Personal Content Research Articles

Analysis and Comparison of Social Media Applications Using Forensic Software on Mobile Devices

With the integration of mobile systems into daily life, social media applications used especially on Android and iOS platforms contain a significant amount of sensitive information. Social media applications on mobile systems have huge personal and sensitive content. Therefore, it is important to design effective techniques for forensic analysis of social media applications and to detect personal data. In this research, three different paid mobile forensic software and 4 different brands and models of smartphones with different operating systems were used and analyzed. The study shows that private messages, e-mails, time information, shared data, location and time information, and other personal data can be obtained by a forensic expert who performs an examination, and it is seen that one software can access the deleted data, but another software cannot access it. In proportion to the technology used in today’s world, mobile forensics incidents are increasing day by day, and a competitive environment is created among the software used to illuminate these incidents. With this competition, software companies dealing with forensic informatics are trying to obtain different data to illuminate forensic events that may occur due to the active use of social media accounts with the developing technology, and software that does not meet the needs in the face of this situation remains in the background. The criminal elements in the investigation areas of mobile forensics differ daily, and the scope of crimes in the virtual environment is expanding with the developing technology. Therefore, mobile forensic analysis applications should be successful in social media applications other than standard data.

Revolutionizing Retail: An Empirical Study on the Impact of Generative AI in Omnichannel Strategies

This article examines the transformative impact of Generative AI (Gen AI) on omnichannel retail, exploring its role in enhancing customer experiences and streamlining operations across digital and physical platforms. Through a comprehensive analysis of current literature, industry case studies, and empirical data, the study investigates six key applications of Gen AI in retail: personalization and dynamic content creation, virtual shopping assistants, inventory and fulfillment optimization, marketing content generation, customer insights and sentiment analysis, and immersive AR/VR experiences. The findings reveal that Gen AI significantly improves personalization, operational efficiency, and cross-channel integration in retail. However, the research also identifies challenges in implementation, including data privacy concerns and the need for seamless technology integration. This article contributes to the growing body of knowledge on AI in retail and provides practical insights for retailers seeking to leverage Gen AI in their omnichannel strategies. The paper concludes by discussing future research directions and the potential long-term implications of Gen AI on consumer behavior and global retail practices.

Optimization Analysis of Brand Communication Strategies for Champion in the China Region

Champion is established in 1919; it is known for its high-quality athletic wear and also its durable and innovative sportswear. This thesis will take Champion as the research object to find how to make brands become more competitive in today’s market by researching its history, strategy and its status. In the 1930s Champion has introduced the iconic hooded sweatshirt. Champion also invented a technology called reverse weave which prevent clothes from shrinking. The brand has a huge fanbase among the young generation where it keeps collaborate with designers and influencers. This has helped champion expand its product range in both traditional athletic wear and the new trending fashion. Because Champions new innovation and design, it leads to more and more people trust and select this brand. Champion should continue to deepen its exploration and practice in the aspects of youth and digitalization. Through more accurate market positioning, personalized communication content and innovative interactive experiences, Champion is expected to further close the distance with young consumers and inspire their brand loyalty and desire to buy. At the same time, Champion should also focus on issues such as sustainability and social responsibility, and actively communicate positive energy and brand values.

Hungry for nutrition information: Undergraduate students' engagement with nutrition social media nutrition content.

Background: Social media is a fast-changing, normalized way through which youth access health and nutrition information. Yet, social media content that lacks expertise or altruistic motives can negatively impact this vulnerable population through mis- or disinformation. Aim: The aims of this study were to 1) assess how adolescents and young adults engage with nutrition information across social media platforms and account types, and 2) describe their preferences for nutrition social media content. Methods: We conducted an exploratory mixed methods study with 57 undergraduate students enrolled in an introductory public health nutrition course. First, we calculated descriptive statistics on close-ended survey data on social media use and sociodemographic variables. Then, we performed thematic analysis on open-ended survey data regarding social media use, preferences, and experiences. Results: The median age of participants was 20 years old; 70% of participants identified as female and 11% identified as male. We found that TikTok and Instagram were highly preferred by participants for weight loss, wellness, diet promotion, and sports and fitness topics. In general, participants reported wanting personalized content that conveys positive nutrition messages that are supported by credible expertise. Conclusion: These findings suggest that the nutrition community can engage adolescents and young adults by presenting information as image- and video-based content on social media. Social media content related to weight loss, wellness, diet promotion, and sports and fitness topics should establish credibility using visible markers and embrace body positive messaging for maximum uptake.

What do they look for and what do they find? A coproduced qualitative study on young people's experiences of searching for mental health information online.

Many young people (YP) struggle with their mental health and look online for help. To capitalise on their digital presence, we need to better understand how and where they seek information online and what they think of what they find. We recruited 24 YP (aged 13-18 years). Online interviews were co-conducted by research team members and trained young researchers. We presented a persona with depression symptoms and asked about potential sources of information/support they might seek. They were also asked to think aloud while searching online and reviewing mental health resources (NHS, Young Minds). We used reflexive thematic analysis. Analysis generated four themes: (1) the online help-seeking process, showcasing where YP look for information and why; (2) the mismatch between the information YP expected to find and the reality; (3) the strategies YP employed to determine a source's trust and credibility and (4) individual differences that can influence help-seeking. Participants initiated their online search by Googling symptoms. They trusted NHS websites for basic medical information, while charities provided detailed content. Despite scepticism about content, social media offered validation. Online resources should prioritise visual appeal, user-friendliness, age-appropriate and personalised content and peer insights. Codesign is imperative to ensure high-quality, impactful research.

Leveraging ICT and Generative AI in Higher Education for Sustainable Development: The Case of a Lebanese Private University

This study investigates the interaction of information and communication technology (ICT) and higher education in driving sustainable development, with a special emphasis on the rising significance of generative AI tools such as ChatGPT. This quantitative study, conducted in a Lebanese private university, looks into the impact of ICT, specifically generative AI, on promoting sustainability in higher education. A questionnaire was distributed to university instructors to determine how these technologies contribute to sustainable practices in academic environments. The results, which were evaluated using descriptive statistics and correlation analysis, show that ICT tools, professional instructor development, and the upkeep of educational infrastructure all contribute considerably to Lebanon’s sustainable development. Furthermore, integrating generative AI into educational processes has the potential to improve personalized learning, resource efficiency, and content production, all of which contribute to sustainability goals. The study concludes by exploring the findings’ broader implications, acknowledging their shortcomings, and suggesting future research possibilities.

Social media profiling: The influence of personal and professional social media content on hiring ratings

AbstractThis study integrates identity and situational strength theories into a realistic accuracy model framework to develop and test hypotheses regarding how hiring managers perceive social media (SM) information, and how those perceptions translate into ratings. In Study 1, 310 h professionals rated which SM content they perceive as most useful in assessing applicants on various work‐related constructs. The provided a foundation for Study 2, where 151 h professionals participated in an experimental hiring simulation to understand whether these perceptions of SM information translate into rating behavior. Results indicate that manipulating SM content influenced HR professionals’ ratings of applicants. Non‐job‐related SM content influenced ratings of work‐related constructs more strongly than job‐related content. Professional SM was rated more favorably than personal SM.

Artificial Intelligence in Digital Marketing: A Bibliometric Analysis and Future Research Directions

Digital marketing has changed the way companies do business due to the widespread use of artificial intelligence (AI). AI has changed the way businesses talk to their customers by making it easier to create personalised content. Due to the industry’s growing research and development, it can be hard to keep up with the latest trends and technologies. A detailed literature review on AI in digital marketing looks at the research that has been done in this area. Additionally, the study stresses how important it is to investigate the moral effects of using AI in marketing. The focus of the study is on how AI affects privacy, fairness and accountability. The study says that marketers should focus on encouraging openness and giving users control over how AI technology is used. The study suggests that more research be done on how AI affects job loss and the job market. Bibliometric analysis for the last 10 years is done using the global research database. This research makes the case for doing research that uses ideas from different fields, such as marketing, computer science and social science. Researchers and industry practitioners who are interested in how AI and digital marketing work together can learn from this study.

Latarjet and Bankart Repairs: Instagram-Based Perception Analysis Shows Comparable Patient Experiences

PurposeTo conduct a comprehensive analysis of public Instagram posts pertaining to Latarjet procedure and Bankart repair, with the aim of elucidating patients’ perspective on the perioperative process, satisfaction and expectations. Methods: Descriptive social media-based investigation using relevant hashtags associated with surgical treatment of anterior shoulder instability, covering the period from January 2023 until January 2024. Posts were categorized by perspective, timing, content, tone and satisfaction. Results: 2,395 posts were retrieved, with 526 posts concerning surgical treatment of shoulder instability. 201 (37.9%) posts were authored by patients and 224 (42.3%) posts portrayed personal content and patient experience. The majority were neutral (308; 58.1%) or positive in tone (203; 38.3%). Rehabilitation was mentioned frequently by patients undergoing both procedures (49.2% of Bankart posts and 45.6% of Latarjet). Comparative analysis revealed no statistical differences regarding immobilization (P=0.229), rehabilitation (p=0.226), return to sport (p=0.464), tone (p=0.236) or expression of satisfaction (p=0.826) between both procedures. However, patients treated with Latarjet mentioned surgical site more frequently (p=0.011). There were no differences in mentions of complications (p=0.143). Complications were mentioned in 18 (9.6%) Bankart posts, most commonly recurrence, and in 10 (5.3%) of the Latarjet posts where the most mentioned was pain. Conclusion: Social media posts reflect mostly a neutral or positive patient experience in the surgical treatment of anterior instability. Bankart and Latarjet posts exhibited no differences regarding satisfaction, tone, return to work or activities of daily living. Mention of complications is rare. The most frequent for Bankart repair was recurrence, while for Latarjet posts was postoperative pain. Clinical relevanceThis analysis explores the relevance of social media as an instrument to gain insight into patient perception surgical intervention for anterior shoulder instability.

Study on the Role and Influence of AI in Personalized Learning for Second Language Acquisition

The combination of AI and second language acquisition can help to provide personalized teaching content and adjust learning more dynamically so that students can improve their learning efficiency and learning effect. Therefore, the use of AI in SLA has received widespread attention, but its accuracy and usability are still insufficient. This paper analyzes the particularity of SLA teaching and the application of AI in SLA, especially its potential to meet the unique needs of learners, optimize learning paths, and improve teaching efficiency. This paper concludes that the combination of teacher and AI is more ideal than the use of AI alone. Based on this, this paper proposes a new pedagogy that fully implements teacher-AI collaborative teaching in SLA. These improvements will promote a more effective language learning environment while strengthening the role of technology in supporting education, and these strategies will drive SLA teaching practices in a more personalized and technology-driven direction.

Creative City Brand Building on Social Media Platforms

With the advent of the digital age, social media has become an important tool to shape the image of a city's brand. This paper aims to explore how to use social media platforms to build creative city brands, so as to enhance the attractiveness and competitiveness of cities. Through the study of many successful cases at home and abroad, combined with the theory of social media communication and the theory of urban brand image, this paper analyzes the mechanism of social media in urban brand promotion. The research adopted a combination of quantitative and qualitative methods, including online questionnaire survey, social media data analysis and in-depth interviews, to collect and analyze relevant data. The results show that social media can not only increase the visibility of cities, but also promote cultural exchanges and economic cooperation. In addition, the study found that personalized content creation, interactive user engagement, and sustained brand storytelling are key factors for successful city branding on social media. Finally, this paper puts forward suggestions on social media marketing strategies for different types of cities, and points out that future research should pay attention to the impact of changes in social media platform algorithms on the effectiveness of urban brand communication. This study provides a practical reference for city managers to promote city image more effectively in the new media environment.

AI-Powered Content Marketing: Leveraging Algorithms for Creative Content Creation and Distribution

This research investigates the growing influence of Artificial Intelligence (AI) in content marketing, specifically focusing on its role in enhancing both the creative and distribution aspects of marketing strategies. As businesses increasingly adopt AI technologies, tools such as GPT-4, Jasper, and Copy.ai are reshaping content creation by automating time-consuming tasks and delivering personalized content to targeted audiences. This study examines how AI is revolutionizing marketing by offering data-driven insights, enabling real-time feedback, and optimizing distribution methods to improve engagement and conversion rates. The paper also addresses key ethical concerns surrounding AI in marketing, such as the potential impact on creativity, privacy, and the need for human oversight to maintain authenticity in brand messaging. By exploring future trends, including the integration of augmented reality (AR) and advancements in predictive analytics, this research highlights how AI is set to drive the next wave of innovation in content marketing.

Investigating the influence of social media influencer credibility on beauty product purchase behaviors: a case study from Vietnam

The growing dominance of social media as a marketing platform underscores the importance of understanding how influencer credibility affects consumer behavior. Social media influencers, through effective personal branding and content creation, form strong connections with their followers, significantly impacting their audience’s perceptions, attitudes, and purchasing decisions. This study explores the effects of social media influencers’ source credibility, comprising trustworthiness, expertise, and attractiveness, on consumers’ purchase intentions and behaviors regarding beauty products in Vietnam. A quantitative approach was utilized, with data collected from 269 Vietnamese consumers through a structured questionnaire and analyzed using partial least squares structural equation modeling. The results showed that all three components of source credibility, including trustworthiness, expertise, and attractiveness, significantly and positively influenced consumers’ purchase intentions for beauty products. Among these factors, expertise had the strongest effect on purchase intention (β = 0.289, p < 0.001), followed by trustworthiness (β = 0.216, p = 0.001), and attractiveness had the weakest influence on purchase intention (β = 0.195, p < 0.001). Moreover, purchase intention was found to have a significant positive impact on purchase behavior (β = 0.545, p = 0.004). These findings provide valuable insights for marketing managers and companies seeking to optimize their influencer partnerships by selecting influencers whose credibility aligns with their brand values and leveraging their expertise to build consumer trust, drive engagement, and influence purchase decisions. AcknowledgementThe researchers express sincere gratitude to all the participants who participate in this research study.

Innovative Content Production in Marketing Communication Through AI

The integration of artificial intelligence (AI) into marketing communications has changed the way businesses communicate with their customers. The present paper explores the potential of AI-driven systems to automate content creation, highlighting the role of AI in increasing customer engagement and strategic decision making. AI offers significant benefits by leveraging customer data to predict their behaviour, optimize marketing strategies, and personalize content. By systematically creating and evaluating content, AI enables marketers to customize messages to specific audiences, increasing the effectiveness of campaigns. The research employs a mixed methods approach that combines qualitative and quantitative analyses to assess the potential use of AI in content marketing. The qualitative aspect involves a thorough review of existing literature, identifying key trends and relationships in the use of AI for marketing communications. On the other hand, the quantitative aspect of the study involves a process of experimental validation using Python and APIs such as OpenAI and Graph APIs to automate content creation and measure its effectiveness. The findings confirm that AI can be utilized to personalize content using the input data in the form of brand-voice, target group, and the evaluation of current communication. The iterative process allows for continuous improvement of quality and relevance of content, which is important in the dynamic environment of marketing communications. The study highlights the importance of accurately defining the target audience and brand-voice, as the effectiveness of AI depends heavily on the accuracy of the input data. However, the study also acknowledges limitations such as the potential inconsistency of the content generated through AI and the challenges associated with maintaining a coherent brand-voice. Future research should focus on developing advanced machine learning methods to better understand context and improve the alignment of content with brand values and customer expectations. In conclusion, the integration of AI into marketing communications offers a powerful tool for businesses to optimize content creation, increase customer engagement, and achieve strategic goals in an increasingly competitive digital environment. AI's ability to create, evaluate and refine content in real-time provides a significant advantage and ensures that marketing communications remain adaptive and effective.

Social media stethoscope: unraveling how doctors' social media behavior affects patient adherence and treatment outcome.

The exposure of the content posted by doctors on social media has the potential to influence how patients perceive and judge doctors. It is necessary to further investigate whether and how the content posted by doctors affects patients' health behaviors and outcomes, as well as to identify the factors that may influence this mechanism. Multi-respondent survey data was collected from 35 doctors and 322 patients in China, and structural equation modeling (SEM) was used to test the hypothesis model. The findings revealed that doctors posting professional knowledge content on social media positively impacted patient adherence and treatment effectiveness. Conversely, doctors sharing personal life-related content on social media were associated with lower patient adherence and poorer treatment outcome. Moreover, doctor gender and doctor humor moderate the relationship between social media behavior of doctors and patient adherence. Doctors sharing professional knowledge on social media not only fosters trust in physicians but also closely correlates with patient adherence and treatment effectiveness.

Reshaping curriculum adaptation in the age of artificial intelligence: Mapping teachers' AI‐driven curriculum adaptation patterns

AbstractA national curriculum cannot be uniformly applied in all classrooms. Educators frequently adapt the official curriculum to suit their particular circumstances. In exploring the interplay between artificial intelligence (AI) technologies and curriculum adaptation in education, this study bridges a significant gap in the literature by exploring how AI tools influence teachers' strategies for adapting curricula. Employing an explanatory sequential design, the research analyses both qualitative and quantitative data from 440 teachers, using the Curriculum Adaptation Patterns Scale and focus group semi‐structured interviews. Results indicate a balanced approach among teachers towards extending and revising the curriculum, with less emphasis on omission. Notably, curriculum adaptation practices evolve positively with increased professional experience, differ across disciplines, but remain constant across school levels and educational levels. Qualitatively, teachers reported positive experiences using AI, particularly ChatGPT, to make their lessons better fit students' needs. They've used it to omit parts that aren't needed, add more relevant and personalised content, and revise or replace the content. The findings highlight AI's transformative potential in curriculum adaptation, making education more engaging, relevant and personalised. The study contributes to understanding how AI can support effective curriculum implementation and enhance learning experiences in the digital age.

Artificial-Intelligence Cloud-Based Platform to Support Shared Decision-Making in the Locoregional Treatment of Breast Cancer: Protocol for a Multidimensional Evaluation Embedded in the CINDERELLA Clinical Trial.

Shared decision-making (SDM) plays a crucial role in breast cancer care by empowering patients and reducing decision regret. Patient decision aids (PtDAs) are valuable tools for facilitating SDM, now available in digital and artificial intelligence (AI)-powered formats to offer increasingly personalized contents. The ongoing CINDERELLA clinical trial (ClinicalTrials.gov: NCT05196269) evaluates an innovative AI cloud-based approach using a web platform and a mobile application (CINDERELLA APProach) versus the conventional approach to support SDM in breast cancer patients undergoing locoregional treatment. This protocol outlines a trial-based multidimensional evaluation, encompassing economic, financial, implementability, and environmental considerations associated with the CINDERELLA APProach. A within-trial cost-consequence and cost-utility analysis from a societal perspective will be performed using patient-level data on outcomes and resource use. The latter will be valued in monetary terms using country-specific unit costs or patient valuations. A budget impact analysis will be performed over 1 and 5 years from the budget holder perspectives. The CINDERELLA APProach implementability will be assessed through an evaluation of its usability, acceptability, organizational impact, and overall feasibility. The environmental impact will be quantitatively assessed across several dimensions, such as quantity, appropriateness, and emissions, supplemented by qualitative insights. Overall, data for the evaluation will be gathered from patient questionnaires, interviews with patients and managers, focus groups with healthcare professionals, and app electronic data. A thorough understanding of the broad consequences of the CINDERELLA APProach may foster its successful translation into real-world settings, hopefully benefiting breast cancer patients and clinical practice.

Human Computer Interaction Strategies for Effective Digital Learning Experiences: From Classroom to Screen

The rapid advancement of technology has transformed traditional classroom settings into complex digital learning environments, emphasizing the need for effective Human-Computer Interaction (HCI) strategies to enhance online learning experiences. This research explores the intersection of HCI and digital learning, focusing on how HCI strategies can improve user engagement, comprehension, and learning outcomes. Through a review of existing literature, this study identifies common challenges faced by learners and educators, such as usability issues, technical difficulties, and inadequate localization. It also highlights effective HCI strategies, including user- centered design, interactive multimedia features, and feedback mechanisms, which are crucial for creating engaging and personalized learning experiences. Findings indicate that personalized learning systems and interactive content significantly enhance performance and achievement by addressing individual learning needs and improving content retention. Best practices derived from user feedback suggest adopting a user-centered design approach, integrating interactive elements, focusing on usability, and continuously updating learning tools. This research underscores the importance of aligning HCI principles with educational technology to foster a more effective and inclusive digital learning environment. Keywords - Human-Computer Interaction (HCI), Digital Learning Environments, User Experience (UX),Interactive Design, Personalized Learning, Educational Technology.

A systematic review of research on emotional artificial intelligence in English language education

In learning English as a foreign language (EFL), students often experience foreign language anxiety. Artificial intelligence (AI) applications that provide emotional support and/or create emotional impacts on student learning, so-called emotional AI applications, have received increased attention. However, there is a lack of a systematic review of studies on emotional AI in EFL education. This paper presents a systematic review of research in this field. The results reveal five affordances of emotional AI in EFL education, namely (1) enabling human-like conversations, (2) providing personalized real-time feedback or instructions, (3) translating images into English text, (4) generating personalized learning content and tasks, and (5) recognizing and analyzing emotions. The first three affordances are more frequently used and have shown promising effects on improving students’ behavioral, cognitive, and affective learning outcomes. Moreover, the findings reveal that emotional support is often integrated with cognitive support; providing emotional support alone may not be enough to support student learning. Meanwhile, providing cognitive support alone can enhance both affective and cognitive learning outcomes. Finally, attention should be paid to the factors that might influence the adoption and effects of emotional AI in EFL education.

ChatGPT and Tourist Decision‐Making: An Accessibility–Diagnosticity Theory Perspective

ABSTRACTThis paper investigates the role of ChatGPT in informing tourist decision‐making across different destination contexts, focusing particularly on the accessibility and diagnosticity of its recommendations. Specifically, we inform our analysis with the tenets of the Accessibility–Diagnosticity Theory (ADT), to draw insights into ChatGPT's capabilities to produce contextually relevant and personalised travel content. Our findings reveal a sophisticated and multi‐dimensional advisory approach characterised by three themes: ‘Tailored Engagement and Accessibility’, ‘Diagnosticity of Information’ and ‘Contextual Variation in Criteria Prioritisation.’ These themes and their intersections highlight ChatGPT's potential to improve tourist decision‐making by offering comprehensive and user‐centric guidance. Based on these findings, we develop a model of ChatGPT's advisory dynamics in tourism decision‐making, which illustrates how ChatGPT integrates personalised insights, diagnostic relevance and contextual adaptation, offering a blueprint for leveraging artificial intelligence in enhancing the travel experience. We conclude with a discussion of the theoretical and practical contributions of the study.