User-generated Content Platforms Research Articles

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.

Impacts of generative AI on user contributions: evidence from a coding Q &A platform

AbstractThis study investigates the short-term impact of generative AI, exemplified by the introduction of ChatGPT, on user contributions in a coding Q&A platform. We find that the introduction of ChatGPT led to a reduction in the number of high-quality answers provided by users, particularly among highly engaged contributors, despite an overall increase in answers. We identify two key mechanisms: (1) increased perceived question sophistication despite no actual change in content and (2) reduced motivation of loyal users in providing answers in the face of AI-generated alternatives. The findings suggest that while generative AI can facilitate value creation on user-generated content (UGC) platforms, it also poses challenges in retaining core contributors and managing content quality. The paper contributes to the literature on the impact of AI adoption on platforms and suggests practical implications for UGC platform management, such as the need for AI content disclosure measures to retain engaged users.

EXPRESS: Status Downgrade: the Impact of Losing Status on a User-generated Content Platform

Non-financial incentives such as badges, ranks, and status are often used to encourage user participation on online platforms. This study focuses on the effect of one such incentive, “status,” in the context of a thirdparty restaurant-review platform. In contrast to previous research that has mainly focused on the effects of such incentives on subsequent contributions from users who gained statuses, we explore how the intrinsic and perceived quality of content generated by users is impacted after users lose their statuses. Using natural language processing (NLP) techniques to extract quality metrics from online reviews in our dataset, we exploit a quasi-experimental setting and demonstrate that even though the intrinsic quality of reviews significantly decreases after a reviewer is demoted by a platform, consumers on the platform nonetheless perceive these reviews as disproportionately useful. We draw on inequity theory and the elaboration likelihood model to theoretically support our empirical results, as well as conduct mechanism analyses to rule out alternative explanations. Furthermore, we find that temporal associations with a platform or with an elevated status do not moderate the effect of status loss on the intrinsic and perceived quality of reviews written post-demotion. The implications of our findings are significant for platform managers who manage the design of status-driven recognition systems and must determine how the change in status should be displayed on the platform.

The Influence of User-Generated Content on E-Commerce Sales: A Meta-Analysis

User-generated content (UGC) has become a common element of e-commerce platforms, with customers increasingly depending on reviews, ratings, and other kinds of UGC to make purchase choices. While the effect of user-generated content (UGC) on e-commerce sales is generally acknowledged, its quantity and consistency are still debated. This meta-analysis combines data from previous research studies to give a full knowledge of the link between user-generated content and e-commerce sales. Using a variety of empirical research and theoretical frameworks, we investigate the implications of UGC qualities, platform features, and contextual variables on e-commerce revenue success. Our findings provide useful insights into the elements that influence the effect of user-generated content on sales, such as product category, brand reputation, and customer characteristics. Furthermore, we highlight gaps in the current literature and offer future study directions to help us better understand the complicated interaction between user-generated content and e-commerce purchases.

Navigating Platform-Led Affiliate Marketing: Implications for Content Creation and Platform Profitability

Practice- and Policy-Oriented Abstract Recently, some user-generated content (UGC) platforms have introduced shopping features to generate additional commission-based revenue from in-app transactions. These platforms allow creators to produce shoppable content, where promoted products are tagged with affiliate links, and in return, they earn a percentage of the commission the creators receive from merchants This paper aims to investigate the impact of this platform-led affiliate marketing business model on the key stakeholders. Employing a game-theoretic model, our study demonstrates that platform-led affiliate marketing can create a win-win situation for UGC platforms, creators participating in affiliate marketing, and consumers. Furthermore, even creators not participating in affiliate marketing can sometimes benefit indirectly from other creators’ participation. However, we find that platforms may not necessarily benefit from adopting this emerging business model because of potential losses in traffic revenue. These results carry significant managerial implications for platforms regarding whether and how to leverage affiliate marketing. Moreover, our findings suggest that creators may benefit from reducing their production efforts in the face of intensified competition. More importantly, optimal production decisions for creators can vary significantly, depending on specific factors contributing to heightened competition. These findings offer valuable insights that can guide creators in refining their production strategies.

An evolutionary game analysis of algorithmic indirect copyright infringement from the perspective of collusion between UGC platforms and direct infringers.

User-generated content (UGC) is developing rapidly as an emerging platform form, however, the problem of indirect copyright infringement by algorithms is becoming more and more prominent, and infringement governance has become a key act in the development of UGC platforms. When infringement occurs, recommendation algorithms expand the scope and results of infringement, while platforms choose to conspire with direct infringers for their own interests, making it difficult for infringed persons to defend their rights. In order to analyse the influence of different factors in the platform ecosystem on the subject's behavioural strategies, a "platform-infringer" evolutionary game model is constructed, and numerical simulation is used to verify the correctness of the stable results. Based on the simulation results, it is concluded that the factors of uncertain revenue, punishment and reputation loss have important influence on the decision-making behaviour of the subject of infringement governance, and accordingly, the proposed measures on the publishers, platforms and the legal level of the government are conducive to the evolution of the system to the point of positive regulation and stability of rights protection, with a view to promoting the healthier and more stable development of the UGC platforms.

Exploring Learning Outcomes of Wikipedia-Based Assignments in an International Setting: A Case From an Undergraduate Public Administration Course in Taiwan

Wikipedia-based assignments have been adopted successfully in classrooms to improve learning outcomes. However, although this teaching method has been adopted regularly in Western higher education settings, more is needed in an international learning environment. Therefore, this research investigates how employing technology-mediated student-generated Wikipedia content can enhance learning outcomes in an international setting. This study adopted Wikipedia’s online user-generated content platform to create student-generated content in introductory Public Administration classes. We conducted quantitative and qualitative analyses to assess whether a Wikipedia writing assignment increased learning outcomes. Through pre-and post-surveys, students reported increased awareness of their English writing ability, knowledge of Wikipedia content writing, and knowledge of Wikipedia content writing. The study recommends adopting Wikipedia for introductory courses, especially relevant for courses taught in an international environment where English is the language of tuition. Nonetheless, instructors should act as active facilitators in group work, aid students in finding and shaping project topics, and narrow the project scope to ensure that it remains educational within the taught context. Furthermore, it is recommended to have a reasonable project length, design cross-group interactions, and include peer review and technical support in designing the project.

A Study on Video Thumbnails Design Attributes and Their Influence to the Outcome of the Video

In the evolving landscape of user-generated content (UGC) platforms, where videos have become a preferred mode of entertainment and information consumption, the importance of thumbnails in attracting viewers cannot be overstated. This study delves into the intricate relationship between thumbnail design and video success, focusing on the Chinese UGC platform RED. Specifically, the research examines the thumbnails of @Amber Lulu, a vlogger with a consistent style of video and thumbnails. Analyzing a data sample of 30 videos within the "day in my life vlog" category, the study categorizes thumbnails based on three design elements: emphasis on one person vs. a group, two lines of title vs. one line, and a close vs. far background. The results reveal significant correlations between thumbnail design and video metrics, notably emphasizing the positive impact of a distant background, an emphasis on one person, and the use of two lines of heading. Drawing from communication theories such as visual hierarchy, cognitive load theory, and the effect of image characteristics, the study provides valuable insights for content creators seeking to optimize their thumbnails for increased engagement. This research contributes to a nuanced understanding of the role thumbnails play in viewer engagement on UGC platforms, offering practical advice for content creators to design more visually appealing thumbnails for their videos.

Targeting Nearby Influencers: The Acceleration of Natural Triadic Closure by Leveraging Interconnectors

On user-generated content platforms, individuals and firms alike seek to build and expand their follower base to eventually increase the reach of the content they upload. The bulk of the seeding literature in marketing suggests targeting users with a large follower base, that is, high-status influencers. In contrast, some recent studies find targeting lower-status influencers to be a more effective seeding policy. This multimethod article shifts the focus from the follower base of the seeding target to the focal content creator. The authors propose accelerating natural triadic closure by leveraging first-degree followers as interconnectors to target second-degree followers, that is, the nearby (low-status) influencers (who are interconnected with the focal content creator). Empirical studies document that this seeding target is much more effective for building and expanding the follower base, compared with targeting influencers who are not interconnected with the focal content creator—that is, the remote (both high- and low-status) influencers—by 2,300% and 46%, respectively. These studies on the acceleration of natural triadic closure are augmented by a preregistered field experiment to obtain convergent validity of the findings.

How to Regulate Platforms Through a Non-Exploitative User-Generated-Content Levy

The democratization of technology to re-create content and make that content publicly available has spurred a wave of user-generated content (UGC), which has produced remarkable social and economic benefits. However, under current copyright law, UGC creators face the dilemma of being deterred from creating UGC because of the risk of copyright infringement, copyright owners can rarely obtain remuneration from UGC, and UGC platforms profit from UGC without being held liable for copyright infringement. Recent proposals to extend fair use and compulsory licenses to UGC creators and impose direct liability on UGC platforms cannot solve the UGC dilemma due to the inadequate or unreasonable regulation of UGC platforms. This study aims to solve the UGC dilemma by proposing a non-exploitative UGC levy on UGC platforms. We demonstrate the necessity of the non-exploitative UGC levy by conducting a comparative study of existing proposals and illustrate the feasibility of the non-exploitative UGC levy through an institutional analysis of its framework and enforcement mechanisms. Justification of the proposed levy and responses to possible criticism are also provided. The levy scheme also provides inspiration for how copyright law can address burgeoning artificial intelligence-generated content (AIGC).

Buying Future Endorsements from Prospective Influencers on User-Generated Content Platforms

Excessive monetary compensation and existing contractual agreements of influencers limit the ability of many firms to engage in effective influencer seeding. The authors suggest a forward-looking approach of targeting prospective influencers—while they are still largely unknown (e.g., a few months after their platform registration)—and signing them to endorse the firm in the future (e.g., more than a year later). This approach has the potential to significantly reduce costs. However, as only rarely do newly registered users ultimately become influencers (and as signals are weak), the authors propose a novel framework to cope with this rare-event problem. For empirical demonstration and application, the authors conduct data-based simulations using a data set from a worldwide leading audio platform. Every wave of newly registered users is associated with a profit potential stemming from future endorsements by prospective influencers. With knowledge about the order of magnitude of the return on successful influencer spend, managers applying the framework can extract around 20% of this profit potential (if the return is around three times the spend).

Digital Content Model of Social Study Education in User Generated Content Platform as A Media Literacy to Educate Students and Cyber Society

Digital Content Model of Social Study Education in User Generated Content Platform as A Media Literacy to Educate Students and Cyber Society

The liability of online intermediaries under European Union law

This research explores the complex and multifaceted issue of online intermediary liability. It illuminates the challenges arising from the absence of uniform regulations and the need for a collaborative system between online intermediaries and rightsholders. Specifically, the article scrutinizes the liability of online intermediaries under European Union law for violations of legal interests in online content. It strives to balance intermediary liability frameworks and fair competition, drawing attention to the relationship between specific provisions and the concurrent regime outlined in the e-Commerce Directive. Moreover, the article evaluates the consistency of liability frameworks for online intermediaries and their compliance with market functioning rules under the Trade Secrets and the Unfair Commercial Practices Directives. Furthermore, the article consults the consequences of the EU Directive on Copyright in the Digital Single Market, which holds online user-generated content platforms directly responsible for infringing content. And, unlike the original draft, the final version of this directive does not impose general monitoring obligations. Nevertheless, online intermediaries may need to implement filtering measures to avoid liability for unauthorized communication of copyright-protected works to the public. The writing also considers the impact of a prior legal framework, and the Digital Service Act established to address the issue of online intermediaries being held liable for any illegal information disseminated through their platforms. The research underscores the innovative features of the Digital Services Act, acknowledging the challenges of creating a practical legal framework striving to avoid conflicts with relevant laws. Therefore, this paper sheds light on the complex nature of online intermediary liability to the EU approach accordingly.

DanceTrend: An Integration Framework of Video-Based Body Action Recognition and Color Space Features for Dance Popularity Prediction

Background: With the rise of user-generated content (UGC) platforms, we are witnessing an unprecedented surge in data. Among various content types, dance videos have emerged as a potent medium for artistic and emotional expression in the Web 2.0 era. Such videos have increasingly become a significant means for users to captivate audiences and amplify their online influence. Given this, predicting the popularity of dance videos on UGC platforms has drawn significant attention. Methods: This study postulates that body movement features play a pivotal role in determining the future popularity of dance videos. To test this hypothesis, we design a robust prediction framework DanceTrend to integrate the body movement features with color space information for dance popularity prediction. We utilize the jazz dance videos from the comprehensive AIST++ street dance dataset and segment each dance routine video into individual movements. AlphaPose was chosen as the human posture detection algorithm to help us obtain human motion features from the videos. Then, the ST-GCN (Spatial Temporal Graph Convolutional Network) is harnessed to train the movement classification models. These pre-trained ST-GCN models are applied to extract body movement features from our curated Bilibili dance video dataset. Alongside these body movement features, we integrate color space attributes and user metadata for the final dance popularity prediction task. Results: The experimental results endorse our initial hypothesis that the body movement features significantly influence the future popularity of dance videos. A comprehensive evaluation of various feature fusion strategies and diverse classifiers discern that a pre–post fusion hybrid strategy coupled with the XGBoost classifier yields the most optimal outcomes for our dataset.

Research on the Influencing Factors of Consumers' Purchase Intention of Commuter Pants Based on UGC Case Study of Little Red Book App

The Little Red Book App is a typical representative of User-Generated Content (UGC) platform in China, being well-known to the public for its unique sharing function. In this app, numerous users sharing content related to commuting clothing. However, there are very few research on the consumption intention of commuter pants consumers on the UGC platform. This study examines how UGC on the Little Red Book APP influences users' consumption willingness, using the 100 top commuter pants notes about popular commuter pants content as experimental subjects from the Little Red Book platform. Text analysis and network research are conducted based on data collected from Little Red Book open API Dandelion platform. information quality, visual experience and KOL influence significantly influence consumers' purchase intention. This study summarizes the common denominator of the most popular notes for relevant search terms from multiple perspectives and provides insights into how to utilize UGC and KOLs marketing to stimulate consumption desire and promote product sales. Based on the research results, corresponding feasible suggestions have been proposed for UGC e-commerce platforms, brands, and KOLs.

(How) Does User-Generated Content Impact Content Generated by Professionals? Evidence from Local News

Many platforms host user-generated content (UGC) and content developed by professionals side by side. However, thus far, their impact on platform ecosystems has been mostly studied in isolation. In this paper, we use data from a network of 122 local news outlets hosted by an online news platform to study the spillover effects from UGC developed by citizen journalists to the content developed by professional journalists. We use the removal of a status index associated with citizen journalists as an exogenous shock to their supply of UGC to identify these spillover effects. We find that experienced citizen journalists reduce their production of content when this status index is removed. We then find that inexperienced professional journalists increase their output in response to this behavior. However, as a result of these changes, we find a reduction in the overall content hosted by the platform, especially in the case of local news and in more isolated regions. We further show that this is likely to have detrimental effects for the platform. In particular, there is a decline in overall viewership, and the platform may need to hire and pay salaries to more professional journalists to produce enough articles to close the gap left by the departing citizen journalists. Our work contributes to the literature on UGC and online platforms and to the literature on local news. This paper was accepted by D. J. Wu, information systems division. Supplemental Material: The online appendix and data files are available at https://doi.org/10.1287/mnsc.2023.4962 .

Content Quality Assurance on Media Platforms with User-Generated Content

This paper develops a duopoly model for user-generated content (UGC) platforms, which compete for consumers and content producers in two-sided markets characterized by network externalities. Each platform has the option to invest in a content quality assurance (CQA) system and determine the level of advertising. Our model reveals that network effects are pivotal in shaping the platforms’ optimal strategies and user behavior, specifically in terms of single vs. multi-homing. We find that when network effects for producers are weak, consumers tend to engage in multi-homing while producers prefer single-homing. Conversely, strong network effects lead to the opposite behavior. Furthermore, our model demonstrates that user behavior and network effects dictate whether a platform is incentivized to incorporate advertisements and/or invest in CQA. Generally, weak network effects prompt a platform to invest in a CQA system, unless both consumers and producers engage in multi-homing. Our model’s results highlight the importance for platform companies to evaluate the extent of network effects on their platform in order to anticipate user behavior, which subsequently informs the optimal CQA and advertising strategy.

Consequences of Information Feed Integration on User Engagement and Contribution: A Natural Experiment in an Online Knowledge-Sharing Community

This paper investigates the ramifications of information feed integration on user engagements and contributions in online content-sharing platforms by exploiting a natural experiment occurred in a leading knowledge-sharing platform that integrated informal social posts with professional knowledge content in one feed. Our results show that the juxtaposition of incongruous types of content increased mindset switching and cognitive strain, thus hurting user engagements. We also reveal a novel crowding-out effect, viz., the integration heightened concerns that posting informal social posts would dilute the contributor’s professional image, thus inhibiting user contributions. Our findings hold important practical implications for all platforms that host (or are considering hosting) diverse types of user-generated content (UGC). Additional content curation tools can potentially enhance user engagement and retention, but their effectiveness hinges on a foundational and crucial element—the presentation format of heterogeneous content types. Essentially, the value of curating informal social posts in a knowledge-sharing platform would diminish when those content intrudes upon and conflict with the professional domain. This insight underscores that any UGC platforms, when adopting a diversity-oriented strategy, should pay close attention to heterogeneity between different content types for the purpose of optimizing user experiences and promoting user contributions.

User-Generated Content on Online Platforms: A Novel Method for Investigating Heritage Destination Value

ABSTRACT The concept of perceived value has long been attracting special interest in the diverse tourism literature. Despite this widespread attention, the complexity of the value creation process has rarely been empirically addressed, specifically with regard to online user experiences about heritage tourism on tourism review platforms. This study was aimed at investigating the role of user-generated content (UGC) as a source for user engagement in online processes and explored the value of online review experiences in enhancing the overall perceived value of travel destinations. This study bridged the gap by incorporating an online UGC platform experience framework (from an IT-based model), UGC platform value (online value) and overall perceived value of a heritage destination (destination value). A survey was conducted among 304 international tourists in selected heritage destinations within the vicinity of Georgetown, a UNESCO World Heritage Site in Penang, Malaysia. A structural equation modeling analysis revealed that the value of a UGC platform was determined by its perceived usefulness, while the respondents’ overall value of a heritage destination was further influenced by their online review platform experience. The underlying implications, study limitations, as well as suggestions will also be presented.

Two-tier regulation models for the user-generated content platform: A game theoretic analysis

This paper explores the optimal regulation strategies of the government and platforms to provide guidelines for how to maintain the order of user-generated content platforms and prevent adverse social events. The platform decides the intensity of regulation measures to be implemented on its content, and the government decides to enforce either direct or indirect regulation on platform content. The main result is that the platform's regulation strategy depends on the relationship between the loss and fine caused by adverse social events and the cost to improve regulation intensity. We find that the optimal regulation strategy of the government depends largely on the potential probability occurrence of adverse social events and the efficiency of direct regulation. Competition prompts platforms to reduce regulation intensity, and counterintuitively, platform users can benefit from fierce competition. However, weak competition may result in lower consumer surplus in a duopoly market than in a monopoly market. In a duopoly market with greatly disparate intrinsic values of two platforms, the government can appropriately relax regulations.