Social Media Analysis Research Articles

Strategi Pemasaran UMKM di Era Digital: Studi Kasus Gamis Hijab Terbaru Riskirich Menggunakan Aplikasi TikTok Shop

This article discusses the digital marketing strategy used by Riskirich, an MSME operating in the Muslim fashion industry, especially robes and hijabs. The main focus of this research is the use of TikTok Shop as the main marketing and sales platform. This study aims to analyze the effectiveness of TikTok Shop in increasing brand recognition and product sales, as well as identifying the challenges and opportunities faced by Riskirich's newest hijab gamis in developing its digital marketing strategy. MSMEs in Indonesia face many challenges in marketing the products they sell, due to the COVID pandemic – 19 which occurred in 2019 to 2021 changed everything to become completely digital, so that all MSMEs in Indonesia inevitably had to market their products online. This research uses a qualitative approach with a case study method and data collection through interviews, observation and social media analysis. The research results show that using TikTok Shop significantly increases product visibility and interaction with customers, but there are challenges in terms of content management and adaptation to the platform's algorithms.

NGO Navigators, Information Support, and Parental Assistance with Pk-12 School Decisions

ABSTRACT This study provides empirical insights into the perceptions and agency of navigators who provide school selection assistance and this NGO’s unique insider–outsider status. As semi-private actors, navigators offer an important bridge between two kinds of social capital: the informal types of trust and legitimacy that families rely on, and the skills, knowledge, and networks necessary to access higher-quality schools in choice policy contexts. Data for this study are derived from an exploratory case study, including 13 in-depth interviews, documentary and social media analysis, and informal conversations. The findings illustrate the benefits and shortcomings of NGO information support in choice policy contexts.

The Influence of Social Media on College Choice among Undergraduates Majoring in Big Data Management and Application in China: Taking Guilin University of Electronic Technology as an Example

This study aims to investigate the influence of social media on college choice among undergraduates majoring in Big Data Management and Application in China. The study attempts to reveal how information on social media platforms such as Weibo, WeChat, and Zhihu influences the cognition and choice process of prospective students. By employing an online quantitative survey questionnaire, data were collected from the 2022 and 2023 classes of new students majoring in Big Data Management and Application at Guilin University of Electronic Technology. The aim was to evaluate the role of social media in their college choice process and understand the features and information that most attract prospective students. Social media has become a key factor influencing the college choice decision-making of undergraduates majoring in Big Data Management and Application in China. Students tend to obtain school information through social media platforms and use this information as an important reference in their decision-making process. Higher education institutions should strengthen their social media information dissemination, providing accurate, timely, and attractive information. It is also necessary to ensure effective management of social media platforms, maintain a positive reputation for the school on social media, and increase the interest and trust of prospective students. Simultaneously, educational decision-makers should consider incorporating social media analysis into their recruitment strategies to better attract new student enrollment. This study provides a new perspective for understanding higher education choice behavior in the digital age, particularly by revealing the importance of social media in the educational decision-making process. This has important practical and theoretical implications for higher education institutions, policymakers, and social media platform operators.

Investigating the Disclosure of Corporate Social Responsibility in International Hotel Chains

This paper examines how international hotels communicate Corporate Social Responsibility (CSR) on their websites. In particular, this study employs a corpus-based discourse approach to investigate the content and language included in the CSR reports of two international hotel chains, Marriott and Hilton. The aim is to understand the type of information reported as well as the discursive strategies employed to promote CSR performance with regards to economic, social, and environmental issues. The findings reveal several common themes in Marriott and Hilton’s CSR reports, highlighting their commitment to sustainability, ethical behavior, and community involvement. The importance of diversity and inclusion, organizational supervision, and responsibility is emphasized in the reports. Both hotel chains prioritize supporting local communities and upholding social responsibility through charitable efforts and leadership development programs. Environmental sustainability is a key focus, with efforts to adopt sustainable practices in an attempt to achieve long-term environmental goals. Further research will include a wider range of international hotel chains and incorporate social media analysis to provide a more comprehensive understanding of CSR practices in the hospitality and tourism industry.

Intangible cultural heritage versus tourism: residents’ reactions to temporal overtourism during the Day of the Dead in Mixquic, Mexico

ABSTRACT Globalisation opens small communities and their traditions to temporal overtourism, causing adaptations in the local dynamics. This study investigates responses to overtourism during the Day of the Dead in Mixquic, a semi-rural community located in Mexico City. Employing rapid ethnography, semi-structured interviews, participant observation, and content analysis of social media, it delves into local perceptions and actions regarding temporary overtourism dynamics in a global patrimonialisation context. Results reveal a complex interplay between traditional practices, community resilience, and tourist dynamics. Despite challenges, residents show a positive attitude towards tourists, often incorporating them into ancient traditions while preserving cultural authenticity. Notably, symbolic representations of tradition persist amidst external pressures, showcasing the resilience of community identity. The study underscores the importance of embracing community-centric approaches – observing and considering the community – and innovative tourism strategies to mitigate negative impacts for a better preservation of cultural heritage. By prioritising resident values and needs, tourism can lead to practices that are beneficial to both communities and visitors. Furthermore, the research highlights the importance of observing and listening to local communities.

Key Drivers of Consumption, Conceptual, Sensory, and Emotional Profiling of Cheeses Based on Origin and Consumer Familiarity: A Case Study of Local and Imported Cheeses in Greece

The origin of a product, consumer familiarity, and purchasing identity are factors that affect the perception of cheese consumption. The present study aims at identifying consumers’ conceptualizations and attitudes towards local Greek cheeses of the North-Aegean Sea islands, such as Ladotyri, Graviera, Kasseri, Kaskavali, Melichloro, and Kalathaki, some of which have a Protected Designation of Origin (PDO) status, as opposed to cheeses of non-Greek origin, such as Cheddar, Regatto, and Gouda. Sensory and emotional attributes of local, local PDO, and imported cheeses, as well as drivers associated with consumers’ choice and acceptance above and beyond their sensory attributes, were studied using three methods: (a) flash profile to gain insight into the sensory positioning of products and description of samples; (b) qualitative analysis of focus groups to pinpoint consumer knowledge, preference, and consumption criteria; and (c) a new methodology for natural language processing and sentiment analysis of social media posts to determine consumer conceptualizations. Social media posts have proven to be a valuable source of linguistic and cultural data for cheeses. Local cheeses, including PDO products, were found to be linked to village life and family gatherings, home, tradition, and childhood memories, with saltiness and hardness being their main sensory attributes. Imported cheeses were linked to fast food, pizza, and snacking, with elasticity and gumminess as prominent sensory qualities. The main criteria for purchase were intended usage and versatility, taste and texture, price, and familiarity. The findings provide key sensory attributes, information about consumer purchasing criteria, and relevant vocabulary for the promotion of cheeses as agri-food and gastronomic identity key products.

Natural Language Processing for Sentiment Analysis in Social Media Posts to Identify Suspicious Behaviour

This article reports a study on the use of the natural language processing approach in the sentiment analysis of social media posts for detecting suspicious activities involving deep learning methods. The primary objective is to design an automated system capable of identifying unfavourable sentiments and patterns that imply suspicious activity such as cyberbullying, hate speech and disinformation. The methodology involves collecting a diverse data set of 2,000 posts from platforms such as X and Facebook, pre-processed by tokenisation and normalisation. The sentiment analysis model uses a long short-term memory (LSTM) network because it finds that the LSTM network is good for recognising long-term dependencies between texts. Other heuristics are used to give prominence to posts with malicious behavioural profiles to flag them accordingly. The model is designed and tuned to pre-labelled data sets. This work shows considerable improvement over previous techniques at categorising the posts as suspicious by showing higher accuracy, precision, recall and F1 score ratios. In doing so, this research positively impacts the enhancement of paranoid security or the totality of social media safety by offering a strong early detection mechanism for reducing risky behaviours on social networks.

Using social media to share universal messages about early language and literacy development with parents and caregivers

Purpose Health promotion and prevention interventions to support children’s early language and literacy development are essential, however little is known about how social media can be used in this context. This study evaluated an intervention which aimed to build capacity of parents in supporting children’s language and literacy skills, implemented using Facebook. Method A cross-sector partnership of Australian speech-language pathologists developed a suite of universal messages for parents to support their child’s language development. Messages were delivered across 12 weeks via Facebook. A convergent mixed methods study design was used to evaluate survey responses from parents of children aged 0–5 years, before (n = 136) and after the intervention (n = 61). Social media analytics were collected. Result Participants identified social media was a helpful way to receive information about supporting their child’s language and literacy development, reporting an increase in knowledge following the intervention. Participants reported they trusted information provided in Facebook posts as it was from professionals and evidence-based sources. The usability (i.e. look and layout) was generally rated highly. Conclusion Social media is a valuable tool for speech-language pathologists to deliver universal messages. Future social media initiatives should be co-designed with families and social media professionals to ensure delivery of trusted, consistent, and user-friendly information.

Racial Biases Associated With Pulse Oximetry: Longitudinal Social Network Analysis of Social Media Advocacy Impact.

Pulse oximetry is a noninvasive method widely used in critical care and various clinical settings to monitor blood oxygen saturation. During the COVID-19 pandemic, its application for at-home oxygen saturation monitoring became prevalent. Further investigations found that pulse oximetry devices show decreased accuracy when used on individuals with darker skin tones. This study aimed to investigate the influence of X (previously known as Twitter) on the dissemination of information and the extent to which it raised health care sector awareness regarding racial disparities in pulse oximetry. This study aimed to explore the impact of social media, specifically X, on increasing awareness of racial disparities in the accuracy of pulse oximetry and to map this analysis against the evolution of published literature on this topic. We used social network analysis drawing upon Network Overview Discovery and Exploration for Excel Pro (NodeXL Pro; Social Media Research Foundation) to examine the impact of X conversations concerning pulse oximetry devices. Searches were conducted using the Twitter Academic Track application programming interface (as it was known then). These searches were performed each year (January to December) from 2012 to 2022 to cover 11 years with up to 52,052 users, generating 188,051 posts. We identified the nature of influencers in this field and monitored the temporal dissemination of information about social events and regulatory changes. Furthermore, our social media analysis was mapped against the evolution of published literature on this topic, which we located using PubMed. Conversations on X increased health care awareness of racial bias in pulse oximetry. They also facilitated the rapid dissemination of information, attaining a substantial audience within a compressed time frame, which may have impacted regulatory action announced concerning the investigation of racial biases in pulse oximetry. This increased awareness led to a surge in scientific research on the subject, highlighting a growing recognition of the necessity to understand and address these disparities in medical technology and its usage. Social media platforms such as X enabled researchers, health experts, patients, and the public to rapidly share information, increasing awareness of potential racial bias. These platforms also helped connect individuals interested in these topics and facilitated discussions that spurred further research. Our research provides a basis for understanding the role of X and other social media platforms in spreading health-related information about potential biases in medical devices such as pulse oximeters.

Predicting the Transition From Depression to Suicidal Ideation Using Facebook Data Among Indian-Bangladeshi Individuals: Protocol for a Cohort Study.

Suicide stands as a global public health concern with a pronounced impact, especially in low- and middle-income countries, where it remains largely unnoticed as a significant health concern, leading to delays in diagnosis and intervention. South Asia, in particular, has seen limited development in this area of research, and applying existing models from other regions is challenging due to cost constraints and the region's distinct linguistics and behavior. Social media analysis, notably on platforms such as Facebook (Meta Platforms Inc), offers the potential for detecting major depressive disorder and aiding individuals at risk of suicidal ideation. This study primarily focuses on India and Bangladesh, both South Asian countries. It aims to construct a predictive model for suicidal ideation by incorporating unique, unexplored features along with masked content from both public and private Facebook profiles. Moreover, the research aims to fill the existing research gap by addressing the distinct challenges posed by South Asia's unique behavioral patterns, socioeconomic conditions, and linguistic nuances. Ultimately, this research strives to enhance suicide prevention efforts in the region by offering a cost-effective solution. This quantitative research study will gather data through a web-based platform. Initially, participants will be asked a few demographic questions and to complete the 9-item Patient Health Questionnaire assessment. Eligible participants who provide consent will receive an email requesting them to upload a ZIP file of their Facebook data. The study will begin by determining whether Facebook is the primary application for the participants based on their active hours and Facebook use duration. Subsequently, the predictive model will incorporate a wide range of previously unexplored variables, including anonymous postings, and textual analysis features, such as captions, biographic information, group membership, preferred pages, interactions with advertisement content, and search history. The model will also analyze the use of emojis and the types of games participants engage with on Facebook. The study obtained approval from the scientific review committee on October 2, 2023, and subsequently received institutional review committee ethical clearance on December 8, 2023. Our system is anticipated to automatically detect posts related to depression by analyzing the text and use pattern of the individual with the best accuracy possible. Ultimately, our research aims to have practical utility in identifying individuals who may be at risk of depression or in need of mental health support. This initiative aims to enhance engagement in suicidal ideation medical care in South Asia to improve health outcomes. It is set to be the first study to consider predicting participants' primary social application use before analyzing their content to forecast behavior and mental states. The study holds the potential to revolutionize strategies and offer insights for scalable, accessible interventions while maintaining quality through comprehensive Facebook feature analysis. DERR1-10.2196/55511.

Development of an integrated model for SME marketing and CRM optimization

The development of an integrated model for optimizing marketing and customer relationship management (CRM) in small and medium-sized enterprises (SMEs) presents a crucial advancement in enhancing business competitiveness and customer engagement. This paper proposes a unified model that merges scalable software solutions for social media analytics with AI-driven CRM to provide a comprehensive framework for SMEs to enhance their marketing strategies and customer interactions. Social media platforms have become vital channels for customer outreach, engagement, and feedback, yet SMEs often face challenges in leveraging these platforms due to limited resources and expertise. Integrating social media analytics into CRM systems offers businesses the ability to analyze customer behaviors, preferences, and sentiment on a large scale. The proposed model utilizes artificial intelligence (AI) to streamline customer interactions, enabling personalized communication and improving customer retention. By incorporating AI into CRM, SMEs can automate routine tasks, such as responding to customer inquiries and segmenting audiences based on their social media activity. This allows for more efficient marketing strategies and enables businesses to proactively address customer needs, ultimately driving customer loyalty and long-term growth. Furthermore, the unified model focuses on scalability, ensuring that SMEs can adopt and adapt these technologies according to their evolving needs and resources. This paper also explores the benefits of integrating these technologies, including the potential for improved data-driven decision-making, enhanced customer experience, and increased operational efficiency. In conclusion, this integrated model presents a strategic pathway for SMEs to optimize their marketing and CRM efforts through the adoption of scalable, AI-driven tools that enhance customer engagement and boost business performance. Keywords: SME Marketing, CRM Optimization, Social Media Analytics, AI-Driven CRM, Customer Engagement, Scalable Software Solutions, Business Performance, Personalized Communication, Customer Retention, Data-Driven Decision-Making.

Public Participation Monitoring: Social Media Data Mining and Analysis of User Engagement Patterns

Social Media Platforms (SMP) like Twitter and Facebook have become public influencing medium in today’s digital age which facilitate community interactions. These necessitates the monitoring of public participation in these SMP. This work performs such an study by examining the user engagement patterns in social media in Indonesia during the national elections (#Pemilu2024), Ramadan celebrations (#Ramadan2024), and climate change discussions (#ClimateChange). The data for the study was collected during a period of six months from (January to June 2024) using event-specific hashtags and keywords. To identify the user engagement patterns datamining and Natural Language Processing (NLP) tools were utilized. The findings show that the Twitter platform has higher user engagement in morning with news updates and visual updates at evening. The Facebook show user engagement in afternoon with videos and evening engagement with shared articles. The Sentiment Analysis (SA) and network analysis was performed over the dataset and the findings have shown that higher positive sentiments towards elections and Ramadan but argumentative towards climate change discussions. The Twitter show rapid communication effectiveness compared to Facebook. Further the youth prefer faster update and older expect detailed content sharing.

Suchita Poddar on Social Media Analytics

Suchita Poddar on Social Media Analytics

IFusionQuad: A novel framework for improved aspect-based sentiment quadruple analysis in dialogue contexts with advanced feature integration and contextual CloBlock

Aspect-based sentiment analysis (ABSA) represents a crucial field of natural language processing (NLP). It focuses on deriving detailed sentiment insights from textual content. Dialogue-level aspect-based sentiment quadruple extraction (DiaASQ) is specifically concerned with pinpointing target-aspect-opinion-emotion quadruples within conversations. DiaASQ is important in industries like e-commerce, social media analytics, and customer feedback. However, Current ABSA approaches predominantly focus on single-text scenarios, often overlooking the complexities involved in sentiment analysis within conversational contexts. To fill this gap, this paper presents the IFusionQuad model, which is specifically designed for the DiaASQ task. Our contributions include the innovative integration of CloBlock in ABSA, enhancing feature representation with context-aware weights. The InteractiveNet Fusion Module further advances dialogue understanding by aggregating dialogue-specific features such as threads, speakers, and replies. Components such as CloBlock, gating mechanism, and Biaffine attention effectively mitigate data noise issues, improving the relevance of feature extraction. Empirical evaluation on standard datasets demonstrates that the IFusionQuad model outperforms baseline methods, achieving substantial improvements in quadruple extraction. Specifically, our model shows a 6.59% increase in micro F1 and a 7.05% increase in identification F1 for Chinese datasets, and a 2.65% and 4.69% increase in micro F1 and identification F1, respectively, for English datasets. The results clearly demonstrate our IFusionQuad model’s efficacy, which consistently outperforms baseline models across all evaluation datasets on the DiaASQ task.

AI For Real-Time Detection And Mitigation Of Fake News Via Multi-Source Verification

The drought of fake news in the cover of the digital age makes information quality and its influence by senses. To address this issue, this paper introduces an innovative process to counter the misinformation spread by designing a real-time detection and mitigation model which is based on an AI Environment. Existing solutions do postpublication fact-checking, but our proposed system performs cross-verification of news sources for extracting facts from publications, it observes language patterns and compares content with trusted databases, analyzing the reliability among them in real-time. We empirically show that this approach achieves a significant improvement in performance over the state-of-the-art on both the LIAR dataset and real-time social media analysis. Our results demonstrate that the system is able to attain 89% of accuracy on detecting fake news, with a processing latency lower than 1.5s per article. It provides a real-time scalable solution to detect and combat fake news, assisting in the continued fight to keep online information accurate

An Empirical Study on K-Pop Livecast Concerts in Cinema: The Next Best Experience to a Real Live Concert?

This study examines participants’ experiences of K-pop livecast concerts, a new phenomenon where audiences watch live-streamed online concerts together in cinemas. The study is in three stages—social media analysis, qualitative interview, and quantitative questionnaire survey—each generates knowledge on the experience in different dimensions. The first two stages lay the foundation for the final test of the service quality model, which illustrates the effect of service quality factors and artist attachment on satisfaction and repurchase intention. The results show that content quality, convenience, and physical interaction affect satisfaction, among which physical interaction has the strongest effect. Fandom is vital in determining repurchase intention regardless of satisfaction with the current livecast concert participation. This study predicts that livecast concerts will enjoy continuous popularity post-COVID-19 and provides practical insights into future strategies for operating and marketing livecast shows.

A Study on How Social Media FOMO (Fear of Missing Out) Impacts the Gen Z Audience

Generation Z, or Gen Z, refers to people born between 1995 and 2010, often known as "zoomers," the first generation to fully embrace the internet. This paper focuses on the impact of socialmedia-induced Fear ofMissing Out (FOMO) on Generation Z, blending with the characteristics of digital nativity and extensive engagement with social media platforms. Fear of Missing Out, or FOMO, was uniquely introduced in 2004 with the rise of social networking sites. It centers on anxiety and how, for Gen Z, this plays a pivotal role in making one feel absent from rewarding experiences. This study delves into the effects on mental health, psychological well-being, self-esteem, and life satisfaction. It explores social media analytics regarding usage, compulsive checking, behavioural consequences, following trends, and real-life social interactions. The findings highlight the need for strategies to mitigate FOMO's adverse effects, promoting healthier social media habits and fostering a more balanced approach to digital consumption.

Social media and health care: a cross-sectional analysis of X (Twitter) posts using the NodeXL platform

Introduction. The landscape of healthcare and medical education is rapidly evolving, driven by the popularity of social media platforms like Facebook, WeChat, and X (Twitter). These platforms have transformed healthcare communication, enabling trainees, medical students, and practitioners to enhance learning and clinical practice. Educators leverage social media to simplify complex medical concepts, fostering continuous learning and professional development. Despite their benefits, social media, notably X (Twitter), boasting over 186 million active users, are susceptible to misinformation, underscoring the importance of verifying data accuracy and identifying influential medical sources. This study aims to identify the most influential sources of health information through X (Twitter) analysis using the NodeXL tool. Methods. Employing a cross-sectional design, this study utilized the NodeXL platform for social media analysis. NodeXL facilitated the identification of prevalent topics in public health discussions on X (Twitter). Influencers, users, and prominent healthcare providers were categorized to assess the credibility of their information. Data collection extended up to March 1, 2024. Results. Analysis revealed that the #hospital hashtag garnered the highest number of tweets among popular healthcare hashtags (6,438 tweets, with 84% being unique). Conversely, #medicalcare and #medicine had the lowest citation counts (58 and 116 respectively). @MahaHealthIE emerged as the most famous account using the #healthservices hashtag. Notable influencers with the #hospital hashtag included YouTube, Narendra Modi, and Al Jazeera English. @GreenSwelfares led in using the #medicalcare hashtag, while YouTube and the World Health Organization (@WHO) were prominent among accounts using #medicine. Conclusion. This study underscores the significance of monitoring social media and tracking key influencers periodically. However, the study's limitation lies in its snapshot nature, failing to capture the dynamic shifts in influencer popularity trends over time.

An Evaluation of the Maternal Patient Experience through Natural Language Processing Techniques: The Case of Twitter Data in the United States during COVID-19

The healthcare sector constantly investigates ways to improve patient outcomes and provide more patient-centered care. Delivering quality medical care involves ensuring that patients have a positive experience. Most healthcare organizations use patient survey feedback to measure patients’ experiences. However, the power of social media can be harnessed using artificial intelligence and machine learning techniques to provide researchers with valuable insights into understanding patient experience and care. Our primary research objective is to develop a social media analytics model to evaluate the maternal patient experience during the COVID-19 pandemic. We used the “COVID-19 Tweets” Dataset, which has over 28 million tweets, and extracted tweets from the US with words relevant to maternal patients. The maternal patient cohort was selected because the United States has the highest percentage of maternal mortality and morbidity rate among the developed countries in the world. We evaluated patient experience using natural language processing (NLP) techniques such as word clouds, word clustering, frequency analysis, and network analysis of words that relate to “pains” and “gains” regarding the maternal patient experience, which are expressed through social media. The pandemic showcased the worries of mothers and providers on the risks of COVID-19. However, many people also shared how they survived the pandemic. Both providers and maternal patients had concerns regarding the pregnancy risks due to COVID-19. This model will help process improvement experts without domain expertise to understand the various domain challenges efficiently. Such insights can help decision-makers improve the patient care system.

Critical Analysis of the Ethical Challenges related to Data Privacy and Social Media Analytics: A Literature Review

The aim of this study is to find out the ethical challenges to social media data privacy because social media plays an information and dissemination role in our society while on the other side, it is also facing data privacy problems. The study analyzed 38 articles related to Facebook and Twitter to explore the viewpoint of the scholars regarding ethical challenges to data privacy linked to social media. The study revealed that social media users are facing data privacy problems and ethical challenges. Both have their own status and requirements regulations, rights, and laws to protect each other from any mishap in society. It is recommended that social media users read and learn all the rules & regulations and conditions whenever logging into a particular Social Network Site, the SNS providers need to clearly define their rules & regulations, and conditions to use to overcome ethical challenges.