Popular Content Research Articles

Caching Method for Information-Centric Ad Hoc Networks Based on Content Popularity and Node Centrality

In recent years, most internet communications have focused on accessing content such as video, web services, and audio. Conversely, traditional Internet communications are inefficient because they are primarily designed for data transfer between hosts. In response, Information-Centric Networking (ICN) has emerged as a content-oriented networking model. The impact of ICN in reducing the location dependency of data and its high compatibility with ad hoc networks has led to research on realizing Information-Centric ad hoc Networks (ICANET). There has also been extensive research into caching content in the network, which is one of the features of ICN. In static networks, methods have been proposed to cache highly popular content in nodes that are more likely to be used for shortest paths. However, in dynamic networks, content with high popularity should be cached on nodes that are more likely to reach all nodes, as missing nodes need to be taken into account. In this study, we propose a cache control scheme for content caching in ICANET that utilizes both content popularity and the closeness centrality of nodes within the ad hoc network as indicators. To realise the proposed method, a new packet flow based on the Pending Interest Table (PIT) and Content Store (CS) was implemented in the forwarding strategy of ICN. The experiments used ndnSIM, a protocol implementation of NDN based on Network Simulator3, which is widely used in wireless network research. The experimental results showed that the cache hit rate could be increased by up to 4.5% in situations with low content bias. In the same situation, the response delay was also reduced by up to 28.3%.

Epidemic dynamics edge caching strategy for 6G networks

By caching popular content on edge servers closer to users to respond to users’ content requests in 6G networks, the transmission load of backhaul links can be reduced. However, the time-varying characteristics of content prevalence leads to the issue that the cache content may not match the user’s needs, resulting in a decrease in cache success ratio. To solve these issues, we proposed a cache distribution strategy based on epidemic dynamics (CDSED) for 6G edge network. First, a 6G edge caching content model (6G ECCM) is constructed to establish the process of cache content propagation among users as an infectious disease propagation process, analyze the distribution of users’ interest in cache content and obtain the cache content state probability prediction equation, and use the cache content state probability prediction equation to predict the cache content prevalence. Second, based on the predicted prevalence results, a prevalence predictive genetic-annealing cache content algorithm (PGAC) is proposed with the optimization objective of maximizing the cache success ratio. The algorithm designs the selection function of the traditional genetic algorithm as a simulated annealing selection function based on the cache content success ratio, which avoids the defect of the genetic algorithm that converges to the locally optimum cache strategy too early and enhances the cache success ratio. Finally, the optimum cache content decision is solved by iterative alternation. Simulation results demonstrate that CDSED strategy can enhance cache success ratio than the LRU strategy, the LFU strategy, and the MPC strategy.

That time I was reincarnated as a problematic trope: Viewer preferences and cultural trends in reincarnation isekai anime

The genre of reincarnation isekai anime, wherein a protagonist dies in their original world only to be reborn in a fantastical one, has grown significantly in global appeal. Yet, this genre’s expansion also normalizes controversial themes such as mental health issues, sexual and violent content, and the sexualisation of virtual minors. This article employs a data-driven approach, analysing user-generated coding of themes and properties from English-speaking fan sites, to critically examine three emergent and interconnected dimensions: the success of series deemed ‘trashy’, the complexities surrounding sexual themes, and the rising appeal of the ‘zero-to-hero’ motif in a post-pandemic world. The findings reveal a paradoxical allure of ‘trashy’ but popular content, complex ethical dilemmas surrounding sexual themes, and an increased resonance of empowerment motifs resulting from the pandemic. These discoveries provoke renewed discussions on anime’s role in shaping and reflecting societal values and raise questions about regulatory challenges in a globalized media setting. The study confirms the need for more nuanced understandings of the genre’s growing global influence, particularly outside of Japan, and calls for continued research to delve deeper into its complexities.

The Influence of Ewom and Fear of Missing Out in the Intention to Buy Skincare Products Through Tiktok

The current social media platform provide a variety of social content that raises new phenomena to become viral and beauty content is one of the most popular content. This study examines the influence of eWOM and Fear of Missing Out (FOMO) in the purchase intention of Skintific skincare products through TikTok. To understand the influence of eWOM, this study combines the information adoption model with self-determination theory as a theoretical basis. Data collection was conducted by an online survey with a purposive sampling method using 235 selected respondents. The data was then processed by applying PLS-SEM to test the proposed hypotheses. The findings show that information usefulness has a positive effect on information adoption, information adoption and FOMO have a positive effect on purchase intention, information credibility and information quantity have a positive effect on information usefulness, and information quality has no significant effect on information usefulness. The direct managerial implication of this finding be a valuable addition to the academic understanding of FOMO in the information adoption model and provide direction for companies in formulating digital marketing designs that focus on the credibility and frequency of content broadcast to provoke consumer desire to use the company's products so as not to be left behind by others.

Cache content placement in the presence of fictitious requests in mmWave 5G IAB networks

Caching popular content at the edge of wireless networks leads to backhaul congestion mitigation. To come up with an effective caching policy, content popularity distribution should be taken into account, which is not accurately known in most practical scenarios. Moreover, the mobile users’ (MU) request pattern may not always follow a well-defined distribution since some malicious MUs may deliberately issue their requests incompatible with the content popularity statistics. In this paper, we consider the problem of cache content placement in a 5G mmWave small cell network that relies on integrated access and backhaul (IAB) technology for pushing contents to MUs. We assume that the IAB node is equipped with a cache and has no prior knowledge about the content popularity profiles; instead, it only relies on the observation of the instantaneous demands to shape its caching policy. Also, malicious MUs may exist whose goals are to increase cache miss by issuing fictitious requests. The IAB node decides on which contents to cache and for how long, given that frequently replacing contents incurs administrative costs. We model the content placement problem as an ”adversarial combinatorial multi-armed bandit process with switching costs (ACMAB-SC)” and present an online learning algorithm for shaping the caching policy. We conduct extensive simulation experiments to evaluate the convergence property and assess the performance of our algorithm in terms of backhaul congestion, delay, and cache hit ratio. We also compare against two baseline online learning schemes, including a CMAB-based approach and a generic caching policy based on the ”Follow the Perturbed Leader (FPL)” algorithm.

Mobility-aware proactive video caching based on asynchronous federated learning in mobile edge computing systems

With the rapid advance of the Internet of Things (IoT) and the surging user-centric smart devices, video services such as short videos, real-time video streaming, and Virtual Reality (VR) gaming are emerging. Traditional cloud-centric caching is no longer able to satisfy the low-latency needs of mobile user groups. Predicting the most popular content in advance and caching it on edge devices such as small base stations (SBS) is a promising solution to alleviate network congestion and supply better Quality of Service (QoS) for mobile users. However, frequently changing popular content, limited edge caching resources, privacy of user data, and user mobility pose significant challenges to edge video caching. To tackle these challenges, we propose an efficient asynchronous federated learning (AFL)-based mobility-aware proactive video caching scheme (AF-MPVC), which significantly improves the overall network performance while preventing the risk of user data leakage. First, we model the proactive video caching problem in mobile edge computing systems and consider the impact of user mobility on AFL, representing the problem as an autoencoder (AE) model loss minimization problem. Then, a proactive video caching algorithm is proposed based on a filtering model, which uses the hidden features obtained from the trained AE model to compute the user similarity to find the popular videos that are most likely to be accessed by users and combine them with the most frequently requested videos. Meanwhile, we take into account the short duration of high mobility users staying on the current SBS and cache some videos to neighboring edge servers to have more redundant space to store more less popular videos. Finally, experiments on three real-world datasets demonstrate that the presented caching scheme outperforms other baseline caching schemes with respect to cache hit rate and latency reduction.

Caching Policy in Low Earth Orbit Satellite Mega-Constellation Information-Centric Networking for Internet of Things.

Information-Centric Networking (ICN) is the emerging next-generation internet paradigm. The Low Earth Orbit (LEO) satellite mega-constellation based on ICN can achieve seamless global coverage and provide excellent support for Internet of Things (IoT) services. Additionally, in-network caching, typically characteristic of ICN, plays a paramount role in network performance. Therefore, the in-network caching policy is one of the hotspot problems. Especially, compared to caching traditional internet content, in-networking caching IoT content is more challenging, since the IoT content lifetime is small and transient. In this paper, firstly, the framework of the LEO satellite mega-constellation Information-Centric Networking for IoT (LEO-SMC-ICN-IoT) is proposed. Then, introducing the concept of "viscosity", the proposed Caching Algorithm based on the Random Forest (CARF) policy of satellite nodes combines both content popularity prediction and satellite nodes location prediction, for achieving good cache matching between the satellite nodes and content. And using the matching rule, the Random Forest (RF) algorithm is adopted to predict the matching relationship among satellite nodes and content for guiding the deployment of caches. Especially, the content is cached in advance at the future satellite to maintain communication with the current ground segment at the time of satellite switchover. Additionally, the policy considers both the IoT content lifetime and the freshness. Finally, a simulation platform with LEO satellite mega-constellation based on ICN is developed in Network Simulator 3 (NS-3). The simulation results show that the proposed caching policy compared with the Leave Copy Everywhere (LCE), the opportunistic (OPP), the Leave Copy down (LCD), and the probabilistic algorithm which caches each content with probability 0.5 (prob 0.5) yield a significant performance improvement, such as the average number of hops, i.e., delay, cache hit rate, and throughput.

A Study on the Impact of Hallyu (Korean Wave) on Korea’s Consumer Goods Exports to China: Panel Analysis Using Big Data and Provincial-Level Data

This study empirically analyzes how the Hallyu (Korean Wave) phenomenon affects Korea’s consumer goods exports to China using Chinese provincial-level panel data covering the period from 2011 to 2020. This paper adopts Baidu Index big data with the keywords “Korean drama”, “Korean movie”, “Korean music”, and “Korean entertainment” as proxy variables for Hallyu. The paper investigates the impact of Hallyu on Korean consumer goods exports by subdividing consumer goods into seven processing steps. In addition to the effect of the composite Hallyu index, the effect of each Hallyu content is also examined. Moreover, this study also investigates the impact of the political issue of the deployment of the THAAD American anti-ballistic missile defense system by dividing the period from 2011 to 2020 into before and after 2016. An export equation that includes income level, the Hallyu index, as well as other variables recognized as factors affecting Korea’s exports in existing studies, is used. Several interesting conclusions have been reached. First, Hallyu in China has a significant impact on Korea’s exports of non-durable consumer goods and processed household food and beverages to China. Second, the political issue of the deployment of THAAD has a negative impact on Korea’s exports of consumer goods to China. Third, among the four types of Hallyu content, dramas, as the most popular content in China, have the greatest influence on Korea’s exports of consumer goods to China.

Low-complexity collaborative caching strategy based on spatio-temporal graph convolutional model

To meet the low delay requirements for data content access in industrial IoT, efficient content caching strategies need to be designed in the Mobile Edge Computing (MEC). Most existing caching policies reduce access delay by predicting content popularity and caching popular content earlier during off-peak traffic, but these strategies only focus on the temporal order of content popularity without fully considering the graph topology of MEC servers and ignore the spatial correlation of content popularity. This paper proposes a low-complexity collaborative caching strategy based on spatio-temporal graph convolutional model (STCC). We integrate graph convolutional neural network and gated recurrent unit to construct spatio-temporal graph convolutional model, which mines the spatio-temporal correlation features of content popularity and make effective predictions on the MEC server graph topology constructed by proximity and semantic relations. Furthermore, we use hierarchical clustering to classify MEC servers into collaborative domains and design a low-complexity heuristic collaborative caching content placement algorithm to minimize the average access delay. Compared with the existing MEC server caching strategy, simulation experiments show that STCC has a better prediction effect of content popularity, and achieves better performance in the two indicators of the cache hit ratio and average access delay.

Sequential multichannel allocation in cache-enabled multicasting small-cell networks

This paper investigates the optimization of multichannel allocation in cache-enabled multicasting small-cell networks (CMSNs). In the traditional fixed channel allocation scheme (FCA), small-cell base stations (SBSs) allocate a fix channel to each local cache popular content. In order to further increasing the utilization of allocated channels, we first propose a sequential multichannel allocation scheme (SMA), in which SBSs allocate a sequential channel set to their cached files dynamically. We formulate the problem of sequential multichannel allocation as two NP-hard non-linear programming problems with two optimization objectives of minimizing the global collision level (GCL) and maximizing the global file-downloading level (GFL), and demonstrate the advantages of the proposed DSCA over FCA. Then, two channel allocation potential games are proposed to achieve GCL and GFL optimization respectively. To achieve Nash equilibria (NEs) of the proposed games, we present a better response based SMA algorithm (BR-SMA) for both two games, and propose an improved better response based SMA algorithm (IBR-SMA) for the game for GCL maximization which have two exploration modes for each decision-maker.

Who Makes Popular Content? Information Cues from Content Creators for Users’ game Choice: Focusing on User-Created Content Platform “Roblox”

User-generated game platforms have emerged as a channel fostering creative activities in online communities. Therefore, this study reveals users’ decision making at UGG platform such as Roblox, where millions of users contribute to game creation and play the game. In particular, teenagers, who are digital natives and known as Generation Z, select a game from various UGGs on Roblox. Thus, this study focuses on how game creator information in personal profile plays a significant role in determining digital natives’ choice of UGG. Drawing the signaling theory, this research focus on creators’ abilities and popularities as indicators for UGG choices. We collect a total of 1,301 fighting games from Roblox and game creator information. To address endogeneity, a two-stage least squares(2SLS) analysis was conducted using instrumental variables. The results show that the number of limited items and followers achieved by the creator positively affect game popularity.

EXTRACURRICULAR SPORTS PARTICIPATION OF HIGH SCHOOL STUDENTS IN THE NORTHERN MOUNTAINOUS AREAS OF VIETNAM

Extracurricular sports are voluntary learner-oriented sports implemented during non-class hours and suited for the student's interests, gender, age, and physical base. This paper was written to provide thorough information about the extracurricular sports engagement of 872 students from 11 high schools in the northern mountainous area of Vietnam. The researchers utilized statistical analysis and sociological surveys to collect such extensive quantitative data. Six criteria were also suggested to evaluate the circumstances. The findings show that the number of students who regularly participate in extracurricular sports accounts for 23.19%, of which males account for 23.92% and females 22.40%, and over 28% of them confirm that they love and are actively involved in playing sports after school. Moreover, all of the schools confirm that they do organize extracurricular sports for their students and most of them are conducted with the guidance of teachers. The most popular contents of extracurricular sports include shuttlecock kicking, football and badminton. In the 2022-2023 school year, there have been 18 intramural sports tournaments and 10 off-campus sports tournaments held with a total of 1392 students attending.<p> </p><p><strong> Article visualizations:</strong></p><p><img src="/-counters-/soc/0707/a.php" alt="Hit counter" /></p>

Cooperative Distributed Uplink Cache over B5G small cell networks.

The emergence of content-centric network has resulted in a substantial increase in data transmission in both uplink and downlink directions. To tackle the ensuing challenges of network congestion and bottlenecks in backhaul links within Beyond Fifth Generation (B5G) networks, data caching has emerged as a popular solution. However, caching for uplink transmission in a distributed B5G scenario poses several challenges, including duplicate content matching and users' obliviousness about cached contents. Furthermore, it is important to maximize available space by caching the most popular contents in a distributed manner. In this paper, we propose two schemes for uplink transmission in distributed B5G SCNs. The first scheme focuses on content matching to eliminate duplicate contents among distributed caches, while the second scheme redistributes un-duplicated cached contents among distributed caches based on their available space and content's size. These approaches aim to enhance energy and spectral efficiency by reducing unnecessary uploads and optimizing distributed content caching, in addition to improve the content delivery. The analysis shows that the proposed schemes outperform the existing schemes by improving the cache hit ratio, cache hit probability, overall distributed cache efficiency, and diversity by 29.17%, 74.89%, 24.17%, and, 80%, respectively. Furthermore, the average throughput, Spectrum Efficiency (SE), and Energy Efficiency (EE) of the access network is improved by 17.78%, 18%, and 78%, respectively. Besides that, the EE and SE of both the sidehaul and backhaul links of the SBSs are also improved.

#lowbackpain on TikTok: A New Frontier for Orthopaedic Medical Education

#lowbackpain on TikTok: A New Frontier for Orthopaedic Medical Education

#DryByChristmas: A patient and public involvement study on women's engagement with humorous pelvic floor muscle training digital nudges on social media.

Patient and public involvement (PPI) is essential for women's health research. Little is known about how women engage with humorous social media and behavioural health messaging targeting pelvic floor muscle training (PFMT). This PPI aimed to understand how women engage with a humorous social media campaign encouraging PFMT. The study findings will influence the co-design of a digital intervention to support women's adherence to PFMT. The Guidance for Reporting Involvement of Patients and the Public Version 2 short form was used to report the study's findings. The study examined public engagement with a humorous social media campaign encouraging PFMT in women. A healthcare professional and comedian ran the campaign following the national guidelines for engagement in PFMT. Instagram analytics gave insight into the demographics of the public who engaged, how they engaged and the most popular content. The behaviour change techniques (BCTs) used in the digital nudges that generated the highest levels of engagement were analysed using the Capability Opportunity Motivation Behaviour Change Wheel. The majority (96%) of the population showing the highest levels of engagement were women aged 35-44 years and were based in the United Kingdom (77%). The Instagram account saw an increase in engagement by 12% over the 3-month campaign, with 22,032 users seeing digital nudges and 2645 engaging with the digital nudges. The preferred way of engaging was using Likes (9723). The common themes in the digital nudges that generated the highest levels of engagement were BCTs associated with the 'social influences' theoretical domain framework that targeted the core behaviour opportunity. The study findings suggest humour may improve women's engagement with online PFMT programmes; however, more rigorous research is required to better understand diverse women's experiences of humorous online PFMT nudges. Future studies may use PFMT mobile apps instead of social media to capture true user engagement and adherence to PFMT more accurately. The insights gained from the study will be taken forward to co-design a digital behavioural intervention as part of a larger study. Members of the public were involved in the co-design of a digital health intervention that will be trialled as part of a larger research study. The public was involved using the social media platform Instagram. Public engagement with a humorous social media campaign to encourage women to engage with pelvic floor exercises was captured using Instagram analytics, for example, the timing of engagement.

Cooperative Caching and Resource Allocation in Integrated Satellite–Terrestrial Networks

Due to the rapid development of low earth orbit satellite constellations, e.g., Starlink, OneWeb, etc., integrated satellite-terrestrial networks have been viewed as a promising paradigm to globally provide satellite internet services for users. However, when the contents from ground data centers are provided for users by satellite networks, there will be high capital expenditures in terms of communication delay and bandwidth usage. To this end, in this paper, a cooperative-caching and resource-allocation problem is investigated in integrated satellite–terrestrial networks. Popular contents, which are cached on satellites and ground data centers, can be accessed via inter-satellite and satellite–terrestrial networks in a cooperative way. The optimization problem is formulated to jointly minimize the deployment costs of storage resource usage and network bandwidth consumption. A cooperative caching and resource allocation (CCRA) algorithm based on a neighborhood search is proposed to address the problem. The simulation results demonstrate that the proposed CCRA algorithm outperforms Greedy and BFS in reducing the deployment costs.

Impact of internet celebrities’ short videos on audiences’ visit intentions: Is beauty power?

Although celebrity endorsements and promotional videos dominated by destination marketing organizations (DMOs) are critical in travel decision-making and destination marketing, as a new type of endorsement, the impact of internet celebrities’ short travel videos on social platforms remains not well understood. To bridge this gap, the current study applied source credibility theory and the match-up hypothesis to construct a conceptual model for examining the impact of internet celebrity endorsements on audiences’ visit intentions. FangQi Kiki, a typical producer of short travel videos on TikTok, was used as a case study. 649 valid samples were taken from respondents who were asked to complete a questionnaire after watching a short video. The results showed that internet celebrities’ trustworthiness and expertise significantly affect audiences’ visit intentions. However, attractiveness in terms of the physical characteristics of internet celebrities and internet celebrity–destination congruence had no significant effect on audiences’ visit intentions. The implications show that “Appearance Fallacy” exists in online influencers’ marketing, and physical appearance may affect the popularity of short travel videos online, but it has no significant effect on promoting destination marketing. Unlike the celebrity effect in conventional endorsements, internet celebrities’ short videos belong to content marketing, where popular content is more important than an internet celebrity’s personality. This research adds to the body of knowledge concerned with internet celebrity endorsements in tourism, providing valuable insights for DMOs to project short video marketing.

Impact of Segmentation and Popularity-based Cache Replacement Policies on Named Data Networking

The data distribution mechanism of internet protocol (IP) technology is inefficient because it necessitates the user to await a response from the server. Named data networking (NDN) is a cutting-edge technology being assessed for enhancing IP networks, primarily because it incorporates a data packet caching technique on every router. However, the effectiveness of this approach is highly dependent on the router's content capacity, thus requiring the use data replacement mechanism when the router capacity is full. The least recently used (LRU) method is employed for cache replacement policy; yet, it is considered ineffective as it neglects the content's popularity. The LRU algorithm replaces the infrequently requested data, leading to inefficient caching of popular data when multiple users constantly request it. To address this problem, we propose a segmented LRU (SLRU) replacement strategy that considers content popularity. The SLRU will evaluate both popular content and content that has previously been popular in two segment categories, namely the probationary and protected segments. Icarus simulator was used to evaluate multiple comprehensive scenarios. Our experimental results show that the SLRU obtains a better cache hit ratio (CHR) and able to minimize latency and link load compared to existing cache replacement policies such as First In, First Out (FIFO), LRU, and Climb.

Exploring Nexus of Social Media Algorithms, Content Creators, and Gender Bias: A Systematic Literature Review

Drawing on the PRISMA framework, this study systematically investigates the dynamics between social media algorithms, content creators, and gender bias. An analysis of 18 quantitative and mixed-method studies from the Web of Science and Scopus databases, spanning 2019 to 2023, uncovers three main research trajectories: algorithms' influence on gender bias, their role in shaping content, and the interactions between algorithms, gender bias, and content creators. The review synthesizes diverse theoretical approaches and models, offering comprehensive insights into the complex nexus of algorithms, gender bias, and content creators. The application of varied research methodologies, including experiments, surveys, and content analyses, facilitates a thorough examination of algorithmic impacts. The chosen studies, focusing on different social media platforms and algorithmic features, reflect the varied interests of researchers. The findings reveal that algorithms perpetuate gender stereotypes by processing and learning content imbued with gender biases and further marginalizing gender minorities, reinforcing binary gender norms. The algorithmic curation of popular content also introduces inequities among content creators. Highlighting the need for equitable and inclusive digital environments, this review advocates for ethical content creation and algorithmic practices to mitigate gender bias and foster equality on social media platforms.

Innovating Online Otolaryngology: The Development of the ENT Content Engagement and Quality Index for Audiovisual Material.

Introduction The advent of social media platforms, notably TikTok, has significantly transformed the landscape of health information dissemination, offering both opportunities and challenges for public health communication. This study focuses on TikTok's influence on sinus health information, addressing the dual challenge of widespread engagement and the risk of misinformation in otolaryngology, or ENT (ear-nose-throat), information online. We introduce the ENT Content Engagement and Quality Index (ENT-CEQI), a pioneering tool designed to assess the quality and engagement of ENT-related content on TikTok, aiming to improve public understanding of sinus health. Materials and methods Our methodology involved a systematic analysis of sinus health-related content on TikTok. We collected data on the top 100 most popular videos using the hashtag #sinus at two different points in time, analyzing engagement metrics and content quality. The ENT-CEQI was developed to evaluate content, incorporating both quantitative engagement metrics (views, likes, comments, shares, favorites) and qualitative assessments (accuracy, clarity, relevance, practicality, engagement). The study employed statistical analyses, including trend analysis, correlation analysis, principal component analysis (PCA), KMeans clustering, regression analysis, and the Kruskal-Wallis test, to understand the dynamics of content engagement and quality. Results Initial findings revealed significant disparities in engagement metrics and quality scores among different content creator categories, with physician-generated content showing the highest engagement and quality. The trend analysis indicated shifts in content popularity and quality over time, with a notable increase in views and likes for private company content. The PCA and clustering analyses identified distinct content clusters, offering insights into viewer engagement patterns. Regression analysis and the Kruskal-Wallis test, however, did not find significant predictors (p-value: 0.3916) of content quality or differences in likes across content types, suggesting complex factors influencing content engagement and quality perception. Discussion The study highlights TikTok's potential and pitfalls in disseminating sinus health information. The introduction of the ENT-CEQI represents a major step toward enhancing the evaluation of health content on social media, emphasizing the importance of accuracy, clarity, and relevance in public health communication. The analysis underscores the complexity of social media engagement and the need for robust tools to assess content quality. It also points to the critical role of healthcare professionals in engaging with the public through social media to combat misinformation. Conclusions TikTok serves as a potent platform for sinus health education, capable of reaching wide and diverse audiences. The ENT-CEQI emerges as a vital tool for assessing the quality and engagement of ENT-related content, guiding content creators toward producing more reliable and informative content. This study contributes to the understanding of social media's role in health communication, advocating for the strategic use of innovative tools such as the ENT-CEQI to enhance public health outcomes through improved online health education and misinformation management.