is the most important attribute affecting organic consumer food choices followed by organic production, price, shelf-life, and origin. Segmentation analysis identified two clusters of consumers: in cluster 1 sustainability is the most importance attribute while in cluster 2 organic production is the most importance one.In conclusion, this study underscored the potential of social media data as a valuable source for understanding consumer behaviour and preferences in the context of organic food consumption. In addition, this study emphasized the significance of sustainability as a critical attribute in consumer decision-making with lower environmental impact influencing consumers’ likelihood to purchase organic food products.

Introducing Landmarks: The Tugu Yogyakarta's Popularity in Literature Review and Online Media

This paper explores the cultural and urban significance of Tugu Yogyakarta, a famous monument in Yogyakarta, Indonesia. Located at the city's main crossroads, Tugu Yogyakarta is essential to Yogyakarta's Philosophical Axis, symbolizing the city's identity and pride. This analysis examines the academic discourse and social media presence of Tugu Yogyakarta, highlighting the monument's historical, cultural, and urban importance. The study emphasizes the role of social media platforms such as Instagram and TikTok in promoting the monument to a broader audience through visual and interactive content, with virtual reality (VR) technology discussed to provide virtual tours, increasing public engagement and awareness without time and distance limitations. The findings expose that academic literature, predominantly produced by Indonesian researchers, focuses on cultural heritage, urban design, and educational roles, while the topic is just starting to gain attention internationally. The social media analysis shows strong public engagement, with hashtags like #tugujogja widely used, reflecting the monument's cultural significance. This study combines a literature review with social media data analysis, making it a unique approach to comprehensively assess the monument's popularity and public awareness of its historical and cultural value. The social media data was analyzed using Gephi and Excel to calculate hashtag frequency and visualize network interactions. The findings show the critical contribution of Tugu Yogyakarta to the sustainability of cultural heritage preservation. The study highlights the synergistic relationship between academic research and the social media presence in promoting Tugu Yogyakarta, with digital platforms significantly boosting its visibility and public engagement. In conclusion, this study provides a multifaceted analysis of Tugu Yogyakarta, integrating cultural aspects, urban elements, and city marketing strategies. Successful promotion of the monument through academic works and social media can enhance heritage conservation and strengthen the public's engagement with Yogyakarta's historical landmarks.

Research on Guizhou Tourism Marketing Strategy Based on Text Mining and Social Media

With the intensification of market competition and the diversification of consumer demands, how to effectively develop and optimise marketing strategies has become the key to enhancing competitiveness and attractiveness. This paper first introduces the background and theoretical foundation of the application of text mining technology in the tourism industry and explains its importance in analysing large-scale user reviews and social media data. Then, it reveals the challenges and opportunities in the current market environment through empirical analyses of social media data related to tourism in Guizhou. Combining the methods of text classification, topic modelling and sentiment analysis, the in-depth study mines the tourists' perception and evaluation of Guizhou tourism and their influencing factors. Finally, targeted marketing recommendations are proposed, aiming to optimise the marketing strategy of Guizhou tourism and enhance its brand image and market competitiveness.

Creative City Brand Building on Social Media Platforms

With the advent of the digital age, social media has become an important tool to shape the image of a city's brand. This paper aims to explore how to use social media platforms to build creative city brands, so as to enhance the attractiveness and competitiveness of cities. Through the study of many successful cases at home and abroad, combined with the theory of social media communication and the theory of urban brand image, this paper analyzes the mechanism of social media in urban brand promotion. The research adopted a combination of quantitative and qualitative methods, including online questionnaire survey, social media data analysis and in-depth interviews, to collect and analyze relevant data. The results show that social media can not only increase the visibility of cities, but also promote cultural exchanges and economic cooperation. In addition, the study found that personalized content creation, interactive user engagement, and sustained brand storytelling are key factors for successful city branding on social media. Finally, this paper puts forward suggestions on social media marketing strategies for different types of cities, and points out that future research should pay attention to the impact of changes in social media platform algorithms on the effectiveness of urban brand communication. This study provides a practical reference for city managers to promote city image more effectively in the new media environment.

Early Detection of Mental Health Crises through Artifical-Intelligence-Powered Social Media Analysis: A Prospective Observational Study.

The early detection of mental health crises is crucial for timely interventions and improved outcomes. This study explores the potential of artificial intelligence (AI) in analyzing social media data to identify early signs of mental health crises. We developed a multimodal deep learning model integrating natural language processing and temporal analysis techniques. The model was trained on a diverse dataset of 996,452 social media posts in multiple languages (English, Spanish, Mandarin, and Arabic) collected from Twitter, Reddit, and Facebook over 12 months. Its performance was evaluated using standard metrics and validated against expert psychiatric assessments. The AI model demonstrated a high level of accuracy (89.3%) in detecting early signs of mental health crises, with an average lead time of 7.2 days before human expert identification. Performance was consistent across languages (F1 scores: 0.827-0.872) and platforms (F1 scores: 0.839-0.863). Key digital markers included linguistic patterns, behavioral changes, and temporal trends. The model showed varying levels of accuracy for different crisis types: depressive episodes (91.2%), manic episodes (88.7%), suicidal ideation (93.5%), and anxiety crises (87.3%). AI-powered analysis of social media data shows promise for the early detection of mental health crises across diverse linguistic and cultural contexts. However, ethical challenges, including privacy concerns, potential stigmatization, and cultural biases, need careful consideration. Future research should focus on longitudinal outcome studies, ethical integration of the method with existing mental health services, and developing personalized, culturally sensitive models.

#Dolphins: communication and engagement in marine mammal tourism attractions

ABSTRACT This paper delves into the ethical dimensions and public perceptions surrounding dolphin tourism, both seeing them in the wild and in the marine park. Using a comprehensive analysis of social media data over seven years, we focus on dolphins as a central element of marine mammal tourism, investigating how digital narratives shape tourist attitudes and behaviours towards these intelligent creatures. Informed by a multi-method data analysis approach, this study reveals an “echo chamber” effect in social media, perpetuating selective exposure to information that aligns with visitor motivations: hedonism, relevance and reverence. The theoretical contributions of this research extend the existing literature on wildlife tourism by offering a deeper understanding of how public sentiments towards animals in tourism settings evolve and influence tourism practices. The finding underscores the need for informed, ethical communication strategies that consider the complex interplay of entertainment, education, and conservation in wildlife tourism.

Forecasting financial market dynamics: an in-depth analysis of social media data for predicting price movements in the next day

Forecasting financial market dynamics: an in-depth analysis of social media data for predicting price movements in the next day

Citizens’ and government officials’ perceptions of commercialization in Cairo’s public parks

ABSTRACT Public park commercialization has primarily been studied in the Global North, and little is known about how it developed in the Global South. This paper uses public parks in Cairo, Egypt, to explore commercialization practices in public space. Public parks in Cairo are commercialized mainly under the pretext of helping pay for them. In this context, some citizens raise their concerns over the potential impacts of commercialization on the public availability and accessibility of parks. Using on-site observations, semi-structured interviews, and content analysis of social media data, this study aims to understand how commercialization manifests in the landscape of Cairo’s public parks, how managing authorities justify commercialization practices, and how and why some residents contest these changes. Results show that commercialization prevails in the day-to-day administration of Cairo’s public parks. By demonstrating growing public park commercialization in an important megalopolis of the Global South, we contribute to ongoing debates on the social and environmental consequences of prioritizing the parks’ exchange value over their use value.