User-generated Research Articles

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.

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.

Dynamic Music Video: Categorizing the Convergence of Video Games and Music Videos

The present article examines how the aesthetics of a specific format can be applied to another, thus creating a new autonomous product. This is the case of a distinctive point in video games wherein the playthrough is imbued with music video aesthetics, and the player interacts with the music, camera movements and landscape, becoming the protagonist and architect of what I propose to define as a “Dynamic Music Video”—following Collins’ definition of “dynamic music” (2008, 139). This study analyzes the musical transmedia narratives created from Death Stranding and other contemporary games, both regarding the gameplay experience and the User-Generated Content, focusing on their bidirectional motion: one that converts the players’ experience into a music video inserted into a video game, and one that converts the video game into an autonomous music video.

EMhub: a web platform for data management and on-the-fly processing in scientific facilities.

Most scientific facilities produce large amounts of heterogeneous data at a rapid pace. Managing users, instruments, reports and invoices presents additional challenges. To address these challenges, EMhub, a web platform designed to support the daily operations and record-keeping of a scientific facility, has been introduced. EMhub enables the easy management of user information, instruments, bookings and projects. The application was initially developed to meet the needs of a cryoEM facility, but its functionality and adaptability have proven to be broad enough to be extended to other data-generating centers. The expansion ofEMHub is enabled by the modular nature of its core functionalities. The application allows external processes to be connected via a REST API, automating tasks such as folder creation, user and password generation, and theexecution of real-time data-processing pipelines. EMhub has been used for several years at the Swedish National CryoEM Facility and has been installed in the CryoEM center at the Structural Biology Department at St. Jude Children's Research Hospital. A fully automated single-particle pipeline has been implemented for on-the-fly data processing and analysis. At St. Jude, the X-Ray Crystallography Center and the Single-Molecule Imaging Center have already expanded the platform to support their operational and data-management workflows.

Fighting Misinformation on Social Media: An Empirical Investigation of the Impact of Prominence Reduction Policies

Misinformation has dire implications for both public welfare and the operational aims of user-generated content platforms. As a result, platforms have adopted various content moderation policies aimed at decreasing the volume and impact of misinformation. However, implementing new platform policies runs the risk of decreasing user contribution and alienating core users, and results regarding the efficacy of such policies are mixed. Herein, we empirically assess a prominence reduction policy applied to a problematic group that is high in misinformation. The goal of this policy is to reduce the visibility of misinformation on the platform (rather than deleting misinformation or banning users). The results show that while prominence reduction diminishes misinformation dissemination in the focal group, this method also results in a spillover of misinformation to topically related spaces. This spillover is short-lived and driven primarily by a small set of problematic users. As misinformation is not contagious, we find that this spillover to external groups diminishes over time. Finally, prominence reduction is found to have no impact on non-misinformation contribution on the studied platform. The findings of this study have important implications for platform operations and provide useful recommendations for managers regarding effective ways to reduce the spread of misinformation.

Bi-channel dynamic topic model for quality monitoring considering initial and additional online customer reviews

E-commerce provides a vast space for user-generated content, including user reviews from the initial use of products or services to subsequent usage. Existing methods mainly focus on unmodified reviews and ignore customer perception after more experience with products. To address this, we propose a Markov chain-based bi-channel dynamic topic model (BDTM), which extends the sequential structure of the dynamic topic model and incorporates initial customer reviews and additional reviews to reflect topic shifts caused by customer experience perception. Then, a control chart based on word mover’s distance (WMD), called a BDTM-WMD (B-W) chart, is proposed to monitor topic shifts under BDTM. An alternative multiscale dynamic topic model (M-K) chart is constructed for comparison. Using a simulation approach, we find that compared with the existing sequential reverse joint sentiment-topic (SRJST) chart and joint sentiment topic-rating meets review (JSTRMR) chart, the proposed B-W chart is more sensitive to small shifts. Case study 1, using real data, shows the advantages of the proposed BDTM, B-W chart and M-K chart under both initial and additional reviews. Case study 2 shows that with additional reviews, the proposed B-W chart triggers out-of-control signals earlier than those by the existing joint sentiment topic-rating meets review (JSTRMR) chart.

Trust in context: The impact of regulation on blockchain and DeFi

AbstractTrust is a key resource in financial transactions. Traditional financial institutions, and novel blockchain‐based decentralized financial (DeFi) services rely on fundamentally different sources of trust and confidence. The former relies on heavy regulation, trusted intermediaries, clear rules (and restrictions) on market competition, and long‐standing informal expectations on what banks and other financial intermediaries are supposed to do or not to do. The latter rely on blockchain technology to provide confidence in the outcome of rules encoded in protocols and smart contracts. Their main promise is to create confidence in the way the blockchain architecture enforces rules, rather than to trust banks, regulators, and markets. In this article, we compare the trust architectures surrounding these two financial systems. We provide a deeper analysis of how proposed regulation in the blockchain space affects the code‐ and confidence‐based architectures which so far have underwrote DeFi. We argue that despite the solid safeguards and guarantees which code can offer, the confidence in DeFi is still very much dependent on more traditional trust‐enhancing mechanisms, such as code governance, and antifraud regulation to address some of the issues which currently plague this domain, and which have no immediate, purely software‐based solutions. What is more, given the risks of bugs or scams in the DeFi space, regulation and trusted intermediaries may need to play a more active role, in order for DeFi to gain the trust of the next generation of users.

Correlation-aware Cross-modal Attention Network for Fashion Compatibility Modeling in UGC Systems

Empowered by the continuous integration of social multimedia and artificial intelligence, the application scenarios of information retrieval (IR) progressively tend to be diversified and personalized. Currently, User-Generated Content (UGC) systems have great potential to handle the interactions between large-scale users and massive media contents. As an emerging multimedia IR, Fashion Compatibility Modeling (FCM) aims to predict the matching degree of each given outfit and provide complementary item recommendation for user queries. Although existing studies attempt to explore the FCM task from a multimodal perspective with promising progress, they still fail to fully leverage the interactions between multimodal information or ignore the item-item contextual connectivities of intra-outfit. In this paper, a novel fashion compatibility modeling scheme is proposed based on Correlation-aware Cross-modal Attention Network. To better tackle these issues, our work mainly focuses on enhancing comprehensive multimodal representations of fashion items by integrating the cross-modal collaborative contents and uncovering the contextual correlations. Since the multimodal information of fashion items can deliver various semantic clues from multiple aspects, a modality-driven collaborative learning module is presented to explicitly model the interactions of modal consistency and complementarity via a co-attention mechanism. Considering the rich connections among numerous items in each outfit as contextual cues, a correlation-aware information aggregation module is further designed to adaptively capture significant intra-correlations of item-item for characterizing the content-aware outfit representations. Experiments conducted on two real-world fashion datasets demonstrate the superiority of our approach over state-of-the-art methods.