Search Results (33)

Due to technological advancements such as generative artificial intelligence (AI) and large language models, chatbots enable increasingly human-like, real-time conversations through text (e.g., OpenAI’s ChatGPT) and voice (e.g., Amazon’s Alexa). One AI chatbot that is specifically designed to meet the social-supportive needs of youth is Snapchat’s My AI. Given its increasing popularity among adolescents, the present study investigated whether adolescents’ likelihood of using My AI, as well as their positive or negative emotional experiences from interacting with the chatbot, is related to socio-demographic factors (i.e., gender, age, and socioeconomic status (SES)). A cross-sectional study was conducted among 303 adolescents (64.1% girls, 35.9% boys, 1.0% other, 0.7% preferred not to say their gender; M_age = 15.89, SD_age = 1.69). The findings revealed that younger adolescents were more likely to use My AI and experienced more positive emotions from these interactions than older adolescents. No significant relationships were found for gender or SES. These results highlight the potential for age to play a critical role in shaping adolescents’ engagement with AI chatbots on social media and their emotional outcomes from such interactions, underscoring the need to consider developmental factors in AI design and policy. Full article

Social media (SM) use and the burden on healthcare systems have concurrently increased, with the latter resulting in longer wait times and higher costs. As a result, more people seem to use social media to access health information (HI). This study explores how SM is used for accessing HI within an under-resourced community. In this mixed methods study, respondents (N = 256) completed online English and Spanish Qualtrics surveys assessing their use of healthcare services and social media, and its use for HI. We also explored respondents’ experience in understanding and using the HI they found on SM. Qualitative inquiries (N = 7) included focus groups and key informant interviews and expanded on the survey results. Results indicated that most participants used SM for HI. Instagram, Snapchat and TikTok were associated with looking up HI before and after receiving care and for health decision-making and for considering treatments or medication after seeing information about these on social media. To create effective messaging that is accepted, relatable and easy to access for the audiences they seek to reach, healthcare organizations must understand how SM is used as a source of HI. Exploring the associations between SM algorithms, health literacy, access to healthcare and SM use can help improve health communication strategies to be used on SM platforms. Full article

Background: The issue of digital piracy is increasingly prevalent, with its proliferation further fueled by the widespread use of social media outlets such as WhatsApp, Snapchat, Instagram, Pinterest, and X. These platforms have become hotspots for the unauthorized sharing of copyrighted materials without due recognition to the original creators. Current techniques for digital watermarking are inadequate; they frequently choose less-than-ideal locations for embedding watermarks. This often results in a compromise on maintaining critical relationships within the data. Purpose: This research aims to tackle the growing problem of digital piracy, which represents a major risk to rights holders in various sectors, most notably those involved in entertainment. The goal is to devise a robust watermarking approach that effectively safeguards intellectual property rights and guarantees rightful earnings for those who create content. Approach: To address the issues at hand, this study presents an innovative technique for digital video watermarking. Utilizing the 2D-DWT along with the KAZE feature detection algorithm, which incorporates the Accelerated Segment Test with Zero Eigenvalue, scrutinize and pinpoint data points that exhibit circular symmetry. The KAZE algorithm pinpoints a quintet of stable features within the brightness aspect of video frames to act as central embedding sites. This research selects the chief embedding site by identifying the point of greatest intensity on a specific arc segment on a circle’s edge, while three other sites are chosen based on principles of circular symmetry. Following these procedures, the proposed method subjects videos to several robustness tests to simulate potential disturbances. The efficacy of the proposed approach is quantified using established objective metrics that confirm strong correlation and outstanding visual fidelity in watermarked videos. Moreover, statistical validation through t-tests corroborates the effectiveness of the watermarking strategy in maintaining integrity under various types of assaults. This fortifies the team’s confidence in its practical deployment. Full article

This study examined the connections between social media use and behaviors, COVID-19 vaccine conspiracy beliefs, and COVID-19 vaccine uptake in 809 emerging and middle-aged adults. Emerging adults reported more overall social media use, active and passive social media behaviors, and use of most platforms (i.e., Instagram, Snapchat, TikTok, Twitter/X, Reddit, and YouTube), whereas middle-aged adults reported more Facebook use and higher vaccine uptake. COVID-19 vaccine conspiracy beliefs were linked to lower vaccine uptake, with this association unexpectedly stronger among individuals who reported less social media use and fewer active and passive social media behaviors. Active social media behaviors were associated with stronger vaccine conspiracy beliefs, whereas passive social media behaviors and overall use did not show a similar association. Exploratory analyses of platform-specific effects revealed nuanced patterns: TikTok use was associated with stronger vaccine conspiracy beliefs, Instagram use was associated with higher vaccine uptake, and Snapchat use was associated with lower vaccine uptake. Our findings highlight the complex, platform-specific influences of social media use and behaviors on COVID-19 vaccine conspiracy beliefs and vaccine uptake. Future studies are needed to investigate the role of specific social media platforms in spreading, perpetuating, or countering misinformation about the COVID-19 vaccine. Full article

The traditional approach of single-word brand names faces constraints due to trademarks, prompting a shift towards fusing two or more words to craft unique and memorable brands, exemplified by brands such as SalesForce^© or SnapChat^©. Furthermore, brands such as Kodak^©, Xerox^©, Google^©, Häagen-Dazs^©, and Twitter^© have become everyday names although they are not real words, underscoring the importance of brandability in the naming process. However, manual evaluation of the vast number of possible combinations poses challenges. Artificial intelligence (AI), particularly natural language processing (NLP), is emerging as a promising solution to address this complexity. Existing online brand name generators often lack the sophistication to comprehensively analyze meaning, sentiment, and semantics, creating an opportunity for AI-driven models to fill this void. In this context, the present document reviews AI, NLP, and text-to-speech tools that might be useful in innovating the brand name generation and evaluation process. A systematic search on Google Scholar, IEEE Xplore, and ScienceDirect was conducted to identify works that could assist in generating and evaluating brand names. This review explores techniques and datasets used to train AI models as well as strategies for leveraging objective data to validate the brandability of generated names. Emotional and semantic aspects of brand names, which are often overlooked in traditional approaches, are discussed as well. A list with more than 75 pivotal datasets is presented. As a result, this review provides an understanding of the potential applications of AI, NLP, and affective computing in brand name generation and evaluation, offering valuable insights for entrepreneurs and researchers alike. Full article

Internet usage is a salient developmental factor in adolescents’ lives. Although relevant correlates of Internet use have been documented earlier, there is a lack of information on lower socioeconomic status groups. This is important, as these adolescents have increased risk of negative online experiences. The current survey aimed to explore Internet use and parental involvement amongst adolescents from areas of socio-economic disadvantage in 30 urban schools across five European countries. A total of 2594 students participated, of whom 90% were 14–16 years. Virtually all adolescents of socioeconomic disadvantage had Internet access, with 88.5% reporting spending more than two hours per day online, often on apps such as Instagram, Snapchat, and YouTube. Almost one-third of adolescents did not talk with their parents about their Internet use and almost two-thirds indicated that their parents were only a little or not interested in their Internet use. A consistent finding across countries was that girls more often talked with their parents about their Internet use and more often reported that their parents were interested in their Internet use than boys. The results suggest that parents have an important task in explicitly showing interest in their adolescents’ Internet use, with special attention needed for boys. Full article

With the development of image-generating technologies, significant progress has been made in the field of facial manipulation techniques. These techniques allow people to easily modify media information, such as videos and images, by substituting the identity or facial expression of one person with the face of another. This has significantly increased the availability and accessibility of such tools and manipulated content termed ‘deepfakes’. Developing an accurate method for detecting fake images needs time to prevent their misuse and manipulation. This paper examines the capabilities of the Vision Transformer (ViT), i.e., extracting global features to detect deepfake images effectively. After conducting comprehensive experiments, our method demonstrates a high level of effectiveness, achieving a detection accuracy, precision, recall, and F1 rate of 99.5 to 100% for both the original and mixture data set. According to our existing understanding, this study is a research endeavor incorporating real-world applications, specifically examining Snapchat-filtered images. Full article

Background: A substantial portion of the world’s population owns and utilizes computers and mobile devices, contributing to the rapid expansion of digital advertising. Marketers swiftly recognized the communicative benefits of social media platforms like Facebook, YouTube, Twitter, Instagram, Snapchat, Pinterest, and LinkedIn. Considering the importance of social media platforms and digital modes of marketing, it is considered especially significant for small firms to integrate these platforms into their business strategies in order to improve performance. Methods: Based on this aim, this study collected data from 363 owners/managers of SMEs in Pakistan. Structural equation modeling is used to check the hypothesized model of the study. Results: The results show that compatibility, owner/manager support, employee IT skills, financial cost, government policies, and social influence significantly affect adoption of digital marketing by SMEs in Pakistan. Conclusions: Furthermore, digital marketing also positively affects SME performance. This paper discusses the study’s findings as well as managerial and academic implications, including its limitations and future research avenues. Full article

Many physicians consider social media a good tool for building their brands and attracting patients. However, limited data exist on patients’ perceptions of the value of social media in ophthalmology. Therefore, our objective was to examine how social media influences patients when choosing an ophthalmologist among social media users, and people’s behaviors toward ophthalmologists’ social media accounts. This was a cross-sectional study including 1086 participants. Males represented 77.3% of the sample. The majority of the participants (71.3%) were aged between 25 and 54 years. Regarding social media sites frequently checked, Twitter ranked first (75.3%), followed by Snapchat (52.8%) and YouTube (48.7%). The majority (92.3%) used social media sites at all times of the day. Concerning the importance of ophthalmologists’ social media sites, around 36.3% considered it either very or extremely important. As regards the important factors about an ophthalmologist’s social media site from participants’ perspectives, medical information written by the ophthalmologist (45.5%) and recommendations by friends (45.4%) were the most common reasons. Around 21% of females, compared to 16.8% of males, perceived the ophthalmologists’ social media sites as extremely important, p = 0.041. A quarter of participants aged between 18 and 24 years, compared to only 5.5% of those aged 65 and above, perceived the ophthalmologists’ social media sites as extremely important, p = 0.018. In conclusion, a considerable proportion of the people who used social media described ophthalmologists’ social media sites as very/extremely important in their choice of an ophthalmologist. Full article

Public health researchers are increasingly interested in the potential relationships between social media (SM) use, well-being, and health behavior among adolescents. However, most research has assessed daily SM time via self-report survey questions, despite a lack of clarity around the accuracy of such reports given the current tendency of youth to access SM on multiple electronic devices and cycle between multiple SM platforms on a daily basis (i.e., platform swinging). The current study investigates the potential for systematic reporting biases to skew findings. Three hundred and twenty incoming college students downloaded software on their computers, tablets, and smartphones to track their active use of Facebook, Twitter, Instagram, and Snapchat over a 2-week surveillance period and then self-reported their average daily minutes on each platform immediately after. Larger proportions of students over-estimated than under-estimated their use, with the largest overestimations found on the most heavily used platforms. Females logged significantly more SM time and were less accurate in reporting than were males and, independently, the likelihood of substantial inaccuracies in reporting total SM time and time on most individual platforms increased with each additional SM platform participants reported using. Findings demonstrate that self-reported estimates of SM time among adolescents in the age of SM platform swinging are prone to substantial error and may lead to biased conclusions about relationships between variables. Alternative measurement approaches are suggested to improve the validity of future research in this area. Full article

Stock price prediction is a significant research field due to its importance in terms of benefits for individuals, corporations, and governments. This research explores the application of the new approach to predict the adjusted closing price of a specific corporation. A new set of features is used to enhance the possibility of giving more accurate results with fewer losses by creating a six-feature set (that includes High, Low, Volume, Open, HiLo, OpSe), rather than the traditional four-feature set (High, Low, Volume, Open). The study also investigates the effect of data size by using datasets (Apple, ExxonMobil, Tesla, Snapchat) of different sizes to boost open innovation dynamics. The effect of the business sector in terms of the loss result is also considered. Finally, the study included six deep learning models, MLP, GRU, LSTM, Bi-LSTM, CNN, and CNN-LSTM, to predict the adjusted closing price of the stocks. The six variables used (High, Low, Open, Volume, HiLo, and OpSe) are evaluated according to the model’s outcome, showing fewer losses than the original approach, which utilizes the original feature set. The results show that LSTM-based models improved using the new approach, even though all models showed a comparative result wherein no model showed better results or continuously outperformed other models. Finally, the added new features positively affected the prediction models’ performance. Full article

This study investigated the associations between obesity and unhealthy food/drink intake with both the frequency of social media platform usage and food/drink marketing exposure on social media. Data were obtained from 316 university students aged 18–29 years at two universities in Jeddah, Saudi Arabia. These participants completed online questionnaires with sections on demographics, anthropometric measurements, social media platform usage, food marketing exposure via social media, and unhealthy food consumption. All of the participants, 20.3% and 13.6% were overweight and obese, respectively. Snapchat was the most popular application (85.8%), followed by Instagram (75%), YouTube (61%), Twitter (51%), and TikTok (50%). The obese participants were more likely to purchase foods/drinks after watching relevant social media advertisements than their non-obese counterparts (p < 0.04). Moreover, those who purchased foods/drinks more frequently after watching such advertisements consumed higher amounts of potato chips (p < 0.01) and fast foods (p < 0.03). Finally, those who used Snapchat, TikTok, and Instagram tended to have higher consumption rates for potato chips (p < 0.02), fast foods (p < 0.01), sweets (p < 0.02), and sugary drinks (p < 0.04). Public health policymakers in Saudi Arabia should consider regulating unhealthy food and drink advertisements on social media platforms, especially those targeted at younger generations. Full article

The use of social networking sites for socializing, having fun, solving academic tasks or even getting counselling for health-related problems is now inevitable. Methods: A total of 427 medical students, who are users of social media sites, were included in the research. Data about socio-demographic, anthropometric, and self-rated items regarding satisfaction with physical and mental health were collected. Three psychological tools were also used to measure self-esteem (Rosenberg Self-Esteem Scale), body-esteem (Body Esteem Scale for Adolescents and Adults) and loneliness (UCLA Loneliness Scale). Collected data were analyzed using SPSS version 23. Results: Students use these networks for socialization (49.0%), entertainment (31.1%) and academic tasks (19.9%), spending 3.38 ± 0.80 h per day on SNSs. Less than half of them (47.5%) compared themselves to other SNS profiles. The use of Snapchat was found to be strongly positively correlated with self-esteem, and weight status was negatively correlated with the use of TikTok. More than three-quarters declared that they exercised to lose weight or to prevent weight gain. Participants were found to have a high level of body esteem. Almost half of the students proved to have a moderate to a high level of loneliness. Age and gender were found to be important: the younger the user, the higher the scores for loneliness and feeling depressed, and the greater the number of hours on SNSs. The total score for self-esteem was significantly higher in men than in women, and male students appreciated themselves as being in a better state of mental health than women. Conclusions: The results prove a relationship between the use of SNSs and the presence of loneliness, self-esteem and body-esteem, with gender differences. However, the use of SNSs should not be neglected in clinical settings, and are a good means of reaching patients and providing medical and psychological intervention. Full article

Emergency events arise when a serious, unexpected, and often dangerous threat affects normal life. Hence, knowing what is occurring during and after emergency events is critical to mitigate the effect of the incident on humans’ life, on the environment and our infrastructures, as well as the inherent financial consequences. Social network utilization in emergency event detection models can play an important role as information is shared and users’ status is updated once an emergency event occurs. Besides, big data proved its significance as a tool to assist and alleviate emergency events by processing an enormous amount of data over a short time interval. This paper shows that it is necessary to have an appropriate emergency event detection ensemble model (EEDEM) to respond quickly once such unfortunate events occur. Furthermore, it integrates Snapchat maps to propose a novel method to pinpoint the exact location of an emergency event. Moreover, merging social networks and big data can accelerate the emergency event detection system: social network data, such as those from Twitter and Snapchat, allow us to manage, monitor, analyze and detect emergency events. The main objective of this paper is to propose a novel and efficient big data-based EEDEM to pinpoint the exact location of emergency events by employing the collected data from social networks, such as “Twitter” and “Snapchat”, while integrating big data (BD) and machine learning (ML). Furthermore, this paper evaluates the performance of five ML base models and the proposed ensemble approach to detect emergency events. Results show that the proposed ensemble approach achieved a very high accuracy of 99.87% which outperform the other base models. Moreover, the proposed base models yields a high level of accuracy: 99.72%, 99.70% for LSTM and decision tree, respectively, with an acceptable training time. Full article

Research efforts in the field of sentiment analysis have exponentially increased in the last few years due to its applicability in areas such as online product purchasing, marketing, and reputation management. Social media and online shopping sites have become a rich source of user-generated data. Manufacturing, sales, and marketing organizations are progressively turning their eyes to this source to get worldwide feedback on their activities and products. Millions of sentences in Urdu and Roman Urdu are posted daily on social sites, such as Facebook, Instagram, Snapchat, and Twitter. Disregarding people’s opinions in Urdu and Roman Urdu and considering only resource-rich English language leads to the vital loss of this vast amount of data. Our research focused on collecting research papers related to Urdu and Roman Urdu language and analyzing them in terms of preprocessing, feature extraction, and classification techniques. This paper contains a comprehensive study of research conducted on Roman Urdu and Urdu text for a product review. This study is divided into categories, such as collection of relevant corpora, data preprocessing, feature extraction, classification platforms and approaches, limitations, and future work. The comparison was made based on evaluating different research factors, such as corpus, lexicon, and opinions. Each reviewed paper was evaluated according to some provided benchmarks and categorized accordingly. Based on results obtained and the comparisons made, we suggested some helpful steps in a future study. Full article