User-generated Research Articles

Exploring the job (dis)satisfaction indicators of online food delivery employees: insights from employees' online reviews

PurposeThe present study sought to determine the main themes employees discussed in online reviews associated with satisfaction (pros) and dissatisfaction (cons).Design/methodology/approachThis study used qualitative content analysis to examine employee reviews working in India's largest food delivery company.FindingsThe analyses revealed the existence of seven themes: company environment, employees, salary, work–life balance, company culture and values, flexibility and favoritism. Negative themes were associated with the “work-life balance” and “favoritism” themes. The results provided valuable insights into online food delivery platform employees' overall experiences based on social media information and aided in identifying key themes associated with various satisfaction levels.Originality/valueThis study contributes to the literature by uniquely utilizing user-generated content from an employment search engine as a novel data source to investigate the factors influencing satisfaction and dissatisfaction among online food delivery employees. This approach represents a significant departure from traditional survey-based methods, filling a gap in the literature by offering insights directly from employees' unfiltered perspectives. By leveraging this innovative methodology, our research provides a fresh and nuanced understanding of the experiences and concerns of online food delivery workers, thereby adding substantial value to the fields of organizational behavior and human resource management.

Teaching poetry using Instagram: An international, interdisciplinary study with adolescents mobilizing literacy and the arts

Young people engage daily with various social media platforms to communicate with each other across the globe. Adolescents not only share text, but also use images and sound to express themselves on platforms such as Instagram and TikTok to provide access to user-created content. The recent emergence of InstaPoetry—poetry with images on Instagram—has been part of such communication and provides a good entry point into adolescents’ engagement with literature and the arts. Limited research exists, however, on how this literate practice, paired with virtual and in-person museum visits, influences young people’s self-expression. In this article, we offer ways of integrating and involving these dynamic dimensions into research projects based on four sites of inquiry located in Canada and Australia. Funded by the Social Sciences and Humanities Research Council, this research project provides concrete avenues to investigate teachers’ methods to foster adolescents’ engagement with literature and the arts (i.e., contextual design, procedures, environment) in (post) COVID-19 times.

Terminology of medicines used to control seizure disorders: 2024 ILAE Recommendations and profile of real-world term use

Background. The International League Against Epilepsy (ILAE) has provided recommendations on the terminology of medicines for control of seizure disorders, which should be adapted into different languages.Objective: to evaluate the usage profile of terms describing the effects of medicines used in patients with epilepsy.Material and methods. The search query analysis systems Google Trends and Yandex Wordstat as well as PubMed/MEDLINE, Google Scholar and eLibrary databases were used to evaluate the general public and professional usage profile, respectively.The following terms in Russian and English were studied: “antiepileptic”, “antiepileptic drug”, “antiseizure”, “antiseizure drug”. In order to ascertain a prevailing opinion within professional and patient communities, a questionnaire survey was conducted on the public online resources of the non-profit partnership “The Association of Epileptologists and Patients” carried out via the cloud-based cross-platform social media as well as instant messaging service Telegram and the social media platform VK.Results. In the English-speaking internet segment, the term “antiepileptic” is used more than 5 times frequently than “antiseizure” by the general public. Conversely, in the Russian-language segment, the term “antiseizure” is almost 2.5 times more common. The term “antiepileptic” is more prevalent in scientific community in both segments. This finding aligns with the profile of terminology usage observed among epileptologists and epilepsy patients. The questionnaire survey results revealed that 56.42% of 179 respondents were in favor of the terms “antiepileptic drug”, “AED” and “antiepileptic therapy”, whereas as few as 17.32% of those endorsed the ILAE-proposed terminology.Conclusion. In light of the established practice of terminology use in both professional scientific community and among practicing epileptologists and epileptic patients, as well as the differences in the semantics of English- and Russian-language terms and the identified lack of precision in the ILAE definition of epilepsy, it can be concluded that for the Russian-speaking population, the terms “antiepileptic drug”, “AED” are more preferable rather than the terms “antiseizure medication”, “ASM”. Additionally, it is preferable to use the terms “convulsive ictus” and “epileptic ictus” due to negative connotation with term “seizure” in Russian. The terms “antiseizure” and “antiepileptic” should not be regarded as mutually exclusive in the context of treatment that has a direct impact on epilepsy course. Finally, it is necessary to further refine epilepsy definition and classification.

Influence of information credibility of Instagram posts on tourist decision-making behaviour

This research tries to identify the influence of Instagram posts on the decision-making behaviour of tourists. Social media plays a pivotal role in today’s travel and tourism environment. It also determines how people perceive and choose a destination. Social media acts as a platform for people to discuss and share about travel adventures, providing detailed information that can widely influence the decision-making process among visitors. Response to social media sharing and information credibility are the independent variables. Prior knowledge and tourist decision-making are the dependent variables. The research also studies the moderating role of the authenticity of social media posts in shaping tourists' responses and choices. An online survey was conducted to collect primary data from 511 Delhi and the National Capital Region (NCR) respondents. The sampling design adopted was stratified random sampling. Analysis of moment structures (AMOS) was used to analyse the quantitative data collected. A structural equation modelling approach was used to analyse the data. The study reveals that Instagram and other platforms transform intangible aspects of destinations into tangible experiences, enriching tourists' knowledge. Influencers and user-generated content play a crucial role in decision-making. The authenticity of the post plays a crucial role in shaping the tourist’s decision-making process.

Graph neural networks for multi-view learning: a taxonomic review

With the explosive growth of user-generated content, multi-view learning has become a rapidly growing direction in pattern recognition and data analysis areas. Due to the significant application value of multi-view learning, there has been a continuous emergence of research based on machine learning methods and traditional deep learning paradigms. The core challenge in multi-view learning lies in harnessing both consistent and complementary information to forge a unified, comprehensive representation. However, many multi-view learning tasks are based on graph-structured data, making existing methods unable to effectively mine the information contained in the input multiple data sources. Recently, graph neural networks (GNN) techniques have been widely utilized to deal with non-Euclidean data, such as graphs or manifolds. Thus, it is essential to combine the advantages of the powerful learning capability of GNN models and multi-view data. In this paper, we aim to provide a comprehensive survey of recent research works on GNN-based multi-view learning. In detail, we first provide a taxonomy of GNN-based multi-view learning methods according to the input form of models: multi-relation, multi-attribute and mixed. Then, we introduce the applications of multi-view learning, including recommendation systems, computer vision and so on. Moreover, several public datasets and open-source codes are introduced for implementation. Finally, we analyze the challenges of applying GNN models on various multi-view learning tasks and state new future directions in this field.

Leveraging user-generated content (UGC) in pre-travel behavior: A PLS-SEM and necessary conditions analysis (NCA) approach

Online review reading is almost ritualistic before making any purchases. Building on social cognition theory, uses and gratification theory, and stimulus organism response theory, this study explores user-generated content (UGC) attributes affecting intentions to travel via a mediating variable (destination image). Using partial least squares structural equation modeling (PLS-SEM) analysis and necessary conditions analysis (NCA) on responses ( N = 392) collected online in India, the findings reveal that the perceived novelty of content (PNC) and perceived value of content (PVC) positively influence destination image and behavioral intention. Destination image mediates the relationship between PNC, PVC, and content users’ behavioral intentions. Perceived reliability of content (PRC) positively affects behavioral intention. The findings suggest that adequate PNC, PRC, and PVC levels are necessary to enhance destination image and generate intention to travel. This study contributes to the UGC literature and provides recommendations for stakeholders to improve user experience and enhance destination image perception.

Safety Insights from E-Scooter Rider and Bicyclist Behavior Observations in Two U.S. Cities

E-scooters, a novel micromobility mode, have transformed U.S. transportation in urban cores, offering potential advantages like cost-effective mobility, congestion reduction, community accessibility, and lower emissions. However, owing to their novelty, their associated safety impacts and rider behaviors remain uncertain, complicating policy formulation. Specifically, e-scooter policy often mirrors bicycle policy, yet there have been few studies that have explicitly investigated how e-scooter and bicycle rider behaviors compare. This research investigated rider behavior relative to infrastructure by examining riding patterns through a video-based platform on 26 Nashville and Portland streets, offering insights on e-scooter and bicyclist behaviors, including riding location and helmet use. This study differs from other observational studies since it covers longer observation periods than prior in-person studies, including nighttime riding behavior. The video analysis also allows for observations of basic demographic characteristics. The findings revealed that bike lanes were well-used and dramatically reduced sidewalk riding and general travel lane usage. Without bike lanes, riders typically resorted to using sidewalks on high-capacity roads and sharing lanes with vehicles on low-capacity streets. The data also suggested demographic trends, such as riskier behavior among younger, male riders, and low helmet usage among e-scooter riders. The results emphasize the need for micromobility-supportive infrastructure in road design to encourage safer riding practices.

Exploring the Impact of AI Enhancement on the Sports App Community: Analyzing Human-Computer Interaction and Social Factors Using a Hybrid SEM-ANN Approach

The iteration of AI technology has enhanced the industry competitiveness of sports apps and brought a new emotional experience to users. However, the community function of AI-enabled sports apps has not yet received sufficient attention. This study adopts the technology acceptance model framework through the human-computer interaction and social psychology perspectives, incorporating perceived interactivity (human-human interaction, human-information interaction, human-system interaction), user experience (functional experience, content experience, and emotional experience), attachment theory (emotional attachment, emotional loyalty), social identity, social trust, and satisfaction variables to construct a theoretical model. It aims to solve the problem of how AI can enhance users’ emotional interaction, community stickiness, and ultimately social interaction satisfaction in sports apps. Through literature review, theoretical analysis, and case study, the Chinese “KEEP” APP was identified as the research object, and the questionnaire was constructed with an improved test scale. The study used a cross-sectional sampling method to collect data from Chinese online platforms for three months (N = 520) and tested the hypotheses in the theoretical model through structural equation modeling and artificial neural networks. The results of the study show that (1) emotional attachment, emotional loyalty, social identity, and social trust positively and significantly affect user satisfaction; (2) emotional attachment and social identity positively and significantly affect human-human interaction; (3) perceived usefulness, social identity, and social trust significantly construct an influence loop on the internal path. Compared with existing studies, this study proposes a multidimensional innovative framework that reveals the deeper influence mechanism of AI technology in sports APP communities and makes up for the shortcomings of existing studies in terms of users’ emotional interactions and community stickiness. Through this study, sports APP developers and operators, marketers, AI technology developers, as well as sports enthusiasts, and general users can obtain specific practical suggestions to further optimize the design and functionality of sports apps, and enhance the overall user experience and satisfaction.

Playing away from home: a qualitative sentiment analysis of online viewers’ non-social emotions on sex tourism vlogs

ABSTRACT Popular yet so controversial, sex tourism remains a significant attraction in Southeast Asia, generating an abundance of scholarly explorations. However, little is known about the emotions expressed by viewers on user-generated content regarding this contentious form of tourism. Thus, this qualitative sentiment analysis, which aims to dilate non-social emotions found in comments expressed by online viewers on video content featuring sex tourism in Southeast Asia, is conducted. Through Data Miner, online comments (n = 10,864) were gathered and extracted from purposively selected sex tourism vlogs on YouTube, which were then subjected to Phronetic Iterative Qualitative Data Analysis (PIQDA) (Tracy, 2013). A member-checking procedure was performed further to establish the validity of the study’s findings. Interestingly, a model dubbed YouTube online viewers’ non-social emotions on sex tourism as a magnetic field consisting of labelled emotions known as enabling and disabling has emerged in this qualitative study. Notably, enabling emotions capture distinct emotions of interest, admiration, joy, amusement, satisfaction, sexual desire, excitement, awe, and romance. The second notion, disabling emotions, encapsulates patent emotions of dislike, anger, boredom, sadness, confusion, emphatic pain, disappointment, disgust, fear, anxiety, and awkwardness. Theoretical and practical implications, limitations, and recommendations on non-social emotions in tourism were discussed in this paper.

Sentiment analysis using graph-based Quickprop method for product quality enhancement

ABSTRACT The degree to which customers express satisfaction with a product on Twitter and other social media platforms is increasingly used to evaluate product quality. However, the volume and variety of textual data make traditional sentiment analysis methods challenging. The nuanced and context-dependent nature of product-related opinions presents a challenge for existing tools. This research addresses this gap by utilizing complex graph-based modelling strategies to capture the intricacies of real-world data. The Graph-based Quickprop Method constructs a graph model using the Sentiment140 dataset with 1.6 million tweets, where individuals are nodes and interactions are edges. Experimental results show a significant increase in sentiment classification accuracy, demonstrating the method’s efficacy. This contribution underscores the importance of relational structures in sentiment analysis and provides a robust framework for extracting actionable insights from user-generated content, leading to improved product quality evaluations. The GQP-PQE method advances sentiment analysis and offers practical implications for businesses seeking to enhance product quality through a better understanding of consumer feedback on social media.

Impact of user-generated content in digital platforms on purchase intention: the mediator role of user emotion in the electronic product industry

Impact of user-generated content in digital platforms on purchase intention: the mediator role of user emotion in the electronic product industry

Analysis Of Social Media Promotion and Marketing Strategies for Disney Movies: A Case Study on Zootopia & Frozen 2

This study aims to deeply analyze the successful strategies of Disney movies in social media marketing, taking “Zootopia” and “Frozen 2” as case studies. With the expansion of the global movie market and the diversification of audience demand, animated movies are highly competitive, and Disney occupies a leading position with its strong strength and perfect network. The study first introduces the background of The Walt Disney Company, explaining the study's purpose and significance and the framework. Then it analyzes the global influence and marketing strategy of “Zootopia” respectively, including its background, the current fire situation, social media promotion techniques innovative parts, and offline promotion strategy. Then the background, audience reaction, social media promotion methods, and effects of Frozen 2 are discussed. It then compares the differences and changes in the marketing strategies of the two films, including social media strategies, offline promotion focus, cross-border cooperation methods, etc. It also discusses the evolution of marketing strategies, such as digital transformation, emphasizing user-generated content, exploring the application of new technologies, etc., as well as cross-cultural communication strategies. Finally, suggestions and expectations for movie marketing in the digital era are given, including strengthening innovation, building a social interaction ecology, pursuing content quality, and deepening cross-cultural communication strategies. The study summarizes the results, points out the limitations, and offers an outlook on future research directions, providing valuable experience and inspiration for the film industry.

Danger, Nuisance, Disregard: Analyzing User-Generated Videos for Augmented Reality Gameplay on Hand-held Devices

Augmented Reality (AR) has been largely deployed on smartphones in recent years. AR gaming, featured with geo-reference, is anchored to our real-world environments. Nevertheless, designing such AR user interaction in-the-wild is under-explored. Therefore, we examined 242 YouTube videos regarding AR gameplay, primarily Pokémon GO, (1) to reveal personal and social threats during gameplay, and (2) to identify how such threats connect to the user and spatial contexts. Our video analysis generalises the threats, including but not limited to hitting bystanders, falling off a cliff, car crashes, crowds and blockages, and stampedes. Through video analysis, we connect the threats with the user and spatial contexts. Subsequently, we propose several design tactics to deliver safe and nuisance-free AR usages in physical environments. Finally, we suggest strategies to consider large-scale deployment of AR at the levels of individuals and local communities, such as an auditable and accountable framework for user safety and social acceptability.

Inventory Management System Using Economic Order Quantity And Reorder Point

The inventory management system for car workshops is still using Excel, which makes it inefficient and inaccurate for complex stock calculations. The solution is to design an inventory application that uses the Economic Order Quantity and Reorder Point methods. The application will provide timely stock replenishment information and optimize order quantities, increasing inventory management efficiency and accuracy. The Software Development Life Cycle used in this study is Rapid Application Development, a process emphasizing short development cycles. User Acceptance Test results show that users received the inventory management system very well, with an average satisfaction level of 98.44% for Admins and 97% for general users, indicating high satisfaction with the system's features and functionality.

Leveraging event portfolios for strategic business development: a case study of Singapore Changi Airport

PurposeThis research article examines event marketing in the airport business, specifically how an airport leverages event portfolios to develop its businesses by marketing itself as a destination, improving the user experience and generating revenue outside of aviation-related activities. Focusing on Singapore Changi Airport as a case study, the research underscores the airport’s business development in a competitive environment.Design/methodology/approachThe study employs thematic data analysis to examine how Changi Airport leverages an event portfolio as a strategic business development tool. It conducts documentary research by gathering a diverse range of grey literature sources from the online public domain. These sources include documents related to airports, online news and media portals and user-generated content on social media platforms. The analysis is conducted in an inductive manner.FindingsSingapore Changi Airport has recently leveraged events as a strategic marketing strategy for developing nonaeronautical business operations. As an attraction to appeal to tourists, the airport organised multiple types of events in its portfolio. Furthermore, the airport offers a wide range of event-related activities within its premises, including both sporting and cultural events. The purpose of these activities is to actively engage visitors and air travellers by offering a range of interactive experiences, including games and challenges. Such event-related activities reflect the airport’s transformation into a lifestyle platform.Originality/valueCritics commonly highlight the lack of empirical research and the restricted relevance of the findings in event and festival research. This article consolidates the existing knowledge on airport management and event business to enhance the event marketing and event portfolio literature, specifically in the setting of airport business.

Sentiment analysis of post-COVID-19 health information needs of autism spectrum disorder community: insights from social media discussions.

This study explores the health information needs of individuals with autism spectrum disorder (ASD) and their caregivers in the post-COVID-19 era by analyzing discussions from Reddit, a popular social media platform. Utilizing a mixed-method approach that integrates qualitative content analysis with quantitative sentiment analysis, we analyzed user-generated content from the "r/autism" subreddit to identify recurring themes and sentiments. The qualitative analysis uncovered key themes, including symptoms, diagnostic challenges, caregiver experiences, treatment options, and stigma, reflecting the diverse concerns within the ASD community. The quantitative sentiment analysis revealed a predominance of positive sentiment across discussions, although significant instances of neutral and negative sentiments were also present, indicating varied experiences and perspectives among community members. Among the machine learning models used for sentiment classification, the Bi-directional Long Short-Term Memory (Bi-LSTM) model achieved the highest performance, demonstrating a validation accuracy of 95.74%. The findings highlight the need for improved digital platforms and community resources to address the specific health information needs of the ASD community, particularly in enhancing access to reliable information and fostering supportive environments. These insights can guide future interventions and policies aimed at improving the well-being of autistic persons and their caregivers.

Augmenting Multimodal Content Representation with Transformers for Misinformation Detection

Information sharing on social media has become a common practice for people around the world. Since it is difficult to check user-generated content on social media, huge amounts of rumors and misinformation are being spread with authentic information. On the one hand, most of the social platforms identify rumors through manual fact-checking, which is very inefficient. On the other hand, with an emerging form of misinformation that contains inconsistent image–text pairs, it would be beneficial if we could compare the meaning of multimodal content within the same post for detecting image–text inconsistency. In this paper, we propose a novel approach to misinformation detection by multimodal feature fusion with transformers and credibility assessment with self-attention-based Bi-RNN networks. Firstly, captions are derived from images using an image captioning module to obtain their semantic descriptions. These are compared with surrounding text by fine-tuning transformers for consistency check in semantics. Then, to further aggregate sentiment features into text representation, we fine-tune a separate transformer for text sentiment classification, where the output is concatenated to augment text embeddings. Finally, Multi-Cell Bi-GRUs with self-attention are used to train the credibility assessment model for misinformation detection. From the experimental results on tweets, the best performance with an accuracy of 0.904 and an F1-score of 0.921 can be obtained when applying feature fusion of augmented embeddings with sentiment classification results. This shows the potential of the innovative way of applying transformers in our proposed approach to misinformation detection. Further investigation is needed to validate the performance on various types of multimodal discrepancies.

Expo2020 Dubai and Tourism Marketing: An Online User-Generated Content Analysis

Dubai is currently one of the world’s leading cities in the tourism field. Expo2020 has placed it in a remarkable position in sustainable tourism development amongst other tourist destinations. Information communication technologies (ICTs) have become essential for connecting tourism destinations with their visitors and other market partners. X (Twitter) facilitates online communication; embracing it have proved to be critical for boosting the brand image and reputation of tourism spots and augmenting their competitive advantage in the market. User-generated content (UGC) and electronic word of mouth (eWOM) have become important key players in this field. The purpose of this research was to deepen the study of the Expo2020 event in relation to tourism within the framework of ICTs. To do so, 3811 tweets posted from April 2021 to March 2022 using the hashtags #expo2020 and #visitdubai were analyzed. Users generated more than 10 tweets daily on this topic. Dubai’s institutional accounts were highly involved in creating content, leading personal accounts to follow the eWOM and UGC. Destination marketing organization (DMO) related to the Indian segment account stood out as one of the main players in the analyzed sample.

Get Ready to Click: A Comprehensive Review of User-Generated Photography on Instagram

Objective: This study seeks to perform a systematic literature review to explore the concepts of user-generated photography and the use of Instagram in the tourism industry. Theoretical Framework: Systematic review of user-generated content and Instagram's impact on tourism Method: A comprehensive search was performed in SCOPUS to gather relevant papers. The search resulted in an analysis of 25 papers published between 2014 and 2023. Results and Discussion: The results revealed that the growth of social networks, particularly Instagram, impacts tourists' decision-making when planning their trips. Additionally, user-generated content, especially photographs, influences various aspects of travelers' experiences. Research Implications: The review highlights the scarcity of studies on this topic and suggests that the findings strengthen the literature on user-generated content and Instagram in tourism, providing a reference for future research. Originality/Value: The systematic review addresses a gap in the literature, frames the current understanding, and identifies clusters of research to illuminate the state of research in this field

The Role of Social Media in Shaping Public Opinion: A Comparative Analysis of Traditional vs. Digital Media Platforms

This study examines the role of social media in shaping public opinion, contrasting it with traditional media platforms through a qualitative literature review. As digital media becomes increasingly prevalent, understanding its impact on public discourse is essential. The research highlights the unique attributes of social media, including interactivity, immediacy, and user-generated content, which differentiate it from traditional media formats such as television, radio, and print. By synthesizing findings from a variety of scholarly articles, this study identifies key themes regarding how social media influences public perception, mobilizes communities, and facilitates the spread of information. Furthermore, the analysis reveals significant challenges, including the prevalence of misinformation and echo chambers, which can distort public opinion and polarize discourse. The results indicate that while social media offers opportunities for broader engagement and diverse perspectives, it also necessitates critical literacy among users to navigate the complexities of digital information landscapes. This paper contributes to the ongoing discourse on media studies by providing a comprehensive overview of the comparative effects of traditional and digital media platforms on public opinion formation. The findings underscore the need for further research into the ethical implications of social media usage and its potential to shape democratic processes.