Social Media Interactions Research Articles

Suicide thoughts screening with social media using cat swarm-intelligent adaptive recurrent network

The research on detecting suicidal ideation through social media in early stages and faces several difficulties. Many persons with a propensity for suicide express their ideas and beliefs on social media. Numerous studies have found that accessible social media posts have useful criteria for rapidly identifying people thosewho are considering suicide. Finding and comprehending the risk factors and warning signals that may lead to suicide, is the most challenging aspect of suicide prevention. By automatically detecting the abrupt changes in a user behavior, this can be accomplished. Social media interactions can be used to gather textual and behavioral data using natural language processing (NLP) techniques. The framework can use this data to spot trends in social interactions that point to suicide intent. We can anticipate suicidal ideation using classification techniques based on deep learning. We can accomplish this by identifying these emotions in user postings using a combination of Cat Swarm-Intelligent Adaptive Recurrent Network (CSI-ARN) models. Adding extra training data and using an attention model to improve model performance can increase accuracy. This study examines social media comments for suicidal thoughts using a CSI-ARN model. The suggested model outperformed baseline models with 90.3% accuracy and 92.6% F1-score.

Mutual Expected Rationality in Online Sharing: An Agent-Based Model Study

AbstractModels of content-sharing behavior on online social media platforms typically represent content spread as a diffusion process modeled on contagious diseases; users’ behavior is modeled with single-agent decision theory. However, social media platforms are interactive spaces where users care about reactions to, and further spread of, the content they post. Thus, social media interaction falls under the intended use cases for game theory. In contrast to existing models leaving strategic reasoning out, we capture agents’ social media decisions within a cognitive hierarchy framework, which can be interpreted as making formally precise how agents make strategic choices based on mutual expectations of rationality. Analytically, we identify limit cases in which a platform can be swamped with content that no agents personally like but all expect to elicit reactions (think obvious fake-news). We then use agent-based simulations to show that a range of more realistic cases give rise to similar outcomes.

On being green in the social media interactivity’s information seeking-sharing: Case of Generation Z in Indonesia

Social media interactivity creates consumer’s space of information seeking-sharing where its intensity could produce knowledge, creates new values and changes behavior. The aim of this study is to exploratory investigate the dual role of Generation Z’s information seeking-sharing behavior within green context through the interactive space of social media as a resource for the development of social media marketing strategy. The research employs mixed-method approach of qualitative-explorative data mining, quantitative cross-tabulation Chi-Square test, and integration. Two findings of this research are elaborated. First, consumer’s space of information-seeking leads to the process of green awareness rationalization, i.e., how environment-oriented actions can be rationalized. Second, consumer’s space of information-sharing leads to green social values, i.e., How environment-oriented actions can be socially recognized. The marketing implications of these two findings are business’ efforts to develop green-oriented strategic mindset through space of social media marketing “customer engagement” where the dual role of information seeking-sharing within green context is facilitated.

Do Customers Foster Engagement Through Social Media Interaction? Brand Prominence as a Moderator

ABSTRACT Social media considerably impacts how customers attain information and share their experiences or choose their services/products including hotel brands. Based on this significance, we test a theoretical framework that inspects the association among social-media-interaction (SMIN), customer-brand-engagement (CBEN), self-brand-congruence (SBCO), co-creation, and revisit intention toward hospitality brands following service-dominant-logic and self-congruence-theory. We also test the moderating role of brand prominence among the proposed associations. The present research employs a sample of 315 luxury hotel-customers by operating PLS-SEM. The results reported that SMIN and SBCO have positive impacts on CBEN. Results also unveiled the CBEN’s significant influence on consumer’s co-creation and their revisit intent. The results illustrated the SMIN’s and SBCO’s indirect effect on co-creation as well as on revisit intention, while mediated through CBEN. Finally, the findings exposed a significant positive moderating effect of hotel-brand prominence in proposed links. This study concludes by summarizing crucial theoretical/practical implications for hospitality brands.

Research on the Impact of Agricultural Products' Social Media Interaction on Consumers' Purchase Intentions

In the digital economy era, social media's influence on customers' purchase intentions is steadily growing due to its increased interactivity for agricultural products. This paper utilizes the following steps: first, it examines relevant literature regarding the relationship between social media and consumers' intention to purchase agricultural products; second, it analyzes the current state of development and issues surrounding this relationship; finally, it uses the SWOT and PEST analysis models to discuss the current state of affairs and issues surrounding this relationship. Lastly, several solutions and recommendations are provided to improve communication, boost product sceneries, foster an experience-first environment, reinforce engagement, and raise the standard of information sharingr.

Assessing the information needs of patients implanted with a left ventricular assist device via analysis of social media forums

Abstract Background Left Ventricular Assist Devices (LVADs) serve as a feasible treatment alternative for patients with end-stage heart failure who do not respond sufficiently to maximal medical therapy. An increasing number of individuals with end-stage heart failure, both pre- and post-LVAD implantation, are utilizing social media to find medical information and peer support. Purpose The objective of this study was to identify unmet education and information needs of patients with LVAD through social media interactions. Methods In this research, information was gathered from the forum section of the website MyLVAD.com through an automated extraction technique. We conducted an anonymous collection and analysis of a dataset from MyLVAD.com, which comprised 459 forum threads and 3886 messages across the entire forum, contributed by 945 unique users. To establish a matrix addressing existing information and educational needs, we examined 867 posts and conversations that contained questions and inquiries, ultimately identifying a total of 435 concrete questions. A series of categorization matrices were developed and updated to facilitate the discovery of new categories. Results Numerous information gaps and unanswered questions were discovered in relation to the following areas: clothing, complications/adverse events, LVAD pros and cons, self-care, therapy, and recent LVAD implantation. Among the 435 questions, 216 (49.7%) questions had a primary focus on self-care for patients with LVAD, including subjects such managing the driveline site, understanding equipment functionality, and adapting lifestyle habits to ensure safety and health while living with an LVAD. Two other major question categories emerged: those regarding complications/adverse effects, comprising 80 questions (18.4%), and those discussing the pros and cons of LVAD, with 67 questions, accounting for 15.4%. Conclusion The social media data retrieved from MyLVAD.com is the first to be used to assess the unmet needs and knowledge gaps among patients implanted with LVAD and develop categories to describe LVAD patient knowledge and information needs. Unlike investigator-generated data, social media data reflect the natural and organic conversation between patients, untainted by investigator agendas or biases. Social media data can enhance our understanding of patient information deficiencies. This can lead to the creation of materials specifically aimed at fulfilling unaddressed educational requirements.Unmet LVAD patients information needs

What Does Media Say about Mental Health: A Literature Review of Media Coverage on Mental Health

As global attention to mental health issues increases, its media portrayal becomes increasingly important. This literature review explores the representation of mental health in the media. The study found that the media plays a key role in shaping the public’s views on mental health issues. Positive media reports can help increase public awareness, reduce stigma, and promote help-seeking behaviors and policy improvements. However, negative reports may deepen misunderstandings and prejudices, increase stigma, and hinder people from seeking help. Several topics including violence and threats have become common themes in media reports on mental health issues. In addition, the portrayal of mental health on social media is emphasized, which provides a platform for discussing mental health issues, but also brings challenges to information authenticity and privacy protection. Social media interactions can provide support for mental health issues, but negative comments and cyberbullying may exacerbate the problem. The significance of this literature review is to provide theoretical support for improving media coverage and formulating public strategies, calling on media practitioners to report mental health issues in a responsible manner to promote healthier public attitudes and more effective policy interventions.

Exploring the impact of negative online feedback on well-being: A comprehensive analysis incorporating Big-Five personality traits and physiological responses

This study investigates the dynamics between social media engagement and well-being, employing a multi-dimensional analysis encompassing psychological, behavioral, social media usage patterns, and physiological perspectives. Through the utilization of electrodermal activity, our study identified a statistically significant increase following exposure to a negative comment. In addition, analysis of facial expressions revealed a variety of reactions, including laughter, irritation, sadness, and neutrality, once participants were confronted with the negative comment. Our analysis revealed intriguing insights into the relationship between personality traits and social media interactions. We observed that individuals with higher agreeableness were more likely to contemplate deactivating social media platforms. Conversely, lower levels of neuroticism were associated with self-reported social media addiction, while extraversion showed a positive correlation with increased posting frequency. These findings highlight the intricate relationship between individual personality traits and reactions to negative social media experiences, offering valuable insights into the complex dynamics of online interactions and their effects on overall well-being.

Predicting Customer Sentiment in Social Media Interactions: Analyzing Amazon Help Twitter Conversations Using Machine Learning

Social media platforms, particularly Twitter, have become essential sources of data for various applications, including marketing and customer service. This study focuses on analyzing customer interactions with Amazon's official support account, "@AmazonHelp," to understand and predict changes in customer sentiment during these interactions. Using the Twitter API, we extracted English-language tweets mentioning "@AmazonHelp," pre-processed the data, and categorized conversations to facilitate analysis. The primary objectives were to classify changes in customer sentiment and predict the overall sentiment change based on initial sentiment. We conducted experiments using multiple machines learning algorithms, including K-nearest neighbor, Naive Bayes, Artificial Neural Network, Bayes Net, Support Vector Machine, Logistic Regression, and Bagging with RepTree. Our dataset comprised over 6,500 conversations, filtered to include those with four or more tweets. Results indicated that K-nearest neighbor and Support Vector Machine offered the best balance between accuracy and F-measure, while Bagging with RepTree achieved the highest accuracy but had a lower F-measure. This study demonstrates the potential of integrating sentiment analysis and machine learning to effectively predict customer sentiment in social networks, providing valuable insights for improving customer engagement strategies.

The Role of Digital Identity in the Age of Social Media: Literature Analysis on Self-Identity Construction and Online Social Interaction

This study investigates the role of digital identity in the age of social media, focusing on how self-identity is constructed and how it influences online social interactions. The primary objective is to qualitatively analyze the literature on digital identity to understand the mechanisms by which individuals construct their online personas and the implications for social interactions in digital spaces. The research employs a qualitative literature review methodology, synthesizing findings from academic articles, theoretical papers, and empirical studies on digital identity and social media interactions. The literature review methodology involves systematically collecting, reviewing, and analyzing a wide range of scholarly sources that address the construction of digital identity and its impact on social behavior. The study categorizes the literature into key themes, such as identity formation, self-presentation, privacy concerns, and the psychological effects of online interactions. By examining diverse perspectives and case studies, the research provides a comprehensive overview of the current understanding of digital identity dynamics. The findings reveal that digital identity is a complex, multifaceted construct influenced by both individual and social factors. Social media platforms provide a space for individuals to curate and present idealized versions of themselves, which can affect self-esteem and social validation. The literature indicates that while digital identity offers opportunities for self-expression and community building, it also raises concerns about authenticity, privacy, and the potential for identity fragmentation. The role of social media in shaping identity highlights the importance of digital literacy and the need for policies that protect users' online privacy and identity. In conclusion, the study underscores the critical role of digital identity in contemporary social interactions and the necessity for ongoing research into its implications for mental health, social behavior, and privacy. Future research should focus on longitudinal studies to track the evolution of digital identity over time and investigate strategies to promote healthy identity practices in the digital age.

Interaksi Media Sosial Terhadap Niat Beli Merek Produk Fashion Lokal ‘Erigo’: Pengaruh Kredibilitas Sumber, Homofili Sumber, Dan Kualitas Konten Sebagai Variabel Mediasi

The purpose of this research is to analyze the factors influencing social media interaction regarding the purchase intention of the local fashion brand Erigo. The methodology employed in this study utilizes a quantitative approach with purposive sampling techniques. The criteria for respondents in this research are active Instagram users from the millennial and Generation Z cohorts, aged 19-36, residing in Indonesia. The valid number of respondents is 150. Data processing is carried out using the PLS-SEM method. The variables examined in this study include social media interaction, source credibility, source homophily, content quality, and purchase intention. The findings of this research indicate that social media interaction has a positive influence on source credibility, source homophily, and content quality. Source homophily and content quality positively affect purchase intention. However, source credibility does not have a significant impact on purchase intention. The results of this research are expected to assist companies and marketers in developing more intimate relationships with consumers and encouraging consumer intent to purchase through social media interaction.

Analysing social media with an Improved K-Means Clustering Algorithm

Nowadays, sharing human social behavior and growing a multi-user network requires the use of social media. Analysis of social media provides a valuable opportunity to study human social activity at scale. People often bring and talk about different kind of topics on social media platforms. depending on which, discussions are made to show the positive as well dis positives ways. This is an interesting point about using social media. There is a great deal of information about the type and substance of these discussion holography’s that informs us more broadly around patterns in social media interactions, and how content flows between individuals. Data mining techniques exists which can be utilized to crunch out user information from social media and relationships existing within the network. However, state of the art methods often fail to model user communities and their behaviors at a correct level. To solve this problem and help successfully do the grouping of related information, in our work here we present an enhanced fuzzy means clustering approach. Using this technique reduces the creation and time complexity of clusters, resulting in higher quality group outcomes. Our proposed system aims at mitigating the problems as described above by introducing a scalable and effective solution for real-time cluster social media data.

Human Behavior Analysis Using Machine Learning

The study and analysis of human behavior have been longstanding challenges in various fields such as psychology, sociology, and anthropology. With the advent of advanced technologies and the proliferation of data, machine learning has emerged as a powerful tool for deciphering and understanding complex human behaviors. This research aims to explore the application of machine learning techniques in the analysis of human behavior, leveraging diverse datasets and cutting-edge algorithms to gain insights into individual and collective actions. The research methodology involves the collection of multidimensional data sources, including but not limited to social media interactions, physiological measurements, and environmental factors. These datasets are preprocessed and integrated to create a comprehensive representation of human behavior. Machine learning models, ranging from traditional statistical methods to deep learning architectures, are then applied to uncover patterns, correlations, and predictive insights from the data. Machine learning algorithms can analyze patterns in behavior to identify potential signs of mental health issues, enabling early intervention and treatment. Tracking behavioral data can help in managing chronic conditions by providing insights into lifestyle choices, adherence to treatment plans, and overall well-being. Machine learning models can identify unusual patterns of behavior, which is crucial for security purposes. This can include recognizing suspicious activities in public spaces or flagging unusual behavior in online environments. The purpose of this study is to explore the challenges of multiple attribute decision-making when dealing with intuitionist fuzzy information. In this scenario, the attribute weights are not entirely known, and the attribute values are represented by intuitionist fuzzy numbers. To determine the attribute weights, an optimization model is constructed based on the traditional grey relational analysis (GRA) fundamental principles. The proposed method involves calculating the grey relation degree between each alternative and the positive-ideal solution and negative-ideal solution. This degree is then used to define a relative relational degree, which enables the ranking of all alternatives simultaneously with respect to both the positive-ideal solution (PIS) and negative-ideal solution (NIS). From the result 22 is ranked at first position and 45 is ranked at fifth position

Use of social media for academic and professional purposes by gynecologic oncologists

ObjectiveTo investigate the prevalence and patterns of social media use among gynecologic oncologists for professional and academic purposes.MethodsA prospective online survey between November and December 2022 targeted gynecologic oncology practitioners...

Digital Battlegrounds: The Interplay of Social Media, State Power, and Influencers in Türkiye’s Earthquake Response

This study examines the critical role of Twitter (X) in crisis communication during the earthquake that struck Türkiye on 6 February 2023, focusing on how two prominent influencers, Haluk Levent and Oğuzhan Uğur, effectively utilized the platform in response to the disaster. Analysis of highly retweeted posts and engagement rates for tweets collected using related keywords—“ahbap,” “babalatv,” “haluklevent,” and “oguzhanugur”—reveals how Twitter served not only for functions such as sharing locations for rescue operations, information dissemination, organization, and fundraising but also for defense against attacks and criticism of the government. Furthermore, Twitter emerged as a digital battleground where power struggles unfolded between the state and activist influencers. This study addresses this oversight by examining how Twitter functions not merely as a tool for communication and aid coordination but also as an arena of political contestation, filling a critical gap in understanding the role of social media. It underscores the critical need to consider the underlying political dynamics that social media interactions bring to the forefront during emergencies.

Program Literasi Digital dan Etika Media Sosial bagi Pelajar

This Community Service Program aims to improve digital literacy and understanding of social media ethics among high school (SMA) or equivalent students in Buahbatu District, Bandung. With the rapid development of technology, students are increasingly exposed to various risks in the digital world, such as cyberbullying, misinformation, and privacy violations. This program is designed to address two main issues: the lack of awareness and knowledge among students about digital security, and the low understanding of ethics in social media usage. Through an interactive educational approach, the program teaches students how to protect themselves in the digital world, including practicing good security habits and effectively managing privacy settings. Additionally, students are taught to understand and apply ethical principles in their social media interactions, including responsibility in sharing content, respecting copyright, and demonstrating empathy toward others. It is expected that after participating in this program, students will be more proactive in safeguarding their digital security and more ethical in their social media usage, thereby reducing incidents such as cyberbullying and the spread of misinformation. The program also aims to foster a more positive social media culture among students, where interactions are based on mutual respect and shared responsibility.

Investigating the Dynamics of Social Media Addiction and Well-Being in Jordan: An Empirical Analysis

This study examines the complex associations among social media usage, engagement, addiction and subjective well-being. Employing a sophisticated framework that integrates both first- and second-order models, this study employs structural equation modeling (SEM) to analyze data from a sample of 510 Jordanian young people. The findings indicate a surprisingly positive correlation between social media usage and engagement and their effects on social media addiction and subjective well-being. Conversely, social media addiction is found to have a negative connection with subjective well-being. These insights are crucial for experts aiming to improve user experiences and increase well-being. This study contributes to the current literature by offering new perspectives on the dynamics between social media interactions and personal well-being.

A Structural Path Analysis Bangladeshi SMEs’ Sustainability through Social Media Marketing

Businesses use technological and social media marketing to respond to the COVID-19 pandemic. The study aims to better understand the factors that impact the spread of new technologies, the effectiveness of social media advertising, and the longevity of businesses. The researcher used a quantitative strategy based on partial least squares structural equation modeling to learn about the phenomenon of interest as much as possible. Dhaka, Bangladesh, small and medium enterprises (SMEs) participated in the survey. According to the findings, the popularity of internet and e-business technologies may be attributed to their apparent usefulness and practical experience ease of use. It is connecting internet/e-business technology, social media marketing, and the long-term success of small and medium enterprises (SMEs). Still, the expense factor was insufficient to prove that SMEs engage in social media advertising. The mediating effects between components may be better understood via internet/e-business technology and social media interactions, as shown in the study. This study is the first of its kind in Bangladesh and sheds light on the resilience of SMEs in the face of the COVID-19 pandemic, notwithstanding its focus on the capital city of Dhaka.

Surviving at a Falling Transportation Industry with the Help of Emotional Relationship with your Customers

Introduction Public intercity transportation is an example of a rapidly evolving industry. In developing countries, we observe the increasing popularity of airlines, whereas bus lines start to lose their significance. Methods The successive bankruptcy of bus companies, which have made a large amount of investment and provided significant employment, is a case worth examining. This article offers a case study from the Turkish public intercity transportation industry in which a bus company distinguishes itself by becoming a love brand with the relationship it establishes with its customers. The case shows that the emotional relationship established during the ups and downs of sectors have become insurance that protects companies during the downturn. Results To support our argument, we estimated the occupancy rates of bus companies based on characteristics such as ticket price, travel time, existence of catering service, social media interaction, and year of establishment. We found that the significant variables explaining occupancy rates are the companies' founding year and positive social media interaction frequency. Conclusion In an estimation where even the price is not a significant variable at the 0.01 level, the significance of the above-mentioned variables shows that relationship-based dynamics can be important demand determinants in a conventional transportation sector.

From Tweets to Energy Trends (TwEn): An exploratory framework for machine learning-based forecasting of urban-scale energy behavior leveraging social media data

Understanding energy behavior is crucial in addressing climate change, yet the accuracy of energy predictions is often limited by reliance on oversimplified occupancy data. This study develops an exploratory framework, from Tweets to Energy Trends (TwEn), leveraging machine learning and geo-tagged social media data to investigate the social dynamics of urban energy behavior. TwEn explores the relationship between social media interactions, specifically the frequency of tweets using data from the X Platform, and energy use patterns on an hourly basis. Employing various machine learning models, including artificial neural networks (ANN), decision tree (DTREE), random forest (RDF), and gradient boosting machine (GBM), the study evaluates their efficiency in both static and time-series forecasting of energy use trends and investigates the capability of social media data in predicting urban energy patterns. In addition, the study carries out a series of sensitivity analyses to provide an examination of the data and models. Furthermore, comprehensive data acquisition methods are developed and implemented. Tested on New York City using actual hourly electricity consumption data, the framework demonstrates significant predictive power of tweet frequency on urban electricity use. The framework also exhibits significant seasonality in X data, identifying patterns and trends that can inform time series urban building energy models (UBEM). The results offer new insights into the determinants of urban energy behavior and provide crucial perspectives for augmenting UBEMs, ensuring they are closely aligned with the complex social dynamics of contemporary urban environments. By integrating both digital and physical data, this study sheds light on urban energy behavior, supporting the formulation of effective and sustainable energy policies for urban futures.