Personal Content Research Articles

Professionals’ perspectives on interventions to reduce problematic alcohol use in older adults: a realist evaluation of working elements

ObjectivesThis study set out to understand how (which elements), in what context and why (which mechanisms) interventions are successful in reducing (problematic) alcohol use among older adults, from the perspective...

Exploring Digital Dermatology: An Analysis of Ichthyosis Content on TikTok.

Background TikTok is amongthe most popular social media sites, and its utilization for health information is growing each day. The present studyassesses the popularity and quality of the top 100 most-liked videos on TikTok tagged with "#ichthyosis." This study aims to do so by assessing contributions from physician, and nonphysician sources (such as from patients) to guide healthcare professionals interested in leveraging this platform for public health. Methodology A cross-sectional analysis of the top 100 most-liked videos tagged with "#ichythosis" on TikTok as of January 3, 2024, was conducted. The parameters assessed for this study include observable characteristics, content type, and whether the creator was a physician or nonphysician. The quality of the content was measured using the DISCERN scale. Results Based on these results, 14 of the top 100 videos were posted by physicians with 1,912,975as the mean number of views. There were 86 videos posted by nonphysician creators averaging 2,675,341 views. Videos posted by nonphysician creators had a higher average number of views, number of likes, and number of commentsbut less average saves. Videos made by physicians and educational content had the highest average DISCERN scores, whereas nonphysician, awareness, and personal experience content had the lowest average DISCERN scores. Conclusions Physicians are deemed trustworthy, reliable sources of healthcare-related information on TikTok. This study emphasizes the importance of physicians continuing to provide reliable, evidence-based health information on social media platforms such as TikTok.

Vocational Students' Perception of Motivational Teaching Strategies in English for Specific Purposes Online Classes

The COVID-19 pandemic transformed a traditional classroom setting into an online classroom setting, including the teaching and learning process in the English for Specific Purposes (ESP) context. The change influenced the students’ motivational state. This study aimed to investigate the students’ perception of the demotivating teaching strategies in ESP online learning classes during the COVID-19 pandemic. One hundred and sixty-five students of the Faculty of Vocational Studies in an Indonesian public university participated in the study. They completed a questionnaire that reflected their perception of the least and most motivating teaching strategies. Their perception was rank-ordered to find the teaching strategies that were demotivating. The results showed that the students did not feel motivated when the teachers mentioned that mistakes were a natural part of learning. They also perceived giving challenging tasks, creating group work, inserting personal content into assignments, and offering a variety of material as demotivating practices in ESP online classes. The results are expected to provide a solution and modeling to improve students’ motivational state through motivating teaching strategies in ESP classes, especially in a vocational education setting. Further research should elaborate and expand the studies using more variables since students’ motivation is influenced by many factors.

Digital Marketing Strategies to Build Customer Loyalty: A Systematic Review of Sustainable Financial Benefits

This study examines the role of integrated digital marketing strategies, including personalized content, Customer Relationship Management (CRM) systems, and social media marketing, in building customer loyalty and driving long-term financial benefits. The research design uses a systematic literature review to examine how these digital tools work together to improve things. This is based on theories like relationship marketing and the resource-based view (RBV). The findings demonstrate that integrating these strategies leads to more muscular customer retention, reduced customer acquisition costs, and enhanced return on investment (ROI). Additionally, it confirms that a multi-channel approach, which aligns CRM data with personalized content and social media, provides a more cohesive customer experience, leading to increased customer lifetime value (LTV). The study highlights practical implications for businesses, emphasizing the need for integrated digital strategies to enhance customer engagement and financial sustainability. However, the study's limitations include its reliance on secondary data and the exclusion of emerging technologies like artificial intelligence. Future research should focus on longitudinal studies and explore industry-specific case studies to provide more tailored insights into the evolving landscape of digital marketing.

Development of Personal Beauty App Content for Individuals with Physical Disabilities

Development of Personal Beauty App Content for Individuals with Physical Disabilities

A Study on Data Analytics Techniques Used By Netflix to Personalize Recommendations

This research study investigates the complex processes by which Netflix uses data analytics to personalize suggestions for each user. Knowing the nuances of recommendation algorithms becomes critical as streaming platforms continue to rule the entertainment industry. This study tries to clarify the basic ideas behind personalized content suggestions by thoroughly examining Netflix's approach, including algorithms, data collection techniques, and user engagement measures. It also explores the moral ramifications and privacy issues related to these data-driven behaviors. This study provides insights into how personalized recommendation systems will develop in the digital age by examining the effects on user satisfaction, retention, and overall business success. KEY WORDS Data analytics, recommendation algorithm, user engagement, personalized content.

Electronic newsletter in the system of social communications: prospects for effective use

The article substantiates the necessity to expand research methodologies into the electronic newsletter format. It was acknowledged that modern scientific discourse reluctantly chooses the electronic bulletin as an object of research. At the same time, scholars concur with the fact that this format demonstrates potential while being a user-friendly channel of communicative interaction as well as providing an opportunity to create personalised communication and offer exclusive content, etc. The article examines the examples of the most sought-after newsletters such as: editorial newsletters from The New York Times, aggregated newsletters from SmartBrief, Think with Google resource, The Menu newsletter from SparkToro technical startup, Confident Computing's latest technology newsletter from Ask Leo! website and the female-focused Daily Skimm newsletter from theSkimm media company. The specifics, characteristics, and advantages of this form of interaction with the audience are analysed. It is concluded that tailored personalised content that the user receives via e-mail allows the company to effectively communicate, interact with its target audiences, increasing the level of engagement, building trust and upholding reputation. In this case, the indicators of the website opening, clickability and conversion, make it possible to improve the strategy of email distributions, which will ensure better results. The electronic newsletter is a modern, up-to-date format which helps to perform effective multi-functional social communication, provided it contains interesting, high-quality, interactive, scheduled content aimed at the target audience.

“Shared Online, Made People Envious, Felt Good”

An online survey of 200 participants was conducted on the experiences of sharing material and immaterial artifacts, focusing on interaction with non-media content types. The survey included six categories of sharing: music preferences, travel plans, sports activities, apartments and vehicles, virtual items in online games, and dietary preferences. The study identified factors that motivate participation in content-mediated interaction: discovery, curating self, connectedness, collaboration, enjoyment, and instrumental motivations, as a set of six motivating factors driving contemporary sharing practices. The authors consolidated findings from earlier work on motivations to share personal content, validating the set of motivating factors with the six selected non-media types of content. The results suggest that both extrinsic and intrinsic motivations affect content sharing practices. Furthermore, motivating factors were mapped to the reported positive and negative experiences, in order to understand their relation and to discuss their capacity to guide user experience design.

Analisis Kemampuan Literasi Digital Mahasiswa Jurusan Pendidikan Geografi Fakultas Ilmu Sosial Universitas Negeri Medan

This study aims to analyze the Digital Literacy Skills Students of Geography Education Department, Faculty of Social Sciences, Universitas Negeri Medan. The data collection techniques used were tests and questionnaires as well as direct observation of respondents' social media accounts, then analyzed using descriptive statistical techniques. The results showed that: Overall, the digital literacy skills of Geography Education Department students are classified as good with an average score (70.58) and percentage score (63.58%). When viewed according to its aspects, the knowledge aspect is classified as good with an average score (8.39) and percentage score (64.58%), the attitude aspect is classified as very good with an average score (42.83) and percentage score (85.67%), but the skill aspect is less with an average score (19.35) and percentage score (40.31%). The digital literacy ability of men with an average score (70.71) and percentage score (63.71%) was higher but not significantly different from women who had an average score (70.51) and percentage score (63.52%). Most students of Geography Education Department are not very active in using social media (84.06%) and their uploads are more personal content (30.57%) than work content (4.26%).

What defines Traits, Reputations, and Identity? Personality item content in multi-rater judgments

Personality self- and informant-reports have been ascribed complementary value based on the asymmetric knowledge of the two perspectives. However, this study is the first to investigate what personality (item) content is reflected in the shared and unique components in multi-rater personality judgments. In two large data sets (Sample 1: 664 targets/1,615 informants; Sample 2: 478 targets/1,434 informants), we used latent variable models to separate judgments into variance that is shared across targets and informants (the Trait factor), unique to self-reports (Identity), and unique to informant-reports (Reputation). Then, we predicted the personality items’ loadings for each factor from the items’ content. This included items’ affective, behavioral, cognitive, or desire-related content, observability and evaluativeness, and centrality to identity or reputation. We found that Trait consensus was generally promoted by items reflecting observable, behavioral, but also affective content. Unique self-perceptions were captured especially by cognitions and non-observable content. Evaluativeness had inconsistent effects across samples. Similarly, unique informant-views reflected different content across samples. Both may depend on the types of informants or the available item sample. These insights build the foundation for leveraging the power of multi-rater perspectives on personality for advancing theory and measurement across different perspectives.

Shifting trends shaping content creation in media and newsrooms

This abstract is about content creation in media and newsrooms that includes a concise but accurate picture of the changes that are beginning. Primarily, we should recognize that the audience has turned to become the creator, which comes about as a result of the availability of social networks and this personal content consumption. Data analytics is one of the ways the media is used to serve different audience segments with content they can relate to. The article discusses substantive changes in news content creation patterns that media outlets and newsrooms gradually exhibit, particularly audience-centric approaches, multimedia storytelling, AI, authenticity, and community collaboration. The white paper explores the research and point of view on this matter, and it lays down the thought that converting to this new trend can be a tool for media to remain useful and effective in content production in the digital era.

Increasing the Effectiveness of Prediction in Recommendation Engines Based on Collaborative Filtering

In the era of information abundance, the demand for personalized content recommendations has become paramount. Recommendation engines, particularly those employing collaborative filtering, play a pivotal role in delivering tailored suggestions based on user preferences. As technology evolves, the need to enhance the effectiveness of prediction algorithms within these engines becomes increasingly crucial. This research endeavors to contribute to this evolving landscape by delving into collaborative filtering methodologies, identifying challenges, and proposing novel strategies to elevate the accuracy and relevance of predictions in recommendation systems. Through this exploration, we aim to not only refine existing models but also pave the way for more sophisticated and reliable personalized content recommendations.
 This research aims to enhance prediction accuracy in recommendation engines utilizing collaborative filtering. Through an in-depth exploration of collaborative filtering techniques, we propose innovative approaches to improve the effectiveness of predictions. Our study addresses key challenges in collaborative filtering models, offering insights into refined algorithms and methodologies. By fine-tuning the collaborative filtering process, we anticipate a substantial boost in the overall performance of recommendation engines, ultimately advancing the field of personalized content suggestion. The simulation is performed using Java language and using two datasets Movie Lens 1M and Movie Lens 100K.The proposed model was evaluated using the Mean Absolute Error, Precision, and Recall.
 The proposed model achieved a mean absolute error value ranging between 0.78 and 0.84 using the Movie Lens 100K dataset, and a mean absolute error value ranging between 0.72 and 0.74 using the Movie Lens 1M dataset for different values of the number of user groups. As for precision and recall, the precision of the proposed model ranged between 0.97 and 0.985 using the Movie Lens 100K data set, and a precision value ranging between 0.944 and 0.954 using the Movie Lens 1M data set, also for different values of the number of user groups.
 As for the recall results, the proposed model achieved a recall value ranging between 0.755 and 0.85 using the Movie Lens 100K dataset, and a recall value ranging between 0.72 and 0.75 using the Movie Lens 100K dataset, also for different values of the number of user groups. These results were compared with the PMF, HPF, and NMF algorithms, where the proposed model proved its clear superiority over these algorithms. Using this analysis of the matrix allows us to obtain a good prediction accuracy of users' preferences and to find common groups of people with similar preferences.

Digital Content Creation: An Analysis of the Impact of Recommendation Systems

The success of digital content platforms, such as YouTube, relies on both the creativity of independent content creators and the efficiency of content distribution. By sharing advertising revenue with content creators, these platforms can motivate creators to exert greater effort. Most platforms use recommendation systems to deliver personalized content recommendations to each consumer. As creators’ revenues are contingent on their demand, the demand allocation criteria inherent in the recommendation system can influence their content creation behavior. In this paper, we investigate the influence of a platform’s recommendation system on revenue-sharing plans, content creation, profits, and welfare. Our results show that a platform could benefit by biasing recommendations, that is, recommending content that is not an ideal match to a consumer’s preference, to incentivize creators to produce better-quality content. We refer to this as a biased recommendation strategy. Interestingly, we find that such a biased recommendation strategy may lead to a win-win in which the platform, consumers, and content creators can benefit. Our study also shows that consumers may be worse off when they are more knowledgeable and less dependent on the recommendation system. In addition, the platform, consumers, and creators can benefit when the platform has more accurate information on consumer preferences. This paper was accepted by Raphael Thomadsen, marketing. Supplemental Material: The online appendices are available at https://doi.org/10.1287/mnsc.2022.03655 .

Extracting Course Features and Learner Profiling for Course Recommendation Systems: A Comprehensive Literature Review

As education has evolved towards online learning, the availability of learning materials has expanded and consequently, learners’ behavior in choosing resources has changed. The need to offer personalized learning experiences and content has never been greater. Research has explored methods to personalize learning paths and match learning materials with learners’ profiles. Course recommendation systems have emerged as a solution to help learners select courses that suit their interests and aptitude. A comprehensive review study was required to explore the implementation of course recommender systems, with the specifics of courses and learners as the main focal points. This study provided a framework to explain and categorize data sources for course feature extraction, and described the information sources used in previous research to model learner profiles for course recommendations. This review covered articles published between 2015 and 2022 in the repositories most relevant to education and computer science. It revealed increased attention paid to combining course features from different sources. The creation of multi-dimensional learner profiles using multiple learner characteristics and implementing machine-learning-based recommenders has recently gained momentum. As well, a lack of focus on learners’ micro-behaviors and learning actions to create precise models was noted in the literature. Conclusions about recent course recommendation systems development are also discussed.

Exploring the Feasibility of Using ChatGPT to Create Just-in-Time Adaptive Physical Activity mHealth Intervention Content: Case Study.

Achieving physical activity (PA) guidelines' recommendation of 150 minutes of moderate-to-vigorous PA per week has been shown to reduce the risk of many chronic conditions. Despite the overwhelming evidence in this field, PA levels remain low globally. By creating engaging mobile health (mHealth) interventions through strategies such as just-in-time adaptive interventions (JITAIs) that are tailored to an individual's dynamic state, there is potential to increase PA levels. However, generating personalized content can take a long time due to various versions of content required for the personalization algorithms. ChatGPT presents an incredible opportunity to rapidly produce tailored content; however, there is a lack of studies exploring its feasibility. This study aimed to (1) explore the feasibility of using ChatGPT to create content for a PA JITAI mobile app and (2) describe lessons learned and future recommendations for using ChatGPT in the development of mHealth JITAI content. During phase 1, we used Pathverse, a no-code app builder, and ChatGPT to develop a JITAI app to help parents support their child's PA levels. The intervention was developed based on the Multi-Process Action Control (M-PAC) framework, and the necessary behavior change techniques targeting the M-PAC constructs were implemented in the app design to help parents support their child's PA. The acceptability of using ChatGPT for this purpose was discussed to determine its feasibility. In phase 2, we summarized the lessons we learned during the JITAI content development process using ChatGPT and generated recommendations to inform future similar use cases. In phase 1, by using specific prompts, we efficiently generated content for 13 lessons relating to increasing parental support for their child's PA following the M-PAC framework. It was determined that using ChatGPT for this case study to develop PA content for a JITAI was acceptable. In phase 2, we summarized our recommendations into the following six steps when using ChatGPT to create content for mHealth behavior interventions: (1) determine target behavior, (2) ground the intervention in behavior change theory, (3) design the intervention structure, (4) input intervention structure and behavior change constructs into ChatGPT, (5) revise the ChatGPT response, and (6) customize the response to be used in the intervention. ChatGPT offers a remarkable opportunity for rapid content creation in the context of an mHealth JITAI. Although our case study demonstrated that ChatGPT was acceptable, it is essential to approach its use, along with other language models, with caution. Before delivering content to population groups, expert review is crucial to ensure accuracy and relevancy. Future research and application of these guidelines are imperative as we deepen our understanding of ChatGPT and its interactions with human input.

Influence of Social Cultural Factors on Academic Performance in Public Secondary Schools in Tana River County

The main purpose of the study was scrutinize the socio- cultural aspects on academic performance in public secondary schools in Tana River County. The study employed the Socio-cultural theory as well as System Theory. The study employed descriptive study design. The study was carried out in public secondary schools in Tana River County with a target population of 253 respondents. A census of sample size of 155 participants. The researcher applied primary data. Questionnaires were applied to gather information from principals whereas interview schedule was applied to gather gen from parents. Expert opinion was used to ensure data validity and reliability. The data was analysed descriptively by means and the standard deviation, while multiple regression analysis was used to establish the influence between the variables. The researcher applied SPSS version 25 to examine facts. Regression, ANOVA, correlation as well as model summary was created in addition, facts were presented in tables. The correlation between the independent variables (home chores activities, community activities, parents’ level of edification besides learners’ engrossment in early marriages) besides the dependent variable (academic performance) outcomes showed that all are positive as well as momentous. Particularly, home chores activities is positively as well as significantly correlated (r=.033, p<.05). Likewise, learner’s involvement in community activities (r=.203, p<.05), parents’ level of education (r=.075, p<.05), and learners involvement in early marriages (r=.230, p<.05) are positively as well as ominously interrelated with academic performance in public secondary schools in Tana River. The constant of fortitude infers that the set of independent variables in this research is in lieu of 51.8% of disparities in the academic recital in public secondary schools. The ANOVA outcomes demonstrate that the F_ ratio is 32.048 with a conforming p-value of .000. The projected model linking the social cultural factors to academic performance in public secondary schools is a decent model. Parents’ participation in edification takes various methods comprising the provision of safe as well as steady environment, scholarly imitations, parent to youngster conversation, good replicas of constructive communal as well as scholastic standards and high ambitions connecting to personal contentment, contact with school to pass gen, contribution in school proceedings as well as school work and governance.

Real-time Multi-CNN-based Emotion Recognition System for Evaluating Museum Visitors’ Satisfaction

Conventional studies on the satisfaction of museum visitors focus on collecting information through surveys to provide a one-way service to visitors, and thus it is impossible to obtain feedback on the real-time satisfaction of visitors who are experiencing the museum exhibition program. In addition, museum practitioners lack research on automated ways to evaluate a produced content program's lifecycle and its appropriateness. To overcome these problems, we propose a novel multi-convolutional neural network, called VimoNet, which is able to recognize visitors emotions automatically in real-time based on their facial expressions and body gestures. Furthermore, we design a user preference model of content and a framework to obtain feedback on content improvement for providing personalized digital cultural heritage content to visitors. Specifically, we define seven emotions of visitors and build a dataset of visitor facial expressions and gestures with respect to the emotions. Using the dataset, we proceed with feature fusion of face and gesture images trained on the DenseNet-201 and VGG-16 models for generating a combined emotion recognition model. From the results of the experiment, VimoNet achieved a classification accuracy of 84.10%, providing 7.60% and 14.31% improvement, respectively, over a single face and body gesture-based method of emotion classification performance. It is thus possible to automatically capture the emotions of museum visitors via VimoNet, and we confirm its feasibility through a case study with respect to digital content of cultural heritage.

Effect of Social Media Marketing “Instagram” Towards Purchase Intention : Evidence From Plant-Based Milk Product “Oatside” in Indonesia

In the ever-evolving field of marketing, building and maintaining purchase intent remains a central theme and a significant challenge for marketers. Among various strategies em-ployed to sustain customer brand loyalty, social media marketing has emerged as a recent and effective tool. This study delves into the impact of social media marketing on the pur-chase intent of consumers, an area that is garnering growing interest from both marketing scholars and industry practitioners. The research specifically encompasses customers in Indonesia who follow "Oatside" on Instagram. Oatside is a popular plant-based milk prod-uct, and its Instagram followers form the target demographic for this study. A structured questionnaire was administered to a sample of 130 respondents, serving as the primary data collection method. The collected data were then examined using linear regression analysis conducted via SPSS. The results of the study indicate a positive correlation be-tween purchase intent and several key factors related to the brand's social media pres-ence. These include the brand's provision of entertaining content, facilitation of two-way communication with consumers, presentation of trending topics, and offering of personal-ized content. Each of these elements contributes to enhancing consumer engagement and influencing purchase decisions. As a pioneer in this novel area of marketing, the study not only contributes to the academic understanding of the impact of social media marketing but also suggests practical tactics for industry practitioners. These strategies can be in-strumental in optimizing their social media marketing efforts, thereby enhancing customer engagement and driving purchase intent.

When contexts collapse: How ubiquitous video cameras in the home during COVID-19 pandemic lockdowns transformed family representation

Utilising interviews from a range of caregivers and teachers alongside textual analysis of circulating and non-circulating videos made by children during the COVID-19 quarantine, this article examines the collaborative, experimental models of media making that emerged at this unique time. Children’s media-making practices during quarantine provided a window into and between personal dwelling spaces and private details, disrupting conventions of family self-representation while forcing a confrontation with the capitalist imperative to separate work and private life and for parents to produce, share, and monetize personal media content. This transformation in home mode media is bound up in making visible the incommensurability of home life and office labour, and the insistence of care (for each other) over careful production (of family and professional images).

The comprehensive investigation of the role related to artificial intelligence in education

This systematic literature review extensively explores the prospects of Artificial Intelligence (AI) applications in the realm of learning from 2019 to 2023. It delves into key areas such as intelligent tutoring systems and online learning platforms, while meticulously examining their underlying technologies. The coverage includes methods for integrating AI into student models, as well as algorithms for enhancing adaptive learning and improving the precision of personalized learning content recommendations. The paper also discusses challenges and prospects, including the potential of AI to reshape the teaching profession and privacy concerns. The review underscores the ongoing necessity of striking a balance between human involvement and automation, emphasizing that AI serves as an auxiliary tool rather than a replacement in education. Additionally, the study investigates the integration of AI in online learning, emphasizing the need for user-friendly interfaces and robust data protection policies. Lastly, the research highlights the importance of interdisciplinary collaboration and ethical considerations, envisioning a future where AI and education seamlessly harmonize.