Analysis Of Social Media Data Research Articles

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.

Visual Analysis of Social Media Data on Experiences at a World Heritage Tourist Destination: Historic Centre of Macau

The burgeoning field of sustainable tourism in historically significant urban districts has derived increasing attention in tandem with broader urban economic development. The perceived image of a tourist destination significantly affects tourist consumption behaviors and destination choices, while negative evaluations provide destination managers with insights for targeted improvements to tourist amenities, thereby contributing to the sustainable development of these historically significant districts. This study leveraged Python 3.12.3 software to collect online textual data from popular tourism websites, specifically Ctrip 8.71.4 and Mafengwo 11.1.7. Then, ROST CM6.0 software facilitated high-frequency word analysis, network semantic analysis, and sentiment analysis to outline the perceived image and characteristics of the Macau Historic District, a UNESCO World Heritage site, from the perspective of tourists. The findings are as follows: (1) The image of the Historic Centre of Macao as a tourist destination comprises four primary dimensions: cultural (16.02%), landscape (30.85%), emotional (16.97%), and local (36.16%). (2) Tourists perceive the Historic Centre of Macao as a blend of dynamism and tranquility, tradition, and modernity. (3) Emotional evaluations of the Historic Centre of Macao as a tourist destination are primarily positive (85.45%), whereas negative sentiments constitute 5.76%, with recurring themes pertaining to overcrowding, high prices, a lack of interactive experiences at attractions, excessive commercialization, and insufficient public rest facilities. These findings offer valuable directives for the future planning and development of tourism in Macau, a city recognized for its World Heritage status. (4) In summary, the thematic image of the Historic Centre of Macao can be considered authentic, romantic, and commercialized.

Assessment of visual quality and social perception of cultural landscapes: application to Anyi traditional villages, China

The assessment of landscape visual quality (LVQ) holds significant importance in the preservation and advancement of traditional villages. One challenge in measuring human perception lies in establishing a connection between public preferences and landscape characteristics. This study conducted an analysis of social media data from Anyi traditional villages in China to address this issue and identified eight human perceptions: naturalness, ancientness, colorfulness, variety, uniqueness, ingenuity, vividness, and pleasantness. A total of thirty characteristic indicators with potential explanations for LVQ were determined by research group through field investigations. A questionnaire survey was developed to assess human’s preferences using 82 traditional village photos, and scores for the eight perceptions were obtained. The logistic regression was employed to establish distinct perception models, with perceptions serving as the dependent variables and characteristic indicators as the independent variables. Nomograms were subsequently utilized to visualize regression results and display the correlation between these two factors. The findings suggest that nomograms facilitate intuitive determination of the weights assigned to characteristic indicators in perceptual models, as well as their influence on LVQ. This work provides a reference for decision-making related to the adaptive protection and development of traditional villages, thereby helping to enhance the competitiveness of tourist destinations.

Analyzing online forums to identify information needs and knowledge gaps in patients with left ventricular assist devices: a qualitative study.

To explore the knowledge and unmet informational needs of candidates for left ventricular assist devices (LVADs), as well as of patients, caregivers, and family members, by analyzing social media data from the MyLVAD.com website. A qualitative content analysis method was employed, systematically examining and categorizing forum posts and comments published on the MyLVAD.com website from March 2015 to February 2023. The data was collected using an automated script to retrieve threads from MyLVAD.com, focusing on genuine questions reflecting information and knowledge gaps. The study received approval from an ethics committee. The research team developed and continuously updated categorization matrices to organize information into categories and subcategories systematically. From 856 posts and comments analyzed, 435 contained questions representing informational needs, of which six main categories were identified: clothing, complications/adverse effects, LVAD pros and cons, self-care, therapy, and recent LVAD implantation. The self-care category, which includes managing the driveline site and understanding equipment functionality, was the most prominent, reflecting nearly half of the questions. Other significant areas of inquiry included complications/adverse effects and the pros and cons of LVAD. The analysis of social media data from MyLVAD.com reveals significant unmet informational needs among LVAD candidates, patients, and their support networks. Unlike traditional data, this social media-based research provides an unbiased view of patient conversations, offering valuable insights into their real-world concerns and knowledge gaps. The findings underscore the importance of tailored educational resources to address these unmet needs, potentially enhancing LVAD patient care.

EXPRESS: Spring Forward = Fall Back? the Effect of Daylight Saving Time Change on Consumers’ Unhealthy Behavior

Prior research documents deleterious consequences of the annual clock change to daylight saving time in many contexts, but little is known about the effect the policy has on consumer behavior. While policy debates around ending seasonal clock changes continue, millions of consumers worldwide are potentially adversely affected by the time change. Drawing on the notions of sleepiness and self-control, the authors propose a framework of how the onset of daylight saving time increases unhealthy behavior. The hypotheses are tested via two studies cast in the difference-in-differences modeling framework capturing consumption before and after the time change and across consumers who experience the transition versus who do not. Results of the first study suggest that the onset of daylight saving time increases calorie consumption from packaged snacks that are largely unhealthy, specifically in the evening and on cloudy days. The effect of the end of daylight saving time is not significant, suggesting an overall asymmetric effect of the time change on unhealthy behavior. Study 2 reveals that the onset of daylight saving time decreases fitness center visits, particularly for consumers without healthy consumption habits and with high transaction costs. Analysis of social media data suggests that consumers find the time change disruptive. Overall, the findings imply that public policy makers and businesses should find ways to support consumers around the onset of daylight saving time.

SARS-CoV-2 variant introduction following spring break travel and transmission mitigation strategies.

University spring break carries a two-pronged SARS-CoV-2 variant transmission risk. Circulating variants from universities can spread to spring break destinations, and variants from spring break destinations can spread to universities and surrounding communities. Therefore, it is critical to implement SARS-CoV-2 variant surveillance and testing strategies to limit community spread before and after spring break to mitigate virus transmission and facilitate universities safely returning to in-person teaching. We examined the SARS-CoV-2 positivity rate and changes in variant lineages before and after the university spring break for two consecutive years. 155 samples were sequenced across four time periods: pre- and post-spring break 2021 and pre- and post-spring break 2022; following whole genome sequencing, samples were assigned clades. The clades were then paired with positivity and testing data from over 50,000 samples. In 2021, the number of variants in the observed population increased from four to nine over spring break, with variants of concern being responsible for most of the cases; Alpha percent composition increased from 22.2% to 56.4%. In 2022, the number of clades in the population increased only from two to three, all of which were Omicron or a sub-lineage of Omicron. However, phylogenetic analysis showed the emergence of distantly related sub-lineages. 2022 saw a greater increase in positivity than 2021, which coincided with a milder mitigation strategy. Analysis of social media data provided insight into student travel destinations and how those travel events may have impacted spread. We show the role that repetitive testing can play in transmission mitigation, reducing community spread, and maintaining in-person education. We identified that distantly related lineages were brought to the area after spring break travel regardless of the presence of a dominant variant of concern.

Fine grain emotion analysis in Spanish using linguistic features and transformers

Mental health issues are a global concern, with a particular focus on the rise of depression. Depression affects millions of people worldwide and is a leading cause of suicide, particularly among young people. Recent surveys indicate an increase in cases of depression during the COVID-19 pandemic, which affected approximately 5.4% of the population in Spain in 2020. Social media platforms such as X (formerly Twitter) have become important hubs for health information as more people turn to these platforms to share their struggles and seek emotional support. Researchers have discovered a link between emotions and mental illnesses such as depression. This correlation provides a valuable opportunity for automated analysis of social media data to detect changes in mental health status that might otherwise go unnoticed, thus preventing more serious health consequences. Therefore, this research explores the field of emotion analysis in Spanish towards mental disorders. There are two contributions in this area. On the one hand, the compilation, translation, evaluation and correction of a novel dataset composed of a mixture of other existing datasets in the bibliography. This dataset compares a total of 16 emotions, with an emphasis on negative emotions. On the other hand, the in-depth evaluation of this novel dataset with several state-of-the-art transformers based on encoder-only and encoder-decoder architectures. The analysis compromises monolingual, multilingual and distilled models as well as feature integration techniques. The best results are obtained with the encoder-only MarIA model, with a macro-average F1 score of 60.4771%.

Social Media Data Analytics in the Automotive Industry: A Study of the Interactive Impact of Marketing Strategies and User Ratings

Social Media Data Analytics in the Automotive Industry: A Study of the Interactive Impact of Marketing Strategies and User Ratings

A General Overview of Recent Big Data Applications and Social Media Platforms

The exponential expansion of technology in recent years and rising data production are primarily changing the way businesses and individual industries are working. Technical advances have led to a major amount of data related to various fields which is currently generated in our daily life. Governments, businesses, and research institutions are beginning to understand how crucial it is to use this data to further their missions. As a result, Big Data research has become increasingly popular among academics working in various fields of study. We provide an overview of big data and its potential applications across a range of industries in this paper. Social media feeds are full of impending for analysis and data mining. Platform providers are beginning to limit unrestricted access to this kind of data as they become aware of its potential. This change presents significant difficulties for social inventers and other nonprofit researchers that examine public posts in an effort to better understand human behavior and enhance human welfare. This paper pursues to outline some of the topical modifications in social media data analysis. Some of the most influential and popular social media platforms are also discussed.

Empirical Study of the Impact of Social Media Data Analytics on Marketing Strategy

Social Media Data Analytics (SMDA) has emerged as a dynamic and growing field across various disciplines, including marketing. However, practitioners and researchers in the marketing domain have realized that harnessing the full potential of SMDA for guiding marketing strategies necessitates a clear understanding of the relevant SMDA metrics. A significant challenge lies in the lack of clear guidance on which SMDA metrics are most relevant for enhancing marketing strategies. This study aims to empirically evaluate the impact of SMDA on marketing strategies. To achieve this goal, the study carried out a questionnaire for data collection and employs an empirical investigation using the PLS-SEM methodology. The results show that the impact of SMDA on marketing strategy depends on SMDA metrics (data type, platforms and analysis methods) and also on marketing strategy type. The results suggest a valid conceptual model introducing novel metrics for the SMDA concept. These results present a broader perspective on how SMDA affects marketing strategies and suggest that future research should focus on a specific type of marketing strategy and study SMDA metrics in a different and more in-depth way.

Social Media and the DIY Politics in Thailand's 2023 Election

This article argues that Thailand's 2023 parliamentary election was the first election in which social media played a decisive factor in the electoral outcomes. Prior to this election, social media was an important campaign tool, but it was unclear whether it made a difference in the electoral results. Based on our original post-election survey data (n = 1,249), social media was the most important media in governing vote choice. Social media was a crucial space for activation and conversion—motivating the undecided to become partisans and converting partisan voters to shift their allegiances. Thailand's 2023 election was also marked by a rise in the personalization of political campaigning, wherein citizens felt free to decide how and what their political participation looked like, and parties that encouraged inclusive and open engagement with politics were best poised to win in the electoral arena. Drawing on social network analysis of social media data, this article demonstrates how the Move Forward Party's (MFP) loosely structured and inclusive social media campaigns allowed both their candidates and supporters to mobilize individualized large-scale collective action, in contrast to their rivals who focused on traditional top-down style campaigning. Despite the MFP's winning social media campaigning that produced electoral victories, the party was unable to come to power due to an entrenched authoritarian political system designed to maintain the power of the country's autocratic elites. The Thai case demonstrates powerfully how autocrats might lose an election due to social media, yet still manage to hang on to power through entrenched authoritarian institutions.

Trends in sentiment of Twitter users towards Indonesian tourism: analysis with the k-nearest neighbor method

This research analyzes the sentiment of Twitter users regarding tourism in Indonesia using the keyword "wonderful Indonesia" as the tourism promotion identity. The aim of this study is to gain a deeper understanding of the public sentiment towards "wonderful Indonesia" through social media data analysis. The novelty obtained provides new insights into valuable information about Indonesian tourism for the government and relevant stakeholders in promoting Indonesian tourism and enhancing tourist experiences. The method used is tweet analysis and classification using the K-nearest neighbor (KNN) algorithm to determine the positive, neutral, or negative sentiment of the tweets. The classification results show that the majority of tweets (65.1% out of a total of 14,189 tweets) have a neutral sentiment, indicating that most tweets with the "wonderful Indonesia" tagline are related to advertising or promoting Indonesian tourism. However, the percentage of tweets with positive sentiment (33.8%) is higher than those with negative sentiment (1.1%). This study also achieved training results with an accuracy rate of 98.5%, precision of 97.6%, recall of 98.5%, and F1-score of 98.1%. However, reassessment is needed in the future as Twitter users' sentiment can change along with the development of Indonesian tourism itself.

Trends in sentiment of Twitter users towards Indonesian tourism: analysis with the k-nearest neighbor method

This research analyzes the sentiment of Twitter users regarding tourism in Indonesia using the keyword "wonderful Indonesia" as the tourism promotion identity. This study aims to gain a deeper understanding of the public sentiment towards "wonderful Indonesia" through social media data analysis. The novelty obtained provides new insights into valuable information about Indonesian tourism for the government and relevant stakeholders in promoting Indonesian tourism and enhancing tourist experiences. The method used is tweet analysis and classification using the K-nearest neighbor (KNN) algorithm to determine the positive, neutral, or negative sentiment of the tweets. The classification results show that the majority of tweets (65.1% out of a total of 14,189 tweets) have a neutral sentiment, indicating that most tweets with the "wonderful Indonesia" tagline are related to advertising or promoting Indonesian tourism. However, the percentage of tweets with positive sentiment (33.8%) is higher than those with negative sentiment (1.1%). This study also achieved training results with an accuracy rate of 98.5%, precision of 97.6%, recall of 98.5%, and F1-score of 98.1%. However, reassessment is needed in the future as Twitter users' sentiments can change along with the development of Indonesian tourism itself.

Mitigating healthcare supply chain challenges under disaster conditions: a holistic AI-based analysis of social media data

A key advantage of social media is the real-time exchange of views with large communities. In disaster situations, such bidirectional information exchange is most useful to victims and support teams, especially in communications with authorities, volunteers, and the public. This paper addresses challenges faced by the healthcare supply chain during the COVID-19 pandemic with analyses of Twitter data using an Artificial Intelligence-driven multi-step approach. We investigate tweets for information about healthcare supply chains, such as the scarcity of testing kits, oxygen cylinders, and hospital beds during the pandemic. We deployed machine learning to classify such tweets into imperative and non-imperative categories based on need severity. The study sought to predict the location of victims requesting help based on their imperative tweets if geo-tag information was missing. The proposed approach used four steps: (1) keyword-based informative tweet search, (2) raw tweet pre-processing, (3) content analysis to identify tweet trends, public sentiment, topics related to healthcare supply chain challenges, and crisis classification to label imperative and non-imperative tweets, (4) locating the point-of-crisis from imperative tweets to facilitate coordination of help operations. The pre-processing of tweets, trend analysis, and sentiment analysis relied on natural language processing and machine learning for topic modelling (K-mean clustering), crisis classification (random forest), and point-of-crisis detection (Markov chain). Results demonstrate the potential to capture significant, timely, and actionable information on healthcare supply chain challenges to respond quickly and appropriately in a pandemic.

A machine learning approach for detecting customs fraud through unstructured data analysis in social media

Goods and services are sold through social media by individuals not authorized as legitimate dealers, resulting in lost taxes and customs duties to governments. This study proposes a model called SHIELD for detecting these violations through unstructured data in social media. The process involves collecting 2,373,570 records of commercial goods from social media platforms such as Twitter and Facebook in three phases. In Phase 1, keywords for labeling are collected for text classification. Three categories of results are defined: Red Line for smuggled goods, unpaid duty, prohibited goods, and restricted goods; Green Line for non-commercial goods; and Inspect for goods that cannot be identified from the text and require further investigation. Phase 2 and Phase 3 use keywords to detect smugglers from unstructured social media data for labeling grouped by three algorithms of Logistic Regression (LR), Gated Recurrent Unit (GRU), and Long Short-Term Memory (LSTM), employed to classify imported illegal products. The results of all tests show that the LSTM technique had the best accuracy of 99.44% and the best average F1 score of 90.55%. Using algorithms and techniques such as LR, GRU, and LSTM demonstrates the potential of machine learning and natural language processing in detecting illegal activities and promoting economic security.

Disastrous Event and Sub-Event Detection From Microblog Posts Using Bi-Clustering Method

Social media has become a nondetachable part of our life, with the exponential growth of usage in the past decade. Social sites like Twitter, Facebook, Instagram, Flickr, Weibo, etc., with their millions of user base, apart from being a source of entertainment, has proven to be a very useful mean for public opinion generation, news propagation and information broadcasting by authorities. Social media data analysis has been a popular research area for the past few years. Detecting subevents from social media posts to identify an unusual event that requires special attention, especially in a disaster situation, is one of the key researches in this domain. In this article, we have proposed a novel biclustering-based subevent detection method from the Twitter dataset for retrospective analysis of disaster events. First, we have clustered the data matrix using spectral co-clustering. Then we identified subevents (words) and formulated a ranking framework to find the top-ranked subevents within the clusters. Finally, through statistical analysis, we have shown that the proposed framework works better than other existing subevent detection methods.