Depression attribution and treatment optimism on TikTok and YouTube Shorts

This study examines the impact of social media engagement: What motivates user participation and consumption on YouTube in Nepal. User engagement metrics are the dependent variable. Likewise, the selected independent variables are content type, video length, frequency of uploads, interaction with viewers, and visual appeal. The primary source of data is used to assess the opinions of the respondents regarding these variables in the context of YouTube in Nepal. The study is based on primary data with 17 respondents. To achieve the purpose of the study, a structured questionnaire is prepared. Descriptive statistics, Kendall’s Tau correlations, and regression models are estimated to test the significance and importance of these factors on user engagement metrics on YouTube in Nepal. The study showed that content type has a positive impact on user engagement metrics on YouTube. It implies that varied and interesting content leads to increased user engagement. The result also showed that video length has a positive impact on user engagement metrics. It implies that optimal video length leads to increased user engagement. Moreover, frequency of uploads has a positive impact on user engagement metrics. It implies that regular uploads lead to increased user engagement. Furthermore, interaction with viewers has a positive impact on user engagement metrics, indicating that active interaction leads to increased user engagement. Similarly, the result also showed that visual appeal has a positive impact on user engagement metrics. It indicates that visually appealing videos lead to increased user engagement. The result also showed that improved visual appeal leads to increased user engagement on YouTube.

Content Marketing: An Action Research Approach To Developing A Customer Engagement Strategy Cynthia Wiggins Purpose: The purpose of this thesis was to provide an online customer engagement (OCE) strategy for a start-up content marketing company based in Barbados called ABC Investments (a pseudonym). A framework which assisted in determining a process for identifying the antecedents and consequences of online customer engagement and the strategy to engage online customers was proposed and tested. Rationale: The ability for a start-up content marketing company to create a strategy and monetize its content depends on the company gaining a good understanding of the antecedents and they influence critical online customer engagement metrics. This thesis aims at contributing to the field of online customer engagement in the context of content marketing by researching the antecedents and outcomes of OCE in a real-world environment. Methodology: The research follows a Mixed Methods Action Research (MMAR) approach utilizing both quantitative and qualitative methods. The quantitative data was collected from a start-up content marketing company via Google Analytics, Facebook, and Instagram to identify the what of the antecedents. The influence of eleven (11) antecedents was tested against six (6) metrics including page views, bounce rate, pages per session, average session duration, average time on page and social engagement rate. The qualitative data was collected from interviews with an in-house employee and used as scaffolding to determine the why and how of the results from the quantitative data collection stage. The Action Research process was used to test the hypothesis after each quantitative and qualitative iteration. Findings and Implications: Originally eleven (11) antecedents were proposed, however on completion of the research twelve (12) antecedents were determined to have an impact on the six (6) online customer engagement metrics. There were two new antecedents which came to the forefront after qualitative analysis; “human personality” as an extension of vividness, and “discovery percentage” which replaced “hashtags”. The results also showed that online customer engagement antecedents could be grouped into two main categories: content-based antecedents, and platform-based antecedents. Additionally, the determination of which antecedents impacted on which consequences for practical application is much more complex than posited in previous literature; with results showing that an antecedent may impact on one online engagement metric positively but not on others. Firstly, the findings point to a need for an iterative process when determining the drivers of customer engagement. Secondly, the findings indicate the necessity for developers of content to first understand the components of their content impact on customer engagement and utilize this understanding to create their online customer engagement strategies. Originality and value of the research: Little research exists on online customer engagement within the context of content marketing, which provides a framework for the understanding of the antecedents that drive customer engagement metrics in a real-world environment. In my research, I provided a list of twelve (12) content and platform engagement antecedents and tested their impact on customer engagement to address this gap in the literature. New content marketing companies and content marketing managers can use the findings to create customer engagement strategies, manage engagement levels, and enhance online engagement. Key Words: Online Customer Engagement, Customer Engagement, Content Marketing, Viral Marketing, Start-ups, Content Strategy, Facebook, Instagram, Google Analytics

The swift advancement in social networking platforms has radically shifted the patterns and nature of how people connect with services and brands in America. The utmost objective of this research project was to implement artificial intelligence together with machine learning approaches for creating predictive models that forecast digital pattern development as well as user commitment through social media interactions within the United States. The data used in this analysis are posts aggregated from two leading online platforms, X-Twitter and Reddit, and consist of user-generated material covering a multifaceted set of topics and opinions. X-Twitter posts are current and real-time and give insight into what is currently being discussed and the opinions that are making headlines, whereas Reddit content provides extensive commentary and user engagement on numerous subreddits. To make the dataset more comprehensive and richer in information, extensive engagement metrics like likes, shares, and comments are used to extend its reach and provide insight into the extent to which users engage with the material presented to them. For this research, we used the multi-model approach so that there would be an exhaustive study and strong predictions, by implementing models such as Logistic Regression, Random Forest, and XGB-Classifier. To assess the models properly, we made use of several performance metrics like Accuracy, Precision, Recall, and F1-score. Logistic Regression only manages to achieve a below-average accuracy, signaling an average level of predictive quality. The Random Forest model fares slightly better with a slightly better accuracy rate, which implies that its ensemble method increases its predictive power to classify instances more effectively. In turn, the XG Boost model took the top spot in the comparison with an accuracy rate, projecting its ability to identify complex patterns in the data and showcasing the highest predictive level among the three models. The use of model outputs can greatly maximize real-time content strategy for brands and organizations seeking to maximize engagement in the USA. Based on user behavior patterns and engagement metrics, models can give insight into what should be posted and when to maximize attention. In campaign optimization, predictive modeling assists U.S. brands in making strategic decisions regarding ad spend allocation. Based on an examination of past performance and engagement metrics, brands can see which content is driving the greatest impact and engagement levels and make strategic investments in high-performance content that genuinely resonates with desired audiences. For public communication and policy, predictive models are particularly helpful in projecting how the U.S. public will react to news announcements, policy initiatives, or campaigns. Boosting the effectiveness of predictive models by incorporating transformer-based NLP models like BERT is one direction to explore in the future.

BackgroundThe proliferation of short video platforms has transformed public health communication, yet the quality of medical information shared on these platforms remains inconsistent. Osteoarthritis (OA), a prevalent and burdensome chronic condition, is frequently featured in online health content. However, the reliability of such information has not been systematically evaluated across major Chinese short video platforms. To assess and compare the quality and reliability of OA-related health information on TikTok and Bilibili, and to examine the influence of uploader type and user engagement metrics on content quality.MethodsIn this cross-sectional study, a total of 189 OA-related videos were collected from TikTok (n = 96) and Bilibili (n = 93) using a standardized search strategy. Four validated instruments—the Journal of the American Medical Association (JAMA) benchmarks, modified DISCERN (mDISCERN), Global Quality Score (GQS), and Health on the Net Code (HONcode)—were used for video assessment. Each video was independently rated by two trained reviewers. Differences in quality scores were compared across platforms and uploader types (health professionals vs. non-professionals). Spearman correlation analysis was conducted to explore associations between video quality and engagement metrics (likes, comments, shares, favorites).ResultsTikTok videos exhibited significantly higher median scores on JAMA (2.4 vs. 2.1, P = 0.001), GQS (3.0 vs. 3.0, P = 0.006), and HONcode (11.0 vs. 9.3, P = 0.005) compared to Bilibili. No significant difference was observed for mDISCERN scores. Videos uploaded by healthcare professionals had significantly higher GQS (P = 0.004) and HONcode scores (P = 0.010) than those from non-professionals. User engagement metrics were positively correlated with content quality, particularly on TikTok (e.g., likes vs. JAMA, r = 0.732, P < 0.001).ConclusionsOA-related videos on TikTok demonstrate higher overall quality and reliability compared to Bilibili, especially when created by healthcare professionals. User engagement metrics are positively associated with information quality, underscoring the importance of expert-led digital health communication. These findings highlight the need for platform-level interventions to promote trustworthy content and improve the digital health information ecosystem.

Previous studies of code-learning behaviors have been conducted in structured educational settings, utilizing student engagement metrics such as homework submission, task completion, and interactions with instructors. These types of metrics, however, are absent in open online coding platforms. To characterize autonomous code-learning behaviors in an online community, this work applied Benford’s law to analyze user engagement metrics of trending projects on Scratch, the world’s largest online coding platform for young learners. Statistical analysis revealed that the extent of conformity to Benford’s law is independent of the project categories. Of all four user engagement metrics, the views metric exhibited the strongest conformity to Benford’s law, while the remixes metric–the metric most closely associated with code-learning behaviors–showed the greatest deviation from Benford’s law. This was confirmed by Pearson’s χ² test, Nigrini’s (2012) mean absolute deviation test, and an evaluation of the mantissas of the user engagement metrics. This study demonstrates that the extent of conformity to Benford’s law can be used as novel features for characterizing autonomous code-learning behaviors in unsupervised online settings. The results of this work pave the way for future studies to correlate the extent of conformity to Benford’s law with specific elements of code that attract autonomous learning, providing opportunities to optimize the content and design of online coding platforms.

Online controlled experiments, e.g., A/B testing, is the state-of-the-art approach used by modern Internet companies to improve their services based on data-driven decisions. The most challenging problem is to define an appropriate online metric of user behavior, so-called Overall Evaluation Criterion (OEC), which is both interpretable and sensitive. A typical OEC consists of a key metric and an evaluation statistic. Sensitivity of an OEC to the treatment effect of an A/B test is measured by a statistical significance test. We introduce the notion of Overall Acceptance Criterion (OAC) that includes both the components of an OEC and a statistical significance test. While existing studies on A/B tests are mostly concentrated on the first component of an OAC, its key metric, we widely study the two latter ones by comparison of several statistics and several statistical tests with respect to user engagement metrics on hundreds of A/B experiments run on real users of Yandex. We discovered that the application of the state-of-the-art Student's t-tests to several main user engagement metrics may lead to an underestimation of the false-positive rate by an order of magnitude. We investigate both well-known and novel techniques to overcome this issue in practical settings. At last, we propose the entropy and the quantiles as novel OECs that reflect the diversity and extreme cases of user engagement.

The rapid advancement of digital technologies has transformed content creation, with augmented reality (AR) emerging as a powerful tool to enhance user engagement. Short video content, prevalent on various platforms, offers a unique opportunity to leverage AR interfaces for more interactive and immersive experiences. Existing studies primarily focus on AR applications in static contexts, leaving a gap in knowledge regarding its effectiveness in dynamic short video environments. The objective of the study is to develop a framework that leverages AR technology to enable users to create dynamic video content, facilitating a more engaging storytelling experience. Key variables, including engagement level, learning experience, visual appeal, immersive experience, social interaction, and user empowerment, were measured to assess the impact of AR integration. The dataset includes 20 participant profiles that include information on their gender, age, education, and experience with AR, along with primary device usage with preferred platforms. A mixed-method approach was adopted, combining qualitative user feedback and quantitative analysis of user engagement metrics across various AR-enhanced video content. A structural equation model (SEM) is conducted to examine the relationship between various features of AR, user interaction, and engagement. The result demonstrates that integrating AR interfaces significantly enhanced user engagement levels, visual appeal, and immersive experiences in short video content creation. Quantitative analyses revealed strong positive correlations between AR features and user empowerment (α = 0.84), learning experiences (α = 0.82), and social interactions (α = 0.81). This research contributes to the understanding of AR’s role in dynamic digital storytelling, providing valuable insights for content creators and developers seeking to innovate in this rapidly evolving landscape.

Reply to “Dermatologists in social media: A study on top influencers, posts, and user engagement”: Dermatologist influencers on TikTok

Prior work on user engagement with online media identified web page dwell time as a key metric reflecting level of user engagement with online news articles. While on average, dwell time gives a reasonable estimate of user experience with a news article, it is not able to capture important aspects of user interaction with the page, such as how much time a user spends reading the article vs. viewing the comment posted by other users, or the actual proportion of article read by the user. In this paper, we propose a set of user engagement classes along with new user engagement metrics that, unlike dwell time, more accurately reflect user experience with the content. Our user engagement classes provide clear and interpretable taxonomy of user engagement with online news, and are defined based on amount of time user spends on the page, proportion of the article user actually reads and the amount of interaction users performs with the comments. Moreover, we demonstrate that our metrics are relatively easier to predict from the news article content, compared to the dwell time, making optimization of user engagement more attainable goal.

In the online industry, user engagement is measured with various engagement metrics used to assess users' depth of engagement with a website. Widely-used metrics include clickthrough rates, page views and dwell time. Relying solely on these metrics can lead to contradictory if not erroneous conclusions regarding user engagement. In this paper, we propose the time between two user visits, or the absence time, to measure user engagement. Our assumption is that if users find a website interesting, engaging or useful, they will return to it sooner -a reflection of their engagement with the site -than if this is not the case. This assumption has the advantage of being simple and intuitive and applicable to a large number of settings. As a case study, we use a community Q&A website, and compare the behaviour of users exposed to six functions used to rank past answers, both in terms of traditional metrics and absence time. We use Survival Analysis to show the relation between absence time and other engagement metrics. We demonstrate that the absence time leads to coherent, interpretable results and helps to better understand other metrics commonly used to evaluate user engagement in search.

PurposeVaccine hesitancy has become a critical public health challenge, particularly during the COVID-19 pandemic, amplified by social media facilitating the spread of unverified claims. This study compares COVID-19 and non-COVID-19 vaccine questions posed on a leading USA health Q&amp;A community, examining topical differences, user engagement and themes underlying vaccine hesitancy.Design/methodology/approachQuestions from Medical Sciences Stack Exchange were collected and pre-processed to identify vaccine-related queries. The analysis included 324 COVID-19 vaccine questions and 187 non-COVID-19 vaccine questions for topical differences. Vaccine hesitancy was examined in 75 COVID-19 and 44 non-COVID-19 vaccine questions. User engagement metrics were compared between the two categories.FindingsThe study revealed distinct thematic concerns in COVID-19 versus non-COVID-19 vaccine questions, alongside substantial differences in user engagement metrics. Common strategies to address vaccine hesitancy may not be equally effective during public health crises. COVID-19 vaccine hesitancy primarily stemmed from concerns about rapid development, new biotechnologies and the benefits of post-infection vaccination. Non-COVID-19 vaccine hesitancy focused more on the safety of childhood immunization, manufacturing processes, allergy risks and vaccine-related disease myths. The high popularity but low answer acceptance rate of COVID-19 vaccine questions indicated a substantial unmet information need in addressing hesitancies.Originality/valueThis study compares user concerns regarding established vaccines and those developed during a health crisis, focusing on factors contributing to vaccine hesitancy. It evaluates the extent to which these concerns are addressed on a health Q&amp;A platform, offering insights into information needs and gaps in public understanding of vaccines during routine and emergency health scenarios.

Digital media has transformed health communication, with TikTok emerging as a key platform for disseminating herbalism-related content. Reliance on such content, particularly among young adults, necessitates a critical examination of the presentation and engagement of such information online. This study investigates the representation of herbal wellness on TikTok, examining the relationship between content typologies, influencer credibility, user engagement metrics, and their implications for public health communication. A cross-sectional, descriptive analysis was conducted using the first 120 videos under the #herbalism hashtag, captured on January 30, 2025. Two independent reviewers employed a coding scheme adapted from established methodologies to classify content into themes (e.g., personal experiences, spiritual health, and educational). Statistical analyses, including Mann-Whitney U tests and Spearman's correlation, were performed to evaluate differences and associations in engagement metrics. The study was performed on TikTok, a social media platform characterized by short-form videos and use among young adults. 120 videos were analyzed. Although detailed demographic data of viewers was unavailable, the content primarily targeted a young adult audience. This observational study involved no direct intervention; instead, it systematically categorized and analyzed naturally occurring digital content. Engagement was quantified using metrics such as likes, comments, views, saves, and shares. These were correlated with specific content themes and influencer attributes. Videos referencing spiritual health exhibited higher engagement across multiple metrics, while those featuring personal experiences witnessed lower user interaction. The correlation analyses corroborated these trends, highlighting the influential role of thematic content in shaping audience responses. Findings underscore the dominance of influencer-driven, spiritually resonant herbalism content on TikTok, and raise concerns about frequent uncredentialed expert input. These insights suggest opportunities for integrating evidence-based messaging into digital health communications to mitigate misinformation and enhance public engagement. herbalism, TikTok, content analysis, social media, digital health communication, public health.

The purpose of our study was to describe characteristics of behavior change techniques (BCTs) employed by popular YouTube fitness channels and examine relationships between BCTs used and engagement metrics (e.g. views, likes, comments). Seventy-five videos were coded according to BCT Taxonomy v1. Multi-level modeling was conducted between BCTs and engagement metrics. Fifty-four unique BCTs were used, with "Demonstration of behavior" and "Instruction on how to perform the behavior" used the most. The number of BCTs employed was 12.5 ± 6.65 and BCTs were all unrelated to engagement metrics (ps > 0.05). Application of BCTs within YouTube varies from traditional exercise interventions.

vital for organizations aiming to enhance customer engagement in the modern digital environment. The research evaluates the effects of SFMC on customer engagement metrics through technical documentation combined with industry analyses and case studies of interaction, conversion, and customer loyalty. The study gives a structured way to look at these results. The study looks into how Journey Builder, Email Studio, and Einstein AI features in SFMC help businesses make personalized marketing plans by analyzing data. According to case studies, using SFMC leads to measurable increases in engagement rates, lead conversions, and revenue. Organizations need strategic alignment, combined with robust data management and careful navigation of privacy concerns, to achieve these benefits. The research demonstrates that organizations should unite advanced marketing technologies with proper organizational practices to achieve maximum customer engagement.

User engagement is a relation of emotion, cognitive, and behavior between users and resources at a specific time or range of time. Measuring and analyzing web user engagement has been used by web developers as a means to gather feedback information from web users in order to understand their behavior and find ways to improve the websites. Many websites have been successful in using analytics tools since the information acquired by the tools helps, for example, to increase sales and the rate of returning to the websites. Most web analytics tools in the market focus on measuring engagement with the whole webpages, whereas the insight information about user behavior with respect to particular contents or areas within webpages is missing. However, such knowledge of web user engagement based on contents of the webpages would provide a deeper perspective on user behavior, compared to that based on the whole webpages. To fill this gap, we propose a set of web-content-based user engagement metrics that are adapted from existing web-page-based engagement metrics. In addition, the proposed metrics are accompanied by an analytics tool which the web developers can install on their websites to acquire deeper user engagement information.