Form Of User-generated Content Research Articles

The Role of User-Generated Content in the Sustainable Development of Online Healthcare Communities: Exploring the Moderating Influence of Signals

Grounded in signaling theory, this study explores the influence of user-generated content (UGC) within online healthcare communities on patient purchasing behavior, with the overarching goal of advancing the development of online medical consultation services and contributing to the sustainable evolution of the online healthcare community. Leveraging publicly available data from the “Haodf.com”, we construct an empirical model of online medical consultation purchases, integrating principles from signaling theory and trust theory. Our analysis scrutinizes the effects of various forms of UGC on patient purchasing behavior, alongside the moderating influence of associated signals. The results demonstrate that knowledge-sharing articles authored by doctors and patient ratings positively impact consultation service purchases, whereas public displays of doctors’ past consultation records impede such transactions. Furthermore, external signals were found to moderate the relationship between UGC and consultation service purchases. The implications of these findings offer actionable insights for stakeholders invested in online healthcare communities.

The "Datafication" of Fan Culture in the New Media Environment

In the context of new media, fan culture exhibits significant characteristics of datafication. Fan activities, behaviors, preferences, and interactions are transformed into quantifiable and analyzable digital data. This includes various forms of user-generated content, engagement metrics, and the digital footprint of fan communities. Behind this data lies valuable information that can help people better understand and analyze fan culture. However, attention should be given to the trend of excessive commercialization and profit-driven motives. This paper proposes an in-depth exploration and reflection on the impact of the datafication of fan culture and idol data in the new media environment, on individuals, society, and the economy. Therefore, it is necessary to not only pursue datafication but also pay attention to the problems it may bring, such as excessive competition and commercialization. At the same time, it is important for the public to value the diversity and individual needs of fan communities, to uphold respect for the authenticity and purity of fan culture, and to establish interactions between idols and fans based on respect and sincerity. It is necessary to moderately regulate the datafication of fan culture and avoid the negative impact of commercial interests on the healthy development of fan communities.

Analysis of User-generated Content in Visitor Reviews of Tourist Attractions Using Semantic Similarity

The tourism industry plays an important role in the world economic sector because it makes a significant contribution to the global economy, creates jobs, and strengthens economic growth. Therefore, visitor satisfaction is very important in this industry. Currently tourists use information in online media to find tourist attractions that suit their needs and expectations. Reviews of tourist attractions are growing on the internet. Customers can post reviews, recommendations, or ratings of a tourist spot. Online reviews in the form of User-generated Content (UGC) can provide benefits for business managers to obtain feedback from customers and improve certain product attributes or service characteristics in order to increase business value and support marketing activities. In this study, data collection will be carried out from one of the UGC tourism data sources, namely visitor reviews on Google Maps. Data analysis was performed using the natural language processing method. Analysis of these data will produce comprehensive information about tourist feedback.

Identifying learners' topical interests from social media content to enrich their course preferences in MOOCs using topic modeling and NLP techniques.

Interests play an essential role in the process of learning, thereby enriching learners 'interests will yield to an enhanced experience in MOOCs. Learners interact freely and spontaneously on social media through different forms of user-generated content which contain hidden information that reveals their real interests and preferences. In this paper, we aim to identify and extract the topical interest from the text content shared by learners on social media to enrich their course preferences in MOOCs. We apply NLP pipeline and topic modeling techniques to the textual feature using three well-known topic models: Latent Dirichlet Allocation, Latent Semantic Analysis, and BERTopic. The results of our experimentation have shown that BERTopic performed better on the scrapped dataset.

Exploring the effectiveness of emotional and rational user-generated contents in digital tourism platforms

User-generated content (UGC) is one of the most important notions that influences tourists’ decision-making. However, the nature of UGC is still underexplored in the literature. The present study aimed to examine the impact of two different forms of UGC (i.e., emotional and rational) on tourists’ perceived values and their subsequent behavioural responses. Using the stimulus-organism-response (SOR) model, the two forms of UGC were conceptualised as informational stimuli, while tourists’ overall value assessment (i.e., perceived values, including emotional, functional, relational, and entitativity values) of the utility of tourism activities in the tourism platform were conceptualised as organisms. Tourists’ behavioural responses were demonstrated by impulse buying and future purchase intention. Data from 538 respondents were collected using online survey and analysed using PLS-SEM. The results revealed that both emotional and rational UGC have a significant impact on tourists’ perceived values, which in turn strengthen impulse buying and future purchase intention. This study not only contributes to the tourism marketing literature by empirically examining the impacts of two forms of UGC on tourists’ organisms and responses, but also provides meaningful insights for tourism marketers to facilitate UGC to drive tourists’ perceived values and their subsequent behaviour.

Types (Disinformation)

Types (Disinformation)

Exploring influential factors affecting guest satisfaction

探究影响顾客满意度的因素：基于消费者评论的大数据和商务分析研究目的以消费者评论为主体的社交网络口碑营销对于影响消费者决策和提高服务提供商的品牌形象、销量、和服务创新起到重要作用。然而, 很少研究探索社交媒体上的真正酒店客人评论。因此, 商务分析技术在文献中还是很少使用的, 这种技术应该更多得到科研上的应用以给酒店从业人员给与启示。因此, 本论文旨在探究影响酒店顾客满意度的因素, 通过消费者评论和商务分析, 以展示商务分析技术是如何为酒店业和科研界来使用的。研究设计/方法/途径本论文使用大数据和商务分析技术来进行数据分析。大数据和商务分析能够为酒店从业人员开发有效战略以提高产品和服务质量, 最后达到顾客满意。因此, 本论文分析了Tripadvisor.com的200, 431酒店评论数, 通过商务分析技术, 以探索和审视影响顾客满意度的重要因素。研究结果研究结果显示服务、客房、和价值比成为影响顾客满意度的前三项因素。品牌类型和负面情绪是影响顾客满意度的负面因素。其他因素成为影响顾客满意度的正面因素。研究原创性/价值本论文是利用消费者评论的商务分析来探究影响顾客满意度与具体衡量因素之间关系的起点范例, 以此, 帮助酒店从业商来解决服务中的欠缺因素, 提高绩效。

Culturally-Embedded Visual Literacy

Social media is often analyzed as a "front stage" where social media users perform their identities and manage impressions through various forms of user-generated content: text, picture, video, as well as visual expression such as emoticon, emoji, sticker, and meme. Previous scholarship has suggested various functions of visual expression in supporting computer-mediated communication. In this study, we move beyond the utility perspective to ask what constitutes the literacy to utilize visual expression for impression management. Towards this goal, we conducted an interview study with 30 social media users in China. We found that visual literacy intersects with its cultural context. Our interviewees used text and visual expressions in sophisticated, skillful ways and tailored for different audiences in order to construct desired images. They also interacted with audience memory in order to perform their uniqueness and creativity. Lastly, we discuss culturally-embedded visual literacy and provide implications for design.

Web Archaeology: An Introduction

In this special issue of TMG – Journal for Media History, the focus is on the web history and especially on practices of web archiving. It showcases examples on how to actively engage as media scholars or as librarians and archivists with web archives and archived web-related born-digital sources. While it explores methods that deal with existing web archives, it will also address questions such as how to trace material retrospectively. In that sense, it is taking a ‘web archaeological’ approach, meaning that the focus is on actively uncovering the history of the web in its early days, emphasising the role of ‘digging’ and ‘reconstructing’ as central methods in tracing material objects (software, hardware, terminals, hard drives, cables, et cetera) and born-digital objects (websites, web elements like banners or avatars, blogs and vlogs, and many other forms of user-generated content).

Multimodal Learning toward Micro-Video Understanding

Micro-videos, a new form of user-generated content, have been spreading widely across various social platforms, such as Vine, Kuaishou, and TikTok. Different from traditional long videos, micro-videos are usually recorded by smart mobile devices at any place within a few seconds. Due to their brevity and low bandwidth cost, micro-videos are gaining increasing user enthusiasm. The blossoming of micro-videos opens the door to the possibility of many promising applications, ranging from network content caching to online advertising. Thus, it is highly desirable to develop an effective scheme for high-order micro-video understanding.

Consumer usage of online travel reviews: Expanding the unified theory of acceptance and use of technology 2 model

The present study examines a comprehensive model of travelers’ use of online travel reviews as a form of user-generated content (UGC) through an expanded unified theory of acceptance and use of technology 2 (UTAUT2) framework. The UTAUT2, which includes Performance Expectancy (PE), Effort Expectancy (EE), Social Influence (SI), Facilitating Conditions, Hedonic Motivation (HM), Price-Saving Orientation, and Habit, within this study incorporates two new variables: Trustworthiness and Homophily. We empirically examine the expanded model on a sample of 200 residents in the United Kingdom. The partial least squares–structural equation modeling analysis revealed that Homophily, PR, and Price-Saving Orientation are the strongest predictors of individual usage intentions’ of UGC. Moreover, the dimension of Habit, operationalized as a subjective measure of impulsive and automatic use of UGC, was found to be the strongest predictor of travelers’ actual UGC usage. This study enhances our understanding of the explanatory variables driving the usage of online reviews (e.g. contrary to prevalent knowledge from previous works, Trustworthiness in the present study was nonsignificant), thus providing far-reaching theoretical and practical recommendations for tourism researchers and practitioners.

The view, brew and loo: perceptions of botanic gardens?

PurposeThis is an exploratory and qualitative study to consider approaches to capture, analyse and monitor perceptions from big data, to inform and contribute to place management research and practice of botanic gardens (BGs). This paper aims to address the ongoing significant threat to BGs due to funding being cut and the need to inform and develop sustainable revenue streams for their survival.Design/methodology/approachGuiding research questions for this study were: ‘What are the perceived strengths and areas for development for 2 BGs via a Leximancer Automatic Content Analysis (ACA) of TripAdvisor online reviews; and do they match BGs purpose of scientific research, conservation, display and education?’ A content analysis of 582 online reviews from 2007 to 2017 follows qualitative methodology techniques using a combination of manual and automatic text analysis (Leximancer text mining software). These approaches enabled a comparison of online TripAdvisor reviews with Likert-type or rating scale items of 1 to 5 stars.FindingsInsights revealed the use of Leximancer and TripAdvisor (or similar innovations) as tools for potential place management, place marketing communications and monitoring purposes. Predominant perceptions extracted from reviews are not concerned with documented collections of living plants for the purposes of scientific discovery, conservation, display and education. Reviews clearly focus more upon aesthetics, facilities and services, which support previous studies. Overall, reviews highlighted positive sentiments towards the BGs.Research limitations/implicationsLimitations link to limited data across two BGs, synthesis and meaning of complex perceptions, matters of subjectivity and time needed to interpret information. Implications enable insights into BG “place” gleaned from big data in the form of user-generated content and electronic Word-Of-Mouth using Leximancer; viewed as a measure alongside management action plans. Future studies could strengthen debate and action regarding the use of Leximancer, and also public perception of BGs’ core functions, importance and value. The research supports potential to monitor and transform perceptions, values and beliefs. Outcomes could eventually inform policy and generate a much-needed shift in funds and resources for BGs by highlighting their relevance and value to society.Originality/valueAn empirical and methodological contribution via peer reviewed studies of visitor perceptions via online reviews of Britain’s BGs “place” and “space” analysed with Leximancer have never been published. This study critically explores potential visitor and place management needs of BGs. Managers can make better use of big data from social media platforms/digital channels, using a novel type of data analytical software like Leximancer for strategic planning; with more informed approaches to place management, innovation and development. A key contribution of this study is this ACA methodological approach for place management.

Volunteered Geographic Information: Interpretation, Visualisation and Social Computing (VGIscience)

Abstract. In the past years Volunteered Geographic Information (VGI) has emerged as a novel form of user-generated content, which involves active generation of geo-data for example in citizen science projects or during crisis mapping as well as the passive collection of data via the user’s location-enabled mobile devices. In addition there are more and more sensors available that detect our environment with ever greater detail and dynamics. These data can be used for a variety of applications, not only for the solution of societal tasks such as in environment, health or transport fields, but also for the development of commercial products and services. The interpretation, visualisation and usage of such multi-source data is challenging because of the large heterogeneity, the differences in quality, the high update frequencies, the varying spatial-temporal resolution, subjective characteristics and low semantic structuring. Therefore the German Research Foundation has launched a priority programme for the next 3–6 years which will support interdisciplinary research projects. This priority programme aims to provide a scientific basis for raising the potential of VGI- and sensor data. Research questions described more in detail in this short paper span from the extraction of spatial information, to the visual analysis and knowledge presentation, taking into account the social context while collecting and using VGI.

Who benefits from the wisdom of the crowd in crowdfunding? Assessing the benefits of user-generated and mass personal electronic word of mouth in computer-mediated financing

In this work, we explore the link between electronic word of mouth in the form of user-generated content (online forum interactions on Kickstarter) and through mass personal communication (sharing information through Facebook) on the performance of crowdfunding campaigns. Our formal theoretical model implies that the efficiency of electronic word of mouth is determined by the quality of the underlying crowdfunding campaign. Using a sample of 572 project observations, we test our theoretical predictions in cross-sectional logistic regression and ancillary Granger analyses. Our results highlight the interactive contingency of social media engagement and the success of the crowdfunding campaign. While a higher quality campaign is benefitting from user generated electronic word of mouth (online comments), the returns are diminishing. For mass personal electronic word of mouth (Facebook shares), we even find a reverse causal effect. Social media activity follows a successful campaign, but does not affect the success probability of the campaign. Crowdfunding campaigns need to approach their social media activities with a certain note of sensitivity to achieve the objective of successfully reaching their campaign goal.

Application of text mining techniques to the analysis of discourse in eWOM communications from a gender perspective

The emergence of online user-generated content has raised numerous questions about discourse gender differences as compared to face-to-face interactions. The intended gender-free equality of Internet has been challenged by numerous studies, and significant differences have been found in online communications. This paper proposes the application of text mining techniques to online gender discourse through the analysis of shared reviews in electronic word-of-mouth communities (eWOM), which is a form of user-generated content. More specifically, linguistic issues, sentiment analysis and content analysis were applied to online reviews from a gender perspective. The methodological approach includes gathering online reviews, pre-processing collected reviews and a statistical analysis of documents features to extract the differences between male and female discourses in a specific product category. Findings reveal not only the discourse differences between women and men but also their different preferences and the feasibility of predicting gender using a set of frequent key terms. These findings are interesting both for retailers so they can adapt their offer to the gender of customers, and for online recommender systems, as the proposed methodology can be used to predict the gender of users in those cases where the gender is not explicitly stated.

Echo Chambers of Denial: Explaining User Comments on Climate Change

ABSTRACTAlthough there is a broad consensus among scientists and journalists about the existence of anthropogenic climate change as a global problem, some segments of the population remain doubtful about the human impact on it. The internet provides citizens with opportunities to publicly voice their doubts and user comment sections of online media are a popular form of user-generated content. This study identifies factors that foster comments that are sceptical or supportive of basic assumptions of anthropogenic climate change, drawing on online news in the US, the UK, Germany, India, and Switzerland. The results show that users adapt to the dominant opinion within the respective media outlet: user comment sections serve as echo chambers rather than as corrective mechanisms. Climate change denial is more visible in user comment sections in countries where the climate change debate reflects the scientific consensus on climate change and user comments create niches of denial.

A Survey on Visual Analytics for the Spatio-Temporal Exploration of Microblogging Content

User-generated content is a valuable source of information whose production increases year after year. Twitter data is a form of user-generated content that is frequently adopted to comment on life activities in several contexts. Thus, scientific interest in that data has increased in recent years. This paper focusses on visual analytics approaches addressing the microblogging content exchanged through Twitter. In particular, we concentrate our interest on approaches that consider spatial and temporal aspects and provide visual support. Articles from the major conferences, journals, and digital libraries have been collected, organized and compared based on different criteria such as research questions, application focus, analytical and visual methods adopted, and interaction provided. In addition to these comparisons, opportunities and challenges are illustrated to inspire future research.

Bumps and bruises in the digital skins of cities: unevenly distributed user-generated content across US urban areas

ABSTRACTAs momentum and interest build to leverage new forms of user-generated content that contains geographical information, classical issues of data quality remain significant research challenges. In this article we explore issues of representativeness for one form of user-generated content, geotagged photographs in US urban centers. Generalized linear models were developed to associate photograph distribution with underlying socioeconomic descriptors at the city-scale, and examine intra-city variation in relation to income inequality. We conclude our analyses with a detailed examination of Dallas, Seattle, and New Orleans. Our findings add to the growing volume of evidence outlining uneven representativeness in user-generated data, and our approach contributes to the stock of methods available to investigate geographic variations in representativeness. In addition to city-scale variables relating to distribution of user-generated content, variability remains at localized scales that demand an individual and contextual understanding of their form and nature. The findings demonstrate that careful analysis of representativeness at both macro and micro scales can simultaneously provide important insights into the processes giving rise to user-generated data sets and potentially shed light on their embedded biases and suitability as inputs to analysis.

POS-RS: A Random Subspace method for sentiment classification based on part-of-speech analysis

With the rise of Web 2.0 platforms, personal opinions, such as reviews, ratings, recommendations, and other forms of user-generated content, have fueled interest in sentiment classification in both academia and industry. In order to enhance the performance of sentiment classification, ensemble methods have been investigated by previous research and proven to be effective theoretically and empirically. We advance this line of research by proposing an enhanced Random Subspace method, POS-RS, for sentiment classification based on part-of-speech analysis. Unlike existing Random Subspace methods using a single subspace rate to control the diversity of base learners, POS-RS employs two important parameters, i.e. content lexicon subspace rate and function lexicon subspace rate, to control the balance between the accuracy and diversity of base learners. Ten publicly available sentiment datasets were investigated to verify the effectiveness of proposed method. Empirical results reveal that POS-RS achieves the best performance through reducing bias and variance simultaneously compared to the base learner, i.e., Support Vector Machine. These results illustrate that POS-RS can be used as a viable method for sentiment classification and has the potential of being successfully applied to other text classification problems.

Exploring the Intraurban Digital Divide Using Online Restaurant Reviews: A Case Study in Franklin County, Ohio

Web 2.0 has yielded a wealth of publicly available data, largely through the popularization of social media and the proliferation of user-generated content (UGC). Thus, the data void in social science research is diminishing as these new data sources are exploited. The discipline of geography, in particular, stands to benefit considerably from geotagged UGC or volunteered geographic information (VGI). In this article, we utilize a form of UGC—restaurant reviews—to evaluate the geography of Web 2.0 in Franklin County, Ohio. We find that restaurant reviews are largely concentrated in relatively few areas of the city, indicating that the impacts of Web 2.0 have not touched down evenly within the central county of the Columbus metropolitan statistical area. We argue that an intraurban digital divide is still apparent and might even be exacerbated by Web 2.0.