Social Media Recommendation Research Articles

User engagement on global social networks: Examining the roles of perceived brand globalness, identification and global identity

Building on the global branding literature, brand relationship theory and social identity theory, this study investigates the relationship between perceived brand globalness (PBG) and user engagement (active/passive) on global social networks (GSN). Additionally, the study investigates the mediating effects of two distinct forms of user identification (i.e., user identification with the GSN brand and user identification with the GSN community) as well as the moderating effects of user global identity on the relationship between PBG and user engagement with such brands. Covariance-based structural equation modeling was used to analyse data collected from users of a GSN (i.e., Facebook) in the United Kingdom (UK) and India. The results indicate that PBG significantly influences both active and passive user engagement. This relationship is mediated by users' identification with a GSN brand and community. Additionally, the findings indicate that the associations between PBG and user engagement (active/passive) on GSN vary as a function of users' global identity. The results also demonstrate some country-specific variations in key relationships. Finally, the study offers useful recommendations for social media managers to rethink and redesign their user engagement strategies, keeping in mind global cultural diversity.

Adolescents' Algorithmic Resistance to Short Video APP's Recommendation: The Dual Mediating Role of Resistance Willingness and Resistance Intention.

Adolescents have gradually become a vital group of interacting with social media recommendation algorithms. Although numerous studies have been conducted to investigate negative reactions (both psychological and behavioral reactance) that the dark side of recommendation algorithms brings to social media users, little is known about the resistance intention and behavior based on their agency in the daily process of encountering algorithms. Focusing on the concept of algorithm resistance, this study used a two-path model (distinguishing resistance willingness and resistance intention) to investigate the algorithmic resistance of rural Chinese adolescents (N = 905) in their daily use of short video apps. The findings revealed that the perceived threat to freedom, algorithmic literacy, and peer influence were positively associated with the resistance willingness and intention; while the independent psychology on algorithmic recommendations significantly weakened resistance willingness and intention. Furthermore, this study verified the mediating role of resistance willingness and intention between the above independent variables and resistance behavior. Additionally, the positive impact of resistance willingness on resistance intention was confirmed. In conclusion, this study offers a comprehensive approach to further understanding adolescents’ algorithmic resistance awareness and behavior by combining psychological factors, personal competency, and interpersonal influences, as well as two types of resistance reactions (rational and irrational).

The Influence of Social Media on the Treatment of Acne in Saudi Arabia.

ObjectivesThe purpose of this study was to examine the effects of social media on acne treatment among the Saudi Arabian population.MethodsThis was a cross-sectional survey-based study conducted from January 2021 to August 2021. A self-administered survey was distributed through social media to different regions of Saudi Arabia. The survey obtained participants’ sociodemographic information and details on whether people used social media for advice on acne treatment. It also asked whether they noticed any change in their acne based on social media recommendations.ResultsOf the 5,539 respondents, 4,227 experienced acne, of which 1,793 were influenced by social media. Most respondents were women. The majority of social media users were between 18 and 25 years old and chose social media as their first approach for acne advice. The most commonly used platform was Instagram (34%). The most frequent social media recommendation chosen was to increase water intake. Many noticed a slight change in their acne (64%), and 14.9% had side effects. There was a significant association between the use of social media for advice and gender (p-value < 0.001), education level (p-value = 0.002), and severity of acne (p-value < 0.001).ConclusionSocial media has an influence on acne treatment, with many advice not recommended by established guidelines. These findings imply that dermatologists should pinpoint inaccuracies resulting from advice found on social media.

Nudging Social Media toward Accuracy.

A meaningful portion of online misinformation sharing is likely attributable to Internet users failing to consider accuracy when deciding what to share. As a result, simply redirecting attention to the concept of accuracy can increase sharing discernment. Here we discuss the importance of accuracy and describe a limited-attention utility model that is based on a theory about inattention to accuracy on social media. We review research that shows how a simple nudge or prompt that shifts attention to accuracy increases the quality of news that people share (typically by decreasing the sharing of false content), and then discuss outstanding questions relating to accuracy nudges, including the need for more work relating to persistence and habituation as well as the dearth of cross-cultural research on these topics. We also make several recommendations for policy-makers and social media companies for how to implement accuracy nudges.

A Stanford Conference on Social Media, Ethics, and COVID-19 Misinformation (INFODEMIC): Qualitative Thematic Analysis.

BackgroundThe COVID-19 pandemic continues to challenge the world’s population, with approximately 266 million cases and 5 million deaths to date. COVID-19 misinformation and disinformation led to vaccine hesitancy among the public, particularly in vulnerable communities, which persists today. Social media companies are attempting to curb the ongoing spread of an overwhelming amount of COVID-19 misinformation on their platforms. In response to this problem, the authors hosted INFODEMIC: A Stanford Conference on Social Media and COVID-19 Misinformation (INFODEMIC) to develop best practices for social media companies to mitigate online misinformation and disinformation.ObjectiveThe primary aim of this study was to develop recommendations for social media companies to address the COVID-19 infodemic. We report the methods used to execute the INFODEMIC conference, conference attendee engagement and analytics, and a qualitative thematic analysis of the conference presentations. The primary study outcomes were the identified themes and corresponding recommendations.MethodsUsing a constructivist paradigm, we conducted a thematic analysis of the 6-hour conference transcript to develop best practice recommendations. The INFODEMIC conference was the study intervention, the conference speakers were the study participants, and transcripts of their presentations were the data for this study. We followed the 6-step framework for thematic analysis described by Braun and Clarke. We also used descriptive statistics to report measures of conference engagement including registrations, viewership, post-conference asynchronous participation, and conference evaluations.ResultsA total of 26 participants spoke at the virtual conference and represented a wide array of occupations, expertise, and countries of origin. From their remarks, we identified 18 response categories and 4 themes: trust, equity, social media practices, and interorganizational partnerships. From these, a total of 16 best practice recommendations were formulated for social media companies, health care organizations, and the general public. These recommendations focused on rebuilding trust in science and medicine among certain communities, redesigning social media platforms and algorithms to reduce the spread of misinformation, improving partnerships between key stakeholders, and educating the public to critically analyze online information. Of the 1090 conference registrants, 587 (53.9%) attended the live conference, and another 9996 individuals viewed or listened to the conference recordings asynchronously. Conference evaluations averaged 8.9 (best=10).ConclusionsSocial media companies play a significant role in the COVID-19 infodemic and should adopt evidence-based measures to mitigate misinformation on their platforms.

MOTIVATIONAL FACTORS AND USER AWARENESS IN PATIENTS USING SKIN LIGHTENING CREAMS REPORTING TO DERMATOLOGY OUT PATIENT DEPARTMENT IN COMBINED MILITARY HOSPITAL RAWALPINDI

Objective: To check the motivational factors behind the usage of skin lightening creams and users’ awareness in our setup by constructing a validated questionnaire.&#x0D; Study Design: Cross-sectional study.&#x0D; Place and Duration of Study: Outpatient Department of Dermatology, Combined Military Hospital Rawalpindi, from Oct to Nov 2020.&#x0D; Methodology: Two fifty patients who were using skin-lightening creams (SLC) were recruited after the informed consent. Questionnaire was made following seven steps as mentioned in AMEE (Association for Medical Education Europe) guide 87. Data was analyzed by SPSS version 21. Reliability was be checked by Cronbach@. Descriptive statistics of responses including frequencies and percentages were calculated. Exploratory factor analysis of questionnaire was made.&#x0D; Results: Skin lightening cream users had age range from 16-56 years, majority 190 (76%) were females. Out of 158 (63.2%) were married, 180 (72%) well educated and 146 (58.4%) had Fitzpatrick skin type 3-5 predominate. Cronbach alpha of our questionnaire was 0.802. The 5-point likert scale was used to record users’ perceptions in the domains i.e., motivational factors, awareness about product and intentions. Majority, 89 (35.6%) used the product on social media recommendation. Out of 201 (80.4%) developed side effects with telangiectasia and skin thinning being the commonest ones. Out of 88 (35.2%) were using other skin whitening practices besides these creams.&#x0D; Conclusion: False perceptions about fairness and lack of user’s awareness govern the skin lightening practices in our society resulting in increased usage and side effects experienced by users. A mass educational awareness program regarding “skin lightening.........

Pattern and Factors Associated With the Utilization of Herbs As Medications Among Patients in a Tertiary Care Hospital in Western Saudi Arabia.

Background: Herbs are used worldwide as a treatment for plenty of diseases, and some herbs are used as the origin of modern medications. Although the therapeutic benefit was reported mostly from China, over time the world seemed to realize its importance. The use of herbs as medicine is also very common in the Arab region and Saudi Arabia.Objectives: This study was conducted to assess the pattern of use of herbs as medications in Saudi Arabia and factors associated with its use.Methods: A convenience sample of adult patients in a tertiary care hospital in Jeddah, western Saudi Arabia was used. A questionnaire consisted of two sections, i.e., demographic characteristics and the knowledge, attitudes, and practices on the use of herbs. The data collection was conducted during December 2019.Results: Three hundred and eighty patients were included, of which 53.3% were female. Almost half (49.7%) were college graduates. More than half of patients (55.6%) used herbs themselves, and (59%) reported members of the family used herbs for the past 12 months. Most patients (62.6%) use herbs without medical consultation. Patients used herbs following information received from family members (57.8%), social media (22.5%), doctors (8.1%), books (6.6%), or TV (5%). The most-reported herb was Anise followed by cumin and cinnamon.Conclusion: The use of herbs is common in Saudi Arabia. Herbs are mostly used for a variety of medical conditions following family members’ experience and social media recommendations. Detailed information on the type of herbs used, doses, and effects are worth further study with a focus on variation based on different regions.

Friend's recommendation on social media using different algorithms of machine learning

Friends Recommendation System identifies the behavior of users found in the dataset like user having number of followers, number of followings, common friends between followers and followings and provides the friend suggestions for the users they can follow. Recommendation system can also be used in other areas like recommending webpages to users in searching engines like Google, Explorer, Microsoft Edge. Recommending music in wynk, video recommendation in you tube, movie recommendation in amazon prime, recommendation of products to purchase in e-commerce applications like Flipkart, Amazon. Machine learning is used for providing recommendation on social networking application like Facebook, Instagram, Twitter Etc., In this Paper, recommendation of friends is done for Facebook. Similarity Coefficient calculations can be done using Jaccard Distance, Cosine Distance. Ranking Measures are done using Page Rank. Measuring the F1-Score and comparing the accuracy of different machine learning algorithms. These helps in finding which algorithm is more accurate in providing friends recommendations in social media recommendation system.

Personalized vs non-personalized recommendations: how recommender systems, recommendation sources and recommendation platforms affect trial of YouTube videos among digital natives in Saudi Arabia

Purpose This study aims to examine the difference between personalized and non-personalized recommendations in influencing YouTube users’ video choices. In addition, whether men and women have a significant difference in using recommendations was compared and the predictors of recommendation video use frequency were explored. Design/methodology/approach A survey of 524 Saudi Arabia college students was conducted using computer-assisted self-administered interviews to collect their video recommendation sources and how likely they follow the recommendation from different sources. Findings Video links posted on social media used by the digital natives were found as the most effective form of recommendation shows that social approval is important in influencing trials. Recommendations can succeed in both personalized and non-personalized ways. Personalized recommendations as in YouTube recommended videos are almost the same as friends and family’s non-personalized posting of video links on social media in convincing people to watch the videos. Contrary to expectations, Saudi men college students are more likely to use recommendations than women students. Research limitations/implications The use of a non-probability sample is a major limitation and self-reported frequency may result in over- or under-estimation of video use. Practical implications Marketers will realize that they may not need the personalized recommendation from the large site. They can use social media recommendations by the consumers’ friends and family. E-mail is the worst platform for a recommendation. Social implications Recommendation is a credible source and can overcome the avoidance of advertising. Its influence on consumers will be increasing in years to come with the algorithmic recommendation and social media use. Originality/value This is the first study to compare the influence of different online recommendation sources and compare personalized and non-personalized recommendations. As recommendation is growing more and more important with algorithm development online, the study results have high reference values to marketers in Islamic countries and beyond.

Activity in the brain\u2019s valuation and mentalizing networks is associated with propagation of online recommendations

Word of mouth recommendations influence a wide range of choices and behaviors. What takes place in the mind of recommendation receivers that determines whether they will be successfully influenced? Prior work suggests that brain systems implicated in assessing the value of stimuli (i.e., subjective valuation) and understanding others’ mental states (i.e., mentalizing) play key roles. The current study used neuroimaging and natural language classifiers to extend these findings in a naturalistic context and tested the extent to which the two systems work together or independently in responding to social influence. First, we show that in response to text-based social media recommendations, activity in both the brain’s valuation system and mentalizing system was associated with greater likelihood of opinion change. Second, participants were more likely to update their opinions in response to negative, compared to positive, recommendations, with activity in the mentalizing system scaling with the negativity of the recommendations. Third, decreased functional connectivity between valuation and mentalizing systems was associated with opinion change. Results highlight the role of brain regions involved in mentalizing and positive valuation in recommendation propagation, and further show that mentalizing may be particularly key in processing negative recommendations, whereas the valuation system is relevant in evaluating both positive and negative recommendations.

Free ad(vice): internet influencers and disclosure regulation

AbstractConsumers rely on intermediaries (“influencers”) such as social media recommendations to provide information about products. The advice may be mixed with endorsement in a way that is unobservable to the follower, creating a trade‐off for influencers between the best advice and the most revenue. This article models the dynamic relationship between an influencer and a follower. The relationship evolves between periods of less and more revenue. The model can provide insight into policies such as the Federal Trade Commission's mandatory disclosure rules. An opt‐in policy may be superior: it deregulates influencers who are reaping the rewards of past good advice.

Predicting Implicit User Preferences with Multimodal Feature Fusion for Similar User Recommendation in Social Media

In social networks, users can easily share information and express their opinions. Given the huge amount of data posted by many users, it is difficult to search for relevant information. In addition to individual posts, it would be useful if we can recommend groups of people with similar interests. Past studies on user preference learning focused on single-modal features such as review contents or demographic information of users. However, such information is usually not easy to obtain in most social media without explicit user feedback. In this paper, we propose a multimodal feature fusion approach to implicit user preference prediction which combines text and image features from user posts for recommending similar users in social media. First, we use the convolutional neural network (CNN) and TextCNN models to extract image and text features, respectively. Then, these features are combined using early and late fusion methods as a representation of user preferences. Lastly, a list of users with the most similar preferences are recommended. The experimental results on real-world Instagram data show that the best performance can be achieved when we apply late fusion of individual classification results for images and texts, with the best average top-k accuracy of 0.491. This validates the effectiveness of utilizing deep learning methods for fusing multimodal features to represent social user preferences. Further investigation is needed to verify the performance in different types of social media.

Young Working Women’s Purchase Intention towards Organic Cosmetic Products

This paper aims to assess the influence of awareness of the consumers regarding individual health and environmental health, price sensitivity, availability of the brands that are organic in nature on the consumers attitude towards purchasing organic cosmetic products. The sample was collected from 166 young working women. Furthermore, the conceptual model is tested through factor analysis, and the model is designed through path analysis in which PLS algorithm is used. The results of the study indicate a significant impact of environmental and health consciousness on the attitude of young working women consumers towards purchasing organic cosmetic products. The findings of this study could help businesses operating in the organic personal care or cosmetic industry by giving the psychology and attitude of the major cluster of target customers to grab the market. This research provides valuable recommendations for social media marketing activities. Theoretical and practical implications are discussed.

Modeling user preference dynamics with coupled tensor factorization for social media recommendation

An essential problem in real-world recommender systems is that user preferences are not static and users are likely to change their preferences over time. Recent studies have shown that the modelling and capturing the dynamics of user preferences lead to significant improvements on recommendation accuracy and, consequently, user satisfaction. In this paper, we develop a framework to capture user preference dynamics in a personalized manner based on the fact that changes in user preferences can vary individually. We also consider the plausible assumption that older user activities should have less influence on a user’s current preferences. We introduce an individual time decay factor for each user according to the rate of his preference dynamics to weigh the past user preferences and decrease their importance gradually. We exploit users’ demographics as well as the extracted similarities among users over time, aiming to enhance the prior knowledge about user preference dynamics, in addition to the past weighted user preferences in a developed coupled tensor factorization technique to provide top-K recommendations. The experimental results on the two real social media datasets—Last.fm and Movielens—indicate that our proposed model is better and more robust than other competitive methods in terms of recommendation accuracy and is more capable of coping with problems such as cold-start and data sparsity.

On Blast: A Framework for Monitoring and Responding to Online Comments About Your Graduate Medical Education Program.

On Blast: A Framework for Monitoring and Responding to Online Comments About Your Graduate Medical Education Program.

What Attracts Followers?

Although business and researchers acknowledge the importance of social media, little research has been conducted to explore what attracts people to follow brand Twitter accounts. This research attempts to achieve an analytical understanding of the factors that contribute to brand Twitter follower count based on social network and communication theories. Using data from 346 Twitter accounts spanning 48 industries and 31 countries, the authors found that the quality and quantity of tweets, as well as social learning of brand Twitter accounts are positively related to brand Twitter account followers; contrary to popular belief, the use of hashtags and links and interactivity with users are not positively related to brand Twitter account followers. The study is among the first to investigate what attracts brand Twitter account followers, which offers important strategic recommendations for brand social media managers on how to manage their social media accounts.

Curating the Evidence on Role of Personal Protective Equipment in COVID-19: A Review of Scientific Publications and Social Media Recommendations

This study aims to review Personal Protective Equipment (PPE) recommendations from scientific literature and social media sites for the general public and Health Care Professionals (HCPs) to provide effective infection control against COVID-19. A search was conducted on PubMed, WHO database, Semantic Scholar, Facebook, Twitter, Instagram, Pinterest, YouTube and Google. A total of 44 studies and 131 social media sites were included in the final analysis. Scientific literature suggested that HCPs caring for COVID-19 patients use a combination of disposable work hats, protective masks and clothing, disposable latex gloves and shoe covers while those caring for suspected COVID-19 patients, require N95 respirators. Cloth face masks were the most commonly discussed PPE for the general community. Decontamination, reuse strategies as well as PPE alternatives were discussed primarily in social media posts. This study combines data from social media and scientific literature to put forth best practices for PPE during the pandemic.Funding Statement: This work was not funded by any agency. Declaration of Interests: The authors do not have any conflicts of interest to declare.

Media Studies and the Pitfalls of Publicity

For many academics, using social media has both drawbacks and advantages. Social media may allow connection with colleagues, scholarly promotion, and public engagement, and may also open researchers up to criticism and even possible harassment. This essay argues that we must think critically about logics of self-branding and attention-seeking given these two sides of the coin of social media publicity. First, publicity can easily be weaponized against scholars engaging in projects that may be socially or politically controversial by individuals or organizations who disagree with their premises. Universities are often unprepared to deal with this negative publicity and fail to protect researchers from the consequence. Second, self-branding may undermine one’s ability to be viewed as a serious scholar and requires rigorous self-censorship, particularly for those far from the white, male ideal of the professoriate. I conclude with some recommendations for academic social media use at different career stages.

Influencers and Social Media Recommender Systems: Unfair Commercial Practices in EU and US Law

In the digital society, the availability of information is both a blessing and a curse. Particularly on social media, the question of how information spreads is mostly engulfed in the commercial interests of platforms who rely on algorithmic governance in managing information flows, and the adversarial approach taken by their users to game algorithms in the pursuit of their own benefits. The interaction between social media platforms and content creators who are engaged in monetization has barely received any scholarly attention. YouTubers deal with issues such as shadow-banning, a practice that entails diverting traffic from some channels and videos on policy grounds, and Instagrammers are increasingly scared of an outright ban from the platform. In growing their online presence, content creators depend on platforms making them visible, which is done through recommender systems. This paper aims to shed light onto how recommender systems affect influencers (also referred to as content creators) in a three step approach. First, it looks at how recommender systems work, and how they are applied by social media platforms to the activity of content creators. Second, the paper discusses the potential unfair competition issues that arise out of the use of recommender systems in relation to content creators. Third, it reflects on the suitability of existing frameworks on unfair competition in the EU and US to tackle the tensions arising between content creators and social media platforms. The study innovates existing literature in two ways. It focuses on content creators not as the perpetrators of harms against consumers or citizens, but as the potential victims of social media platforms, thereby providing an illustration of legal issues relating to social media influencers beyond advertising law. Moreover, it revolves around content moderation as an expression of algorithmic governance, with a particular focus on the impact of this governance not on the users-as-audience, but on the users-as-producers, who monetize content as a living.

Dual Implicit Mining-Based Latent Friend Recommendation

The latent friend recommendation in online social media is interesting, yet challenging, because the user-item ratings and the user–user relationships are both sparse. In this paper, we propose a new dual implicit mining-based latent friend recommendation model that simultaneously considers the implicit interest topics of users and the implicit link relationships between the users in the local topic cliques. Specifically, we first propose an algorithm called all reviews from a user and all tags from their corresponding items to learn the implicit interest topics of the users and their corresponding topic weights, then compute the user interest topic similarity using a symmetric Jensen–Shannon divergence. After that, we adopt the proposed weighted local random walk with restart algorithm to analyze the implicit link relationships between the users in the local topic cliques and calculate the weighted link relationship similarity between the users. Combining the user interest topic similarity with the weighted link relationship similarity in a unified way, we get the final latent friend recommendation list. The experiments on real-world datasets demonstrate that the proposed method outperforms the state-of-the-art latent friend recommendation methods under four different types of evaluation metrics.