Content Platforms Research Articles

Accurate multi-behavior sequence-aware recommendation via graph convolution networks.

How can we recommend items to users utilizing multiple types of user behavior data? Multi-behavior recommender systems leverage various types of user behavior data to enhance recommendation performance for the target behavior. These systems aim to provide personalized recommendations, thereby improving user experience, engagement, and satisfaction across different applications such as e-commerce platforms, streaming services, news websites, and content platforms. While previous approaches in multi-behavior recommendation have focused on incorporating behavioral order and dependencies into embedding learning, they often overlook the nuanced importance of individual behaviors in shaping user preferences during model training. We propose MBA (Multi-Behavior sequence-Aware recommendation via graph convolution networks), an accurate framework for multi-behavior recommendations. MBA adopts a novel approach by learning embeddings that capture both the dependencies between behaviors and their relative importance in influencing user preferences. Additionally, MBA employs sophisticated sampling strategies that consider the sequential nature of behaviors during model training, ensuring that the model effectively learns from the entire behavioral sequence. Through extensive experiments on real-world datasets, we demonstrate the superior performance of MBA compared to existing methods. MBA outperforms the best competitor, achieving improvements of up to 11.2% and 11.4% in terms of HR@10 and nDCG@10, respectively. These findings underscore the effectiveness of MBA in providing accurate and personalized recommendations tailored to individual user preferences.

Exploring the Challenges and Opportunities of Blended Learning in a Technology–Enabled Education Environment

Technology integration in education has led to blended learning, a pedagogical approach combining traditional face-to-face instruction with online learning components. Through a comprehensive review of existing literature and empirical research, this investigation examines the impact of blended learning on both students and educators. It delves into this approach's diverse range of technologies, including Learning Management Systems, video conferencing tools, and digital content platforms. Through qualitative and quantitative analysis, the research identifies the opportunities that arise from adopting blended learning. It investigates the advantages of increased student flexibility, personalised learning experiences, and enhanced access to educational resources. The study explores how interactive content and virtual collaboration tools can boost student engagement and motivation. However, the study also uncovers educators' and students' challenges when implementing blended learning. These obstacles include technology access and proficiency issues, consistent communication, and addressing individual learning needs. The study addresses potential resistance to change from traditional teaching methods and the importance of teacher training and support. It compares students' achievements in blended learning environments with those in traditional classrooms and analyses the role of technology in influencing these outcomes. Based on the findings, the research concludes by providing insights into the future implications of blended learning in technology-enabled education environments. Keywords: Blended Learning; Learning Management System; Technology-enabled learning

A game-theoretic analysis for creative mid-roll ads on a content platform

The accessibility of pre-roll ads (PAs) has significantly diminished due to the privilege of skipping ads for paid members. Leading video platforms have introduced a new type of streaming ad, the creative mid-roll ad (CMA), which reduces consumer aversion to ads by tightly integrating them with video. Moreover, paid members are also a target group for CMAs, which may conflict with their interests and potentially reduce their viewing volume. We develop a game-theoretical model to examine the platform's advertising pricing and the advertiser's mode selection process. Counterintuitively, the CMA fee rate at equilibrium decreases as video attractiveness and the CMA's ad conversion ability increase because higher video attractiveness and ad conversion ability enhance the advantages of lowering the CMA fee rate. We find that the advertiser will balance conversion efficiency and price gaps between the two ad modes when making decisions. As increased conversion ability prompts advertisers to invest more, a significant gap in conversion efficiency results in higher equilibrium investment in PA mode compared to CMA mode. A low proportion of paid members results in a better consumer surplus in CMA mode than in PA mode, which is because the CMA mode targets paid members with its advertisements.

On Reverse Dracula and Picky Bites – introduction to study of Urban Dictionary

This article provides an introductory examination of Urban Dictionary, focusing on its attributes and distinctive features. In the article the functionalities of the webpage are explored, the structure of entries is analysed and a typology of definitions is proposed. Through detailed analysis of specific examples from the Urban Dictionary lexicon, the dynamics of this user-generated content platform is discussed. The paper highlights the significance of individual and social perspectives of users as well as the aspects of creativity, humour and narratives included in the definitions. The paper aims to contribute to better understanding of the phenomenon of Urban Dictionary in the context of social linguistics.

Nonlinear moderating effects of individual social engagement in freemium strategies on digital content platforms

PurposeThis study aims to investigate the moderating effects of individual social engagement on the effectiveness of freemium strategies in digital content platforms.Design/methodology/approachThis study involved conducting a randomized field experiment with 74,758 consumers on a prominent e-book platform in China, comparing the effects of offering the first 50 chapters for free against no free content. Additionally, a causal random forest machine learning algorithm was applied to analyze data and optimize strategies based on individual social engagement levels.FindingsThis study indicates freemium strategies on digital content platforms can increase consumer willingness to pay but may reduce social community participation. These effects are moderated by consumers' prior social engagement, with excessive interaction leading to diminishing returns.Practical implicationsThe study offers actionable insights for digital content managers, showing how tailored freemium strategies can effectively balance consumer engagement and revenue generation. The findings suggest that platforms can significantly enhance profitability by moderating free content offerings based on detailed analysis of consumer engagement histories.Originality/valueThis study enhances the understanding of freemium strategies by showcasing their dual impact on consumer willingness to pay and social engagement, and detailing the complex, non-linear effects of individual social engagement, which challenges the traditional linear assumptions in existing literature. Additionally, it provides insights for implementing mixed marketing strategies on digital platforms, where multiple strategies often interact, guiding the effective management of these complexities.

The Students’ Perceived Effectiveness of TikTok as A Means for Learning Unfamiliar Vocabulary at SMAN 10 Mataram

This study examines the perceived effectiveness of TikTok as a tool for learning unfamiliar vocabulary among students at SMAN 10 Mataram. With the rise of social media, TikTok has emerged as a popular platform for educational content, prompting educators to explore its potential in language learning. Through surveys and interviews, this research investigates how TikTok influences vocabulary acquisition, engagement, and motivation in learning. The findings suggest that students view TikTok as an effective and engaging resource that enhances their vocabulary learning experience. The study highlights implications for incorporating social media into educational practices and offers recommendations for leveraging TikTok in language education.

Effects of the Subscription-Based Partitioned Pricing Strategy of Digital Content Platforms on User Willingness to Purchase

This research explores how the partitioned pricing strategy of premium subscriptions affects user willingness to purchase with two studies. Study 1 finds that all-inclusive pricing is more effective than traditional partitioned pricing (i.e., partitioned pricing in Single) and the two new formats of partitioned pricing (i.e., partitioned pricing in Combination and partitioned pricing in Blind Box) in terms of increasing user willingness to purchase due to the induced higher perceived value and perceived price fairness. Study 2 further finds that partitioned pricing in Combination or partitioned pricing in Blind Box (vs. partitioned pricing in Single) can affect perceived value positively via perceived playfulness and negatively via user-perceived price complexity simultaneously. These indirect effects hold across hedonic and utilitarian digital content products. These findings contribute to the literature on the pricing strategies of subscription-based digital content platforms. This research also provides suggestions for digital content platforms to adopt that will help them design their partitioned pricing strategies effectively.

"Exploring Social Media Usage Trends Among Adolescents in a Public Senior Secondary School in Abuja, Nigeria"

This study investigates social media usage trends and prevalence among 260 adolescents in a public senior secondary school in Abuja, Nigeria. A cross-sectional study design was employed, with students aged 13 to 18 years. Data collected revealed that a significant majority of respondents, 63.1% possess a smartphone and indicated a high level of access to social media platforms. Their engagement with social media daily is primarily for social interaction, social comparison, entertainment, and academic support. Furthermore, the findings highlight patterns of usage that vary by gender, with female students displaying a higher, 60% tendency for interaction through visual content platforms, while male students favored gaming and sports-related content. The study also identifies potential concerns regarding the implications of excessive social media use, including mental health issues, cyberbullying, and academic distraction. These results underscore the need for targeted educational programs and parental guidance to promote safe and responsible social media engagement. Overall, this research contributes to a deeper understanding of adolescents' social media behaviors and offers recommendations for fostering a balanced approach to digital engagement in educational settings.

Navigating the Creator Economy: An Analysis of Content Promotion and View Allocation Policies on Digital Content Platforms

Navigating the Creator Economy: An Analysis of Content Promotion and View Allocation Policies on Digital Content Platforms

Legal Analysis of Issues Related to User-Generated Content in Derivative Works

In the information age, the barriers to individual creative production have significantly lowered, leading to new developments in the arts, while also giving rise to various legal issues. One such challenge lies in defining originality and the boundaries of infringement in derivative works. This is especially problematic on user-generated content (UGC) platforms, where new forms of creative expression, such as video mashups, lack clear standards for determining infringement. By comparing international practices and considering the specific context of China, this paper seeks to balance the interests of creators, platforms, and original authors. The goal is to encourage creativity while respecting the copyright of original authors, ultimately achieving a win-win situation in the creative market and fostering cultural innovation and freedom of expression.

Exploring the Challenges and Opportunities of Blended Learning in a Technology–Enabled Education Environment

Technology integration in education has led to blended learning, a pedagogical approach combining traditional face-to-face instruction with online learning components. Through a comprehensive review of existing literature and empirical research, this investigation examines the impact of blended learning on both students and educators. It delves into this approach's diverse range of technologies, including Learning Management Systems, video conferencing tools, and digital content platforms. Through qualitative and quantitative analysis, the research identifies the opportunities that arise from adopting blended learning. It investigates the advantages of increased student flexibility, personalised learning experiences, and enhanced access to educational resources. The study explores how interactive content and virtual collaboration tools can boost student engagement and motivation. However, the study also uncovers educators' and students' challenges when implementing blended learning. These obstacles include technology access and proficiency issues, consistent communication, and addressing individual learning needs. The study addresses potential resistance to change from traditional teaching methods and the importance of teacher training and support. It compares students' achievements in blended learning environments with those in traditional classrooms and analyses the role of technology in influencing these outcomes. Based on the findings, the research concludes by providing insights into the future implications of blended learning in technology-enabled education environments. Keywords: Blended Learning; Learning Management System; Technology-enabled learning

Mapping interpretations of the law in online content moderation in Germany

Content moderation is a vital condition that online platforms must facilitate, according to the law, to create suitable online environments for their users. By the law, we mean national or European laws that require the removal of content by online platforms, such as EU Regulation 2021/784, which addresses the dissemination of terrorist content online. Content moderation required by these national or European laws, summarised here as ‘the law’, is different from the moderation of pieces of content that is not directly required by law but instead is conducted voluntarily by the platforms. New regulatory requests create an additional layer of complexity of legal grounds for the moderation of content and are relevant to platforms’ daily decisions. The decisions made are either grounded in reasons stemming from different sources of law, such as international or national provisions, or can be based on contractual grounds, such as the platform's Terms of Service and Community Standards. However, how to empirically measure these essential aspects of content moderation remains unclear. Therefore, we ask the following research question: How do online platforms interpret the law when they moderate online content?To understand this complex interplay and empirically test the quality of a platform's content moderation claims, this article develops a methodology that facilitates empirical evidence of the individual decisions taken per piece of content while highlighting the subjective element of content classification by human moderators. We then apply this methodology to a single empirical case, an anonymous medium-sized German platform that provided us access to their content moderation decisions. With more knowledge of how platforms interpret the law, we can better understand the complex nature of content moderation, its regulation and compliance practices, and to what degree legal moderation might differ from moderation due to contractual reasons in dimensions such as the need for context, information, and time.Our results show considerable divergence between the platform's interpretation of the law and ours. We believe that a significant number of platform legal interpretations are incorrect due to divergent interpretations of the law and that platforms are removing legal content that they falsely believe to be illegal (‘overblocking’) while simultaneously not moderating illegal content (‘underblocking’). In conclusion, we provide recommendations for content moderation system design that takes (legal) human content moderation into account and creates new methodological ways to test its quality and effect on speech in online platforms.

Strategic analysis of an ad-supported content platform’s subsidy policy: The perspective of the producer’s pricing strategies

We consider a content market with an ad-supported content platform and a representative producer in the presence of altruistic consumers. The platform may launch different subsidy policies (i.e., a monetary subsidy based on the content demand that directly improves marginal profit or a traffic subsidy that directly improves content quality), and the producer creates content under two pricing strategies (i.e., a fixed pricing strategy and a pay-as-you-wish strategy where consumer can pay for the content as they wish). We develop a stylized model and investigate which subsidy policy is a better choice for the platform when the producer is delegated pricing power. Under a fixed pricing strategy, the platform gets a higher profit in the traffic subsidy policy when the consumers’ basic utility is not too low or the quality cost is small, while the producer gets a higher profit in the traffic subsidy when consumers’ basic utility is high or the quality cost is small. Hence, both subsidy policies can achieve the “win-win” situation under certain conditions. Under the pay-as-you-wish strategy, the platform always gets a higher profit in the traffic subsidy policy, while the producer gets a higher profit in the traffic subsidy policy when the consumers’ basic utility for content is high. Hence, only the traffic subsidy policy can achieve the “win-win” situation under certain conditions. Due to the tradeoff between the subsidy enhancement effect on quality and the quality cost, we observe that although the traffic subsidy policy brings a higher content quality than the monetary subsidy policy under both pricing strategies, the producer can increase or decrease his content quality in the traffic subsidy policy compared with the monetary subsidy policy. Our paper provides guidance on how content platforms can provide the right subsidy policy to the producer.

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.

ENHANCING SOFT SKILLS IN TEACHING ENGLISH: A STUDY UTILIZING PADLET APPLICATION AMONG STUDENTS

This research aims to examine the efficacy of utilizing the Padlet application to enhance soft skills in English as a Foreign Language (EFL) education among students at International Information Technology University. Soft skills, such as communication, collaboration, critical thinking, and creativity, are crucial competencies for students navigating the globalized workforce. Technology integration in education has become increasingly important, and Padlet provides a versatile platform for collaborative learning and content sharing. This study adopts a quantitative approach, employing a pre/post-observation design to evaluate the influence of Padlet usage on the development of soft skills among EFL students. Participants are randomly assigned to either an experimental group, engaging with Padlet activities, or a control group receiving traditional instruction. Validated instrument, including performance evaluations, are used to measure soft skills before and after the intervention. Quantitative data analysis techniques are utilized to compare mean scores between groups and assess the effectiveness of Padlet in fostering soft skills. Expected outcomes encompass improvements in communication clarity, increased collaboration in group tasks, enhanced critical thinking through information synthesis, and greater creativity in idea generation. The findings of this study hold implications for EFL educators seeking innovative approaches to skill development and for institutions aiming to effectively integrate technology into language education. By exploring the role of Padlet in enhancing soft skills within the specific context of EFL education at International Information Technology University, this research contributes to a deeper understanding of technology-supported learning and its impact on student outcomes.

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.

Social Learning and Content Quality Under Polarization

Problem definition: This paper studies how polarization influences content consumption and production on digital platforms that monetize consumer engagement. Specifically, we consider a content that advocates a particular position on a divisive issue. Consumers with polarized preferences toward the content’s position are sequentially exposed to the content. Initially, consumers are uncertain about the content quality, but they have the opportunity to learn about it using aggregate consumption metrics and other informative signals provided by the platform. Methodology/results: Using a stylized model, we find that under polarization, social learning based on consumption metrics can mislead consumers to perceive low-quality content as higher quality, even in the long run. Consequently, content providers may decrease their effort to improve content quality. These effects are amplified for more polarizing issues, especially when the content’s position is “mainstream” (i.e., aligned with the majority of the population). Our results thus provide a potential explanation for the proliferation of low-quality, polarizing content on platforms. Managerial implications: We offer normative guidance for content platforms seeking to enhance content quality, including offering consumers information to aid the social learning mechanism and appropriately selecting audience to mitigate the echo-chamber effect. Additionally, we propose payment schemes based on content popularity as an effective means to encourage content providers to improve quality. Funding: This work was supported by the Hong Kong Research Grants Council [General Research Fund 16502920]. Supplemental Material: The online appendix is available at https://doi.org/10.1287/msom.2022.0533 .

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.

(209) TIKTOK FOR LOW T: THE CURRENT LANDSCAPE OF TESTOSTERONE REPLACEMENT THERAPY MISINFORMATION AND SUPPLEMENTATION ON A POPULAR VIDEO PLATFORM

Abstract Introduction Due to challenges diagnosing young men with “low T” secondary to non-specific presenting symptoms, patients have turned towards self-managed forms of treatment obtained from non-medical resources like TikTok without proper risk of future side-effects. Objective Understand the current landscape of symptomatic hypogonadism (i.e., “low T”) content and treatment options on TikTok, the fast-growing social media platform in popularity. Methods Using a proprietary TikTok application program interface (API) program, we queried the most-viewed TRT-related videos based on associated hashtag created from October 2022 – 2023. Videos in a language other than English, lacking textual or verbal content, or deemed unrelated to TRT were excluded. Included videos were subsequently analyzed by two independent reviewers based on the AUA Testosterone Deficiency Guidelines (AUA-TDG). Results Of 562 videos, 50 videos by 22 unique creators met inclusion criteria; since their creation, these videos garnered more than 15 million views, almost 1 million likes, and 35 K shares. While 36% of these videos mentioned TRT would improve erectile function, lean body mass, and/or depressive symptoms (AUA-TDG 14,) only four mentioned inconclusive evidence around TRT improving cognitive function (AUA-TDG 15) or encouraging lifestyle modification prior to starting TRT (AUA-TDG 21). No videos analyzed mentioned the long-term impact of exogenous testosterone on future fertility (AUA-TDG 16). Of the 22 unique creators, only one was an MD trained in family medicine. Most videos (72%, n = 36) were made by companies advertising their supplement. Conclusions Patients are increasingly turning towards non-medical institutions for low T information and supplementation. By the time patients seek medical care, it may be due to improperly advised negative side effects. Given the lack of educational, non-commercial videos about TRT benefits and risks on popular content platforms like TikTok, we urge physicians to actively engage in creating accurate and holistic content to reach patients currently consuming healthcare disinformation. Disclosure No.

Unveiling the Cost of Free: How an Ad-Sponsored Model Affects Serialized Digital Content Creation

Within the field of serialized digital content, platforms have recently introduced the ad-sponsored model to provide free content and attract readers. Although this model potentially benefits readers by removing financial barriers, its impact on content creators remains unclear. Our study investigates the effects of the ad-sponsored model on the content creators’ productivity by leveraging a policy change on a leading web novel platform as a natural experiment. Our findings indicate a significant decline in content creators’ efforts in the ad-sponsored model, suggesting the potential adverse effects. Our results demonstrate that this decline is primarily due to reduced reader engagement, as readers may perceive lower “sunk costs” with free content compared with paid content. Our findings provide valuable insights for serialized digital content platforms adopting or considering the ad-sponsored model, emphasizing the importance of considering both creator and user dynamics when introducing new business models.