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Copyright of Doxa Comunicación is the property of Fundacion Universitaria San Pablo - CEU and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

(1) Background: In the current competitive market environment, accurately forecasting user needs is crucial for business success. By analyzing user-generated content (UGC) on social network platforms, enterprises can mine potential user needs and discern shifts in these needs, thereby enabling more efficient and precise product design that aligns with user needs. For newly launched products with a limited presence in the market, the scarcity of UGC poses a challenge to businesses seeking to predict user needs from small datasets. (2) Methods: To address this challenge, this paper proposes a model using correlation analysis (CA) and linear regression (LR) combined with multidimensional gray prediction (a CA-LR-GM (1, N) model) to help enterprises use small sample data to predict user needs. Using the UGC of the Xiaomi SU7 as a case study, this paper demonstrates the prediction of user needs for the vehicle and refines the prediction outcomes through an optimization design informed by the principle of optimal key feature distribution. (3) Results: The findings validate the feasibility of the proposed theoretical framework, offering a technical solution for the identification and prediction of user need trends. (4) Conclusions: This research puts forward strategic recommendations for enterprises regarding the optimization of their products. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Central China Normal University is the property of Huazhong Normal University and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

This paper locates audience engagement in the act of content creation and considers the levels of engagement that content creation could engender. It explicates the different types of content creators defined in the literature and examines motivating factors for the production of content in a Chinese context. It argues that while some content creators seek to draw attention to the series they support, in other cases the engagement with the international television series is not fully motivated by a desire to interact with the international content and direct audiences to it. Rather, content creators are motivated by a desire to gain exposure for their own craft or to develop transferable skills that help them move between the different categories of content creators. In doing so, we argue that international series can tap into these 'cultural intermediaries' (Hutchinson 2017). [ABSTRACT FROM AUTHOR]

This paper explores the intersection between sustainability and social media activity by studying how user-generated content (UGC) creation enacted and communicated social sustainability in times of restricted social interaction. The context is Wuhan in China, a city that implemented a 76-day lockdown in the early stages of the COVID-19 pandemic. Based on a sample of 187 short videos created and posted by Wuhan residents on Douyin (TikTok) during the lockdown, this paper answers this question – how did UGC creators produce short videos on social media to facilitate social connections with others? UGC creators' video-making practices are conceptualised in the typology of Evoking, Performing, Collaborating, and Narrating, and each practice enabled creators to connect and socialise virtually with others, thus contributing to all participants' social sustainability in a pandemic. This study contributes to media management scholarship by adding knowledge to the understanding of two areas: the productive role of media audiences, especially their content production practices and logics; the nature of short videos as media products. [ABSTRACT FROM AUTHOR]

Copyright of Tourism & Management Studies is the property of Escola Superior de Gestao, Hotelaria e Turismo, Universidade do Algarve and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Officially branding local heritage through recognised labels is a strategy that regions can use to promote economic development. Regions increasingly seek more sustainable tourism development, which can be captured by the quality of local tourist service development. This paper examines whether the UNESCO World Heritage Site (WHS) label is associated with local tourism development of a higher quality and offers the first comparative study across European regions. Using TripAdvisor reviews of over 38,000 European locations, our results reveal a positive correlation between WHS labelling and measures of perceived quality and breadth of local tourist services. [ABSTRACT FROM AUTHOR]

This study aimed to evaluate the psychometric properties (factor structure, reliability and construct validity) of the Motivation of User-generated Technical Instructional Videos (MUTIV) scale. Employing a cross-sectional research design, two rounds of self-administered surveys were conducted in mainland China (N = 271/N = 318). Phase 1 involved item generation through both deductive methods (reviewing existing scales in the literature) and inductive methods (analyzing online discourse). Phase 2 focused on the scale development and questionnaire design. Five factors sixty items were extracted by Exploratory Factor Analysis (EFA; Principal Components Analysis) and confirmed by analysis of moment structures, forming the initial five-factor-16-item MUTIV scale. Phase 3 included the scale evaluation and validation assessment. All goodness of ft indices generated by Confirmatory Factor Analysis (CFA) were found satisfactory (χ2/df = 2.620, root mean square residual = 0.033, root-mean-square error of approximation = 0.076, incremental fit index = 0.934, Tucker-Lewis index = 0.915, comparative fit index = 0.933). Cronbach’s alpha value formed at α = 0.911. The five-factor-16-item MUTIV scale was successfully verified and developed. The MUTIV scale effectively assesses the motivation to produce user-generated technical instructional videos, identifying key factors such as sharing, membership, competence, autonomy, and reward. Technical and Vocation Education and Training (TVET) stakeholders and media platforms can use this tool to promote and assess motivation in educational contexts.

Copyright of New Media Research is the property of New Media Research Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Preference prediction is the building block of personalized services, and its implementation at the group level helps enterprises identify their target customers effectively. Existing methods for preference prediction mainly focus on behavioral interactions to extract the associations between groups and products, ignoring the importance of other auxiliary records (e.g., online reviews and social tags) in association detection. This paper proposes a novel method named GMAT for group preference prediction, aiming to collectively detect the sophisticated association patterns from user generated content (UGC) and behavioral interactions. In doing so, we construct a tripartite graph to collaborate these two types of data, and design a deep-learning algorithm with mutual attention module for generating the contextualized representations of groups and products. Extensive experiments on two real-world datasets show that GMAT is superior to other baselines in terms of group preference prediction. Additionally, GMAT is able to improve prediction accuracy compared with its different variants, further verifying the proposed method's effectiveness on association pattern detection. [ABSTRACT FROM AUTHOR]

Tourist information search behavior is one of the most important aspects of risk reduction in the travel decision-making process. Understanding tourists' behavior when searching for online information has been heavily weighted toward user-generated content (UGC), mainly employed deductive research approaches. This study examines the influence of both UGC and marketer-generated content (MGC) on tourists' choice of restaurant, using eye-tracking technology to measure customers' visual behavior combined with retrospective think-aloud interviews. Data was collected from 28 participants in a laboratory-based study. Video recordings of participants' eye movements during the task aided participant recall in follow-up interviews. This study offers insights into the integrated investigation of the influence of UGC and MGC on tourists' information search behavior. It validates and extends understanding of the roles of pictorial information, written information, and other factors, and newly clarifies the role of both positive and negative reviews in influencing restaurant selection from international tourists' perspectives. [ABSTRACT FROM AUTHOR]

Landscape image is the artistic conception schema of people's cognition of the landscape and the core attraction of tourism. The massive UGC data generated from tourists in the internet era can effectively reflect their perception and evaluation of the destination, and then have an important impact on the scientific protection and tourism utilization of the destination. Taking Longji terrace in Guangxi as an example, based on the data of text and picture, this paper deeply analyzes the perceptual characteristics of tourists' cognitive image, emotional image and overall image from the three dimensions of natural landscape image, artificial landscape image and cultural landscape image. The results show that the perception of landscape image of Longji terrace agricultural cultural heritage site presents the overall characteristics of strong material landscape and weak intangible landscape. Agricultural landscapes and residential buildings occupy the dominant position in the landscape image network of agricultural cultural heritage site, and the correlation between them is also the highest. They are the main types of cognitive images. However, the word frequency of image dimensions such as folk festivals, food culture, folk art and water system landscape is low, the correlation to other image dimensions is less, and the perception of cognitive images is weak. The image dimensions of agricultural landscape, residential buildings and food culture are mainly perceived by positive emotional images such as “ satisfaction”, and negative emotions such as “happiness and bitterness” and “ pity” mainly appear in the emotional image perception of road traffic. The overall image perception analysis based on IPA shows that the importance and satisfaction of agricultural landscapes, residential buildings and natural scenery are high, the importance of mountain landscape, water system landscape and food culture are low, but satisfaction is high, and the importance and satisfaction of folk festivals, folk art and community atmosphere are low. [ABSTRACT FROM AUTHOR]

Airports are essential to the global economy, providing significant revenue and driving regional growth. In order to remain competitive and achieve sustainable development, airports must continuously monitor and improve service quality. To this end, understanding traveller perceptions of their experiences is important. While traditional survey-based methods are beneficial, managers are increasingly looking for alternative ways of collecting feedback, such as online reviews. Automated text analysis provides a cost- and time-effective technique with which to analyse large datasets of unsolicited online reviews, providing managers with strategic insights to enhance service quality. This study explores the potential of supplementing traditional airport service quality monitoring methods with automated text analyses to better understand traveller feedback and improve service quality. The results provide new methods to measure airport service quality, offering a fresh perspective on customers' satisfaction with service quality experiences, and highlighting key strategic implications that can help organisations gain a competitive advantage. [ABSTRACT FROM AUTHOR]

Copyright of New Media Research is the property of New Media Research Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

(1) Background: In the current competitive market environment, accurately forecasting user needs is crucial for business success. By analyzing user-generated content (UGC) on social network platforms, enterprises can mine potential user needs and discern shifts in these needs, thereby enabling more efficient and precise product design that aligns with user needs. For newly launched products with a limited presence in the market, the scarcity of UGC poses a challenge to businesses seeking to predict user needs from small datasets. (2) Methods: To address this challenge, this paper proposes a model using correlation analysis (CA) and linear regression (LR) combined with multidimensional gray prediction (a CA-LR-GM (1, N) model) to help enterprises use small sample data to predict user needs. Using the UGC of the Xiaomi SU7 as a case study, this paper demonstrates the prediction of user needs for the vehicle and refines the prediction outcomes through an optimization design informed by the principle of optimal key feature distribution. (3) Results: The findings validate the feasibility of the proposed theoretical framework, offering a technical solution for the identification and prediction of user need trends. (4) Conclusions: This research puts forward strategic recommendations for enterprises regarding the optimization of their products.

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. [ABSTRACT FROM AUTHOR]

The main goal of the article is the identification, summarization and analysis of current possibilities for cooperation and pricing of UGC content and the proposal of a taxonomy in the subject area. In order to effectively fulfil the identified goal, a search was carried out of domestic and foreign sources of authors relevant to the investigated issue. In order to achieve the main goal, empirical research was carried out in the form of identification and detailed analysis of nine Internet tools that digitize and platformism the management of UGC content. The analysis will primarily focus on identifying the possibilities of cooperation between brands and content creators, on the forms of created content, on the placement of created content, on the forms of remuneration of content creators and on pricing in the case of paid collaborations. The findings and recommendations from the article are the basis for better decision-making processes in the field of digital marketing and UGC content management. [ABSTRACT FROM AUTHOR]

The higher education system of India is one of the largest systems on the globe, where there is a huge young population. The central government aims to increase the higher education gross enrolment ratio, which will improve the living and economic standards of youth. However, enrolment in higher education is lower for many reasons, including the accessibility of institutions in the rural region and the affordability of learning. The institutional capacity cannot be expanded in an accelerated manner with more than three times the existing capacity in a short period of time. As an alternative, the regulation to award degrees and diplomas through online education is formulated with the assurance of recognition on par with regular degrees and diplomas. The regulatory norms, recognition process, human resources, infrastructure requirements, and quality mechanisms of online programs are reviewed in this paper. [ABSTRACT FROM AUTHOR]

In recent years, numerous studies have investigated the development of methods for video quality assessment (VQA). These studies have predominantly focused on specific types of video degradation tailored to the application of interest. However, natural videos or recent videos generated by users (UGC) present complex distortions that are not easy to model. Consequently, most current VQA approaches struggle to achieve high performance when applied to these videos. In this paper, we propose a novel Transformer-based architecture that extracts spatial distortion features and spatio-temporal features from videos in two specialized branches. The spatial distortion branch leverages a transfer learning strategy where a standard ViT is pre-trained using a masked autoencoder (MAE) self-supervised learning task, and then fine-tuned to predict the distortion type of corrupted images from the CSIQ database. The features from this branch capture degradation at the level of individual frames. On the other hand, the second branch employs a 3D Shifted Windows Transformer (Swin-T) to extract spatio-temporal features across multiple frames. Once again, we use transfer learning to extract rich features by pre-training this 3D Swin-T model on a video dataset for human action recognition. Finally, a temporal memory block hinged on an attention recurrent neural networks is proposed to predict the final video quality score from the spatio-temporal sequence of features. We evaluate the performance of our method on two popular UGC databases, namely KoNViD-1k and LIVE-VQC. Results show it outperforms state-of-the-art models on the KoNViD-1k database, achieving a SROCC performance of 0.927 and a PLCC of 0.925, while also delivering highly competitive results on the LIVE-VQC database.

The Web 2.0 era and the following phases of web development bring new challenges to businesses, but also new opportunities to establish and maintain relationships with market participants, indulge in direct contact with customers and learn about their needs, emotions and opinions. The advancement of content creation and sharing technologies creates an opportunity to collect information from anyone with access to the Internet. User-generated content (UGC) information is increasingly supporting decision-making and analysis for various types of business, management or marketing activities. Such information is also increasingly used as a source of data in scientific research. The present study seeks to evaluate the relevance of UGC in scientific research and the scope and ways in which content created by Internet users can be used by researchers of phenomena existing in the service sector. To achieve this goal, a bibliometric literature review (quantitative analysis of publications, identification of research collaborators, co-author analysis, co-citation analysis and co-word analysis) was conducted covering articles between 2012 and 2022 published in journals indexed in the Scopus database. The analysis used descriptive statistics and text and content analysis. A significant increase was observed in publications between 2020 and 2022. Among the various service branches, the researchers most often chose data sets in the form of comments posted online by customers of tourism industries, mainly those using accommodation services, but also restaurants. TripAdvisor was observed to be the most frequently used data source. In their analysis, the authors used both qualitative and quantitative methods, as well as a combination of them. It is observed that more sophisticated machine learning algorithms have been implemented for text analysis. Finally, the paper also presents future research recommendations.

Copyright of New Media Research is the property of New Media Research Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Technology has changed customer preferences, such as cashless transactions. E-wallets like OVO have grown rapidly due to these tendencies. This Indonesian e-wallet promotes its services and lets users voice their perceptions on Instagram. This study measured customer perceived value (CPV) for OVO e-wallet service using PERVAL dimensions on the official Instagram accounts OVO (ovo id). Topic modelling-Latent Dirichlet Allocation (LDA) analyzes 10.054 customer perception rows from January to June 2021. OVO customer perception data showed nine Instagram topics. The OVO customer perception dataset included topic-related emotional, social, and quality values. This study may improve OVO's services. [ABSTRACT FROM AUTHOR]

This study examines BCA Mobile, Livin by Mandiri, and BNI Mobile, the top five most popular mobile banking services in Indonesia, to determine consumer sentiment toward service quality. Using Google Play Store user generated content (UGC) and sentiment analysis, we discovered that all three mobile banking services provided poor service quality. Given the numerous negative opinions, efficiency, compliance, system availability, and privacy must all be considered. According to this study, these three mobile banking services must improve service quality to retain customers. [ABSTRACT FROM AUTHOR]

The goal of this paper is to examine the awareness and implementation of the University Grants Commission (UGC) quality mandate initiatives among teachers in higher education institutions. It focuses on both government-affiliated and private institutions, comparing the differences in awareness and implementation between these groups. Data was collected through a survey of teachers, which included questions on their familiarity with the UGC quality mandate, participation in related workshops, and views on the usefulness of these initiatives. The analysis involved descriptive statistics and hypothesis testing, including T-tests and Chisquared tests. The results showed that teachers from both government and private institutions demonstrated a high level of awareness and positive implementation of the UGC quality mandate, with no significant differences between the two groups. This suggests that the UGC's initiatives have been effectively communicated and adopted across different types of institutions. The study concludes that current strategies are effective but recommends further qualitative research to explore any underlying factors contributing to this high compliance and to identify potential areas for improvement. [ABSTRACT FROM AUTHOR]

This research explores the use of TikTok as a new tool for political communication strategy in the context of the 2024 Presidential Election in Indonesia, focusing on the User Generated Content (UGC) model and the role of "Proxy" as a key component. The concept of UGC emphasizes how social media, particularly TikTok, influences political dynamics by allowing active public contribution to creating and disseminating political content that can shape public opinion. In the new media era, the Two-Step Flow Model and its modifications illustrate a shift from direct message transmission from opinion leaders to followers towards a more complex integration where opinion leaders and influencers interact not only with their followers but also with mass media through UGC. The research methodology employed is qualitative with a phenomenological approach, enabling researchers to delve into how TikTok is used to disseminate political information and shape public opinion. Data collected and analyzed aim to understand new constructions in political communication through UGC in the digital context. The study indicates that TikTok plays a crucial role in political communication strategy in Indonesia, where its success or failure can significantly impact public perceptions of political candidates. Findings provide a foundational basis for strategic recommendations to enhance the effectiveness of political campaigns in an era of rapid technological advancement. Overall, this research not only reveals the evolution of political communication strategies in the digital age but also offers valuable insights for the development of future political campaigns in Indonesia. The intensive use of social media platforms like TikTok as tools to influence public opinion and voter choices is increasingly becoming a critical strategy that needs careful management by political actors and their campaign teams. [ABSTRACT FROM AUTHOR]

Numerous online information systems (e.g., question and answer [Q&A] forums, citizen science communities, and review websites) rely heavily on volunteer contributions. Managers have used monetary incentives to induce individuals to increase their contribution level. However, monetary incentives could also generate adverse effects, which could dampen individuals' intrinsic motivation and lead to lower contribution quality when incentives are small. To address this issue, we propose two intervention strategies, goal-setting and challenge-seeking, and conduct a series of randomized experiments. We find that small monetary incentives, when combined with the appropriate intervention strategies, can motivate users to increase contribution quantity while simultaneously sustaining high quality. Thus, integrating such intervention strategies with small incentives can be a cost-effective way to encourage voluntary contributions. Our research contributes to the literature on incentive provision and provides theoretical and practical implications for platforms relying on voluntary contributions. [ABSTRACT FROM AUTHOR]

More than 50 years have passed since the first article on destination image was published, and this body of research is still one of the most popular topics in tourism studies. However, limited research has focused on recent advancements and emerging trends in destination image research. Therefore, there is a pressing need for a comprehensive and up-to-date review to evaluate its progress, identify key features, and explore future research possibilities. Employing a meta-analysis methodology, this study scrutinized 178 articles from 11 esteemed academic journals published between January 2012 and April 2023. The analysis encompasses various aspects, including the publication trends, journal distribution, productive authors, research topics and research methods. Furthermore, this study introduces the concept of “e-WOM image,” which offers a profound understanding of the intricate associations among various topics within the domain of destination image research. The exploration of “e-WOM image” as a potential research field holds promise for advancing the field of destination image research in the coming years.

The COVID-19 pandemic has caused many entrepreneurs and small and medium enterprises (SMEs) to adapt their business models and business strategies to the consequences caused by the pandemic. In order to identify the main innovations and technologies adopted by SMEs in the pandemic, in the present study, we used a database of 56,941 tweets related to the coronavirus to identify those that contained the hashtag #SMEs. The final sample was analyzed using several data-mining techniques such as sentiment analysis, topic modeling and textual analysis. The theoretical perspectives adopted in the present study were Computer-Aided Text Analysis, User-Generated Content and Natural Language Processing. The results of our analysis helped us to identify 15 topics (7 positive: Free support against Covid-19, Webinars tools, Time Optimizer and efficiency, Business solutions tools, Advisors tools, Software for process support and Back-up tools; 4 negative: Government support, Payment systems, Cybersecurity problems and Customers solutions in Cloud, and and 4 neutral: Social media and e-commerce, Specialized startups software, CRMs and Finance and Big data analysis tools). The results of the present study suggest that SMEs have used a variety of digital tools and strategies to adapt to the changing market conditions brought on by the pandemic, and have been proactive in adopting new technologies to continue to operate and reach customers in a connected era. Future research should be directed towards understanding the long-term effects of these technologies and strategies on entrepreneurial growth and value creation, as well as the sustainability of SMEs in the new era based on data-driven decisions. [ABSTRACT FROM AUTHOR]

Ancient canal towns in Jiangnan have become important tourist destinations due to their unique water town scenery and historical value. Creating a unique tourist image boosts these ancient towns' competitive edge in tourism and contributes significantly to their preservation and growth. The vast amount of data from social media has become an essential source for uncovering tourism perceptions. This study takes two ancient towns in Shanghai, Zhaojialou and Fengjing, as case study areas. In order to explore and compare the destination images of the towns, in the perception of tourists and in official publicity, machine learning approaches like word embedding and K-means clustering are adopted to process the comments on Sina Weibo and publicity articles, and statistical analysis and correspondence analysis are used for comparative study. The results reveal the following: (1) Using k-means clustering, destination perceptions were categorized into 16 groups spanning three dimensions, "space, activity, and sentiment", with the most keywords in "activity" and the fewest in "sentiment". (2) The perception of tourists often differs significantly from the official promotional materials. Official promotions place a strong emphasis on shaping the image of ancient towns based on their historical resources, presenting a more general picture. Tourist perception, which is fragmented, highlights emerging elements and the experiential activities, along with the corresponding emotional experiences. (3) Comparing the two towns, Fengjing Ancient Town stands out, with more diverse tourist perceptions and richer emotional experiences. This underscores the effectiveness of tourism activities that use space as a media to evoke emotions, surpassing the impact of the spaces themselves. [ABSTRACT FROM AUTHOR]

The scientific consensus on the existence, the human origin, the seriousness of the consequences and the urgency of adopting immediate solutions to climate change is growing, as successive reports by the Intergovernmental Panel on Climate Change have shown. The alteration of global climate patterns experienced in recent years confirms the worst prediction models, and yet there are still voices that question these facts. This study proposes to examine the interpretative frameworks underpinning public discussion of climate denialism on the online video platform YouTube. To this end, it presents an analysis of the 50 most popular videos under the label of climate denialism in Spanish, paying attention to the key issues and figures on which the denialist ideology is articulated and the attitude of support, neutrality or rejection on the part of those responsible for the content. The results obtained show the generalized rejection of the denialist discourse among Spanish-speaking content creators, who resort to experienced sources and contrasted resources to refute these arguments, as well as the politicization of scientific knowledge in terms of debate, uncertainty, mistrust and lack of commitment to deal with the consequences. [ABSTRACT FROM AUTHOR]

In this study, we build an economic model to explore the user-generated content (UGC) subsidy issue in the context of two-sided UGC platforms. Most UGC platforms subsidize content providers to encourage them to provide more content with higher quality. However, are these subsidies effective? Which type of subsidy is more effective? Here, we examine and compare the effectiveness of different types of subsides for UGC platforms. First, although the underlying reasons are different, both quantity and attention subsidies can induce users to provide more content with higher quality. Second, given the same level of small subsidy, the attention subsidy is more effective in encouraging better UGC quantity and quality. Third, the optimal subsidy level positively depends on the content provider's sensitivity to the subsidy. In most cases, the magnitude of the optimal quantity subsidy should be larger than that of the optimal attention subsidy. Finally, to maintain or improve UGC quality, the platform should set a threshold to restrict the amount of content accessible to viewers. Doing so can prevent content providers from offering more content at the expense of content quality. [ABSTRACT FROM AUTHOR]

This paper aims to investigate the current performance of budget hotel hotels in Macao via online reviews from hotel visitors. Both quantitative and qualitative methods were employed to analyze 44,703 budget hotel reviews, which collected from a popular online travel agency platform. The findings show that the budget hotels in Macao are clustered in Macao island, where many historical and cultural attractions are located. Macao budget hotels have the advantages in price and location, which mainly attract tourists in "Family trip," "Friends outing" and "Business trip" travel modes. The hotels are generally performing well, but with problems in marketing strategies, service quality and hotel hygiene. This paper provides advice and recommendations for the development of budget hotels with reference to the research findings. [ABSTRACT FROM AUTHOR]

The use of social media and location-based networks through GPS-enabled devices provides geospatial data for a plethora of applications in urban studies. However, the extent to which information found in geo-tagged social media activity corresponds to the spatial context is still a topic of debate. In this article, we developed a framework aimed at retrieving the thematic and spatial relationships between content originated from space-based (Twitter) and place-based (Google Places and OSM) sources of geographic user-generated content based on topics identified by the embedding-based BERTopic model. The contribution of the framework lies on the combination of methods that were selected to improve previous works focused on content-location relationships. Using the city of Lisbon (Portugal) to test our methodology, we first applied the embedding-based topic model to aggregated textual data coming from each source. Results of the analysis evidenced the complexity of content-location relationships, which are mostly based on thematic profiles. Nonetheless, the framework can be employed in other cities and extended with other metrics to enrich the research aimed at exploring the correlation between online discourse and geography. [ABSTRACT FROM AUTHOR]

TripAdvisor is a popular review platform, where users post reviews for the same place, including duplicate reviews. This duplication can skew research results and visitors' perceptions. To address this issue, we analyze TripAdvisor reviews in 3 languages from 20 attractions in 2 UNESCO heritage-listed cities. We identify 3 types of motivations for multiple reviews: hedonic, utilitarian, and publishing issues. Our study recommends that online review platforms implement strategies to mitigate this and advises researchers to on how to overcome duplicate reviews in their research. [ABSTRACT FROM AUTHOR]

This article presents a discussion on the main linguistic phenomena which cause difficulties in the analysis of user-generated texts found on the web and in social media, and proposes a set of annotation guidelines for their treatment within the Universal Dependencies (UD) framework of syntactic analysis. Given on the one hand the increasing number of treebanks featuring user-generated content, and its somewhat inconsistent treatment in these resources on the other, the aim of this article is twofold: (1) to provide a condensed, though comprehensive, overview of such treebanks—based on available literature—along with their main features and a comparative analysis of their annotation criteria, and (2) to propose a set of tentative UD-based annotation guidelines, to promote consistent treatment of the particular phenomena found in these types of texts. The overarching goal of this article is to provide a common framework for researchers interested in developing similar resources in UD, thus promoting cross-linguistic consistency, which is a principle that has always been central to the spirit of UD. [ABSTRACT FROM AUTHOR]