Social Media Platforms Research Articles

A Classification of Features for Interpersonal Disconnectivity in Digital Media: Block, Unfriend, Unfollow, Mute, Withhold, and Eject

This article presents for the first time a classification of, and lexicon for, features for dissolving interpersonal ties in digital environments: blocking, unfriending, unfollowing, muting, withholding, and ejecting. There are two main motivations and two main contributions. The first motivation is that analyses of social media features have not included treatment of features for interpersonal disconnectivity; the second is that analyses of interpersonal disconnectivity have not included reference to the features that enable it. The two main contributions are the disambiguation of a confusing terminological field, thus making it possible to analytically distinguish between types of feature, and an intervention in the question of whether the features described in the article concern filtering <em>information</em> or avoiding <em>people</em>. Data were collected from 37 social media platforms and other services that enable digitally-mediated communication. Features for interpersonal disconnectivity were documented and then the features were grouped based on five questions about their use, where A is the person using the feature, and B is the target of A’s use of the feature: (a) Does it affect A’s feed?; (b) does it affect B’s feed?; (c) is A still connected to B?; (d) is B still connected to A?; (e) can A and B see each other’s profile?

Racial Biases Associated With Pulse Oximetry: Longitudinal Social Network Analysis of Social Media Advocacy Impact.

Pulse oximetry is a noninvasive method widely used in critical care and various clinical settings to monitor blood oxygen saturation. During the COVID-19 pandemic, its application for at-home oxygen saturation monitoring became prevalent. Further investigations found that pulse oximetry devices show decreased accuracy when used on individuals with darker skin tones. This study aimed to investigate the influence of X (previously known as Twitter) on the dissemination of information and the extent to which it raised health care sector awareness regarding racial disparities in pulse oximetry. This study aimed to explore the impact of social media, specifically X, on increasing awareness of racial disparities in the accuracy of pulse oximetry and to map this analysis against the evolution of published literature on this topic. We used social network analysis drawing upon Network Overview Discovery and Exploration for Excel Pro (NodeXL Pro; Social Media Research Foundation) to examine the impact of X conversations concerning pulse oximetry devices. Searches were conducted using the Twitter Academic Track application programming interface (as it was known then). These searches were performed each year (January to December) from 2012 to 2022 to cover 11 years with up to 52,052 users, generating 188,051 posts. We identified the nature of influencers in this field and monitored the temporal dissemination of information about social events and regulatory changes. Furthermore, our social media analysis was mapped against the evolution of published literature on this topic, which we located using PubMed. Conversations on X increased health care awareness of racial bias in pulse oximetry. They also facilitated the rapid dissemination of information, attaining a substantial audience within a compressed time frame, which may have impacted regulatory action announced concerning the investigation of racial biases in pulse oximetry. This increased awareness led to a surge in scientific research on the subject, highlighting a growing recognition of the necessity to understand and address these disparities in medical technology and its usage. Social media platforms such as X enabled researchers, health experts, patients, and the public to rapidly share information, increasing awareness of potential racial bias. These platforms also helped connect individuals interested in these topics and facilitated discussions that spurred further research. Our research provides a basis for understanding the role of X and other social media platforms in spreading health-related information about potential biases in medical devices such as pulse oximeters.

Comparing the Effectiveness and Efficiency of Machine Learning Models for Spam Detection on Twitter

A comprehensive study focused on the efficiency and effectiveness of machine learning models for Twitter spam detection was presented in this research. Spam detection on social media platforms is not only vital for user experience but also poses computational challenges due to the vast and dynamic nature of Twitter data. This investigation encompassed a range of machine learning models, including Naive Bayes (NB), Support Vector Machine (SVM), Logistic Regression (LR), k-Nearest Neighbours (KNN), and Decision Trees (DT). Their performances were scrutinized across two critical dimensions: classification accuracy and computational efficiency, as measured by the time taken for model execution. The results of the analysis revealed valuable insights into model performance. The NB and LR models emerged as the most computationally efficient models, with execution times ranging from 1.016 to 1.949 seconds. These models offered an attractive balance between speed and accuracy, making them suitable for real-time or resource-constrained applications. SVM, LR, KNN and DT were effective in classification with a performance of 98%. However, SVM models demanded longer execution times, ranging from 7.670 to 37.657 seconds. KNN and DT stroked a balance between accuracy and efficiency, with execution times ranging from 2.852 to 10.941 seconds and 1.080 to 2.442 seconds, respectively. Our research underscores the importance of considering both model effectiveness and computational efficiency when selecting a Twitter spam detection model. By offering a comparative assessment of these models, this study equipped researchers with valuable insights for making informed decisions in Twitter spam detection. It highlighted the trade-offs between model performance and efficiency, paving the way for more effective and resource-conscious approaches to combating spam on social media platforms.

Dynamics of cMOOC learner interactions in different social media: a longitudinal network analysis

ABSTRACT Social media may have an impact on people’s interaction patterns and social relationships. Investigating the interaction patterns of learners across various social media platforms can provide profound insights into the characteristics and mechanisms of connectivist learning. This study explores interaction network characteristics and the dynamic evolution of interaction patterns across multiple social media platforms within a cMOOC by using SNA and SIENA. The results show that cMOOC learners' interaction patterns have evolved with different characteristics on WeChat, blogs, and forums, eventually forming distinct interaction networks on each platform. The interaction networks on WeChat demonstrate superior performance in terms of size, cohesion, and connectivity, followed by the networks on blogs, and lastly, those on forums. WeChat and blog interaction networks exhibit a multi-center structure and modular characteristics, while forum interaction networks display a single-center structure. The evolution of interaction patterns on WeChat reveals more significant effects, such as reciprocity, transitivity, homophily, and preferential attachment, in contrast to blogs and forums, where transitivity and homophily are not prominent. The paper concludes with a discussion on the interaction patterns and network characteristics supported by three types of social media. Additionally, it highlights the significance and implications of these findings for educational practice.

The intention of Egyptian healthcare workers to take the monkeypox vaccine: is urgent action required?

Abstract Background In light of the ongoing monkeypox (MPOX) epidemic, healthcare workers (HCWs) have been in contact with various diseases. Therefore, they should take appropriate preventive and control measures to maintain their health. This study assessed Egyptian HCWs’ intentions to take MPOX vaccines. Methods A cross-sectional survey was conducted using social media platforms between September 27 and November 4, 2022. An anonymous online survey using the 5C scale was conducted using convenience and snowball sampling methods to assess the five psychological antecedents of vaccination (i.e., confidence, constraints, complacency, calculation, and collective responsibility). Results A total of 399 HCWs with a mean age of 32.6 ± 5.7 participated in this study. Of them, 89.7% were female. The five C psychological antecedents of vaccination were as follows: 55.9% were confident about vaccination, 50.6% were complacent, 56.6% experienced constraints, 60.7% calculated the risk and benefit, and 58.4% had collective responsibility. Multivariate analysis showed that high income level and having information about MPOX were significant predictors of confidence in the MPOX vaccines (adjusted odds ratio ((AOR) = 4.19, 95% CI (1.12– 15.59), P = 0.032). Participants aged 31–45 years and 19–30 years showed significant association (AOR = 2.46, 95% CI (0.85–7.15), P = 0.096) and (AOR = 4.19, 95% CI (1.39–12.64), P = 0.011), respectively. Having an idea about the MPOX vaccines significantly predicted the complacency domain (AOR = 3.77, 95%CI (1.47–9.65, P = 0.006). Moreover, precollege/undergraduate education and having an idea about MPOX vaccination were significant predictors of the constraint domain (AOR = 1.81.95% CI (1.09–2.99, P = 0.020), (AOR = 2.70, 95% CI (1.05–6.95, P = 0.038), respectively). Female sex, having a diploma, postgraduate studies, and having an idea about MPOX vaccine significantly predicted calculation domain (AOR = 2.06, 95% CI (1.05–4.04, P = 0.035), (AOR = 3.98,95% CI (1.33–11.87, P = 0.013), (AOR = 2.02, 95% CI (1.25–3.26, P = 0.004) & (AOR = 2.75. 95% CI (1.05–7.18, P = 0.039), respectively. The only significant predictor of collective responsibility was having a diploma and postgraduate studies (AOR = 3.44, 95% CI (1.21–9.78, P = 0.020), (AOR = 1.90,95% CI (1.17–3.09, P = 0.009). Conclusions Efforts to control MPOX should focus on promoting protective measures such as the vaccination of HCWs as well as raising their awareness about the updated information regarding the virus and the approved vaccines.

Assessment of beliefs and attitudes towards benzodiazepines using machine learning based on social media posts: an observational study.

Benzodiazepines are frequently prescribed drugs; however, their prolonged use can lead to tolerance, dependence, and other adverse effects. Despite these risks, long-term use remains common, presenting a public health concern. This study aims to explore the beliefs and opinions held by the public regarding benzodiazepines, as understanding these perspectives may provide insights into their usage patterns. We collected public tweets published in English between January 1, 2019, and October 31, 2020, that mentioned benzodiazepines. The content of each tweet and the characteristics of the users were analyzed using a mixed-method approach, including manual analysis and semi-supervised machine learning. Over half of the Twitter users highlighted the efficacy of benzodiazepines, with minimal discussion of their side effects. The most active participants in these conversations were patients and their families, with health professionals and institutions being notably absent. Additionally, the drugs most frequently mentioned corresponded with those most commonly prescribed by healthcare professionals. Social media platforms offer valuable insights into users' experiences and opinions regarding medications. Notably, the sentiment towards benzodiazepines is predominantly positive, with users viewing them as effective while rarely mentioning side effects. This analysis underscores the need to educate physicians, patients, and their families about the potential risks associated with benzodiazepine use and to promote clinical guidelines that support the proper management of these medications. Not applicable.

Showing climate action: exploring Fridays for Future Germany’s visual activism on Flickr

This article examines how Fridays for Future Germany (FFFG) uses photographs of climate protests to convey the politics of climate change to wider audiences. The author argues that FFFG is an ideal-type form of visual activism in which visual imagery is central to its climate activism. The article builds on climate change communication scholarship and visual social movement studies to contribute an inquiry about FFFG’s visual activism. The focus is on FFFG’s visual self-representations on the social media platform Flickr, which promises to give insights into its strategies of self-legitimation. The empirical analysis discusses the recurring visual patterns in FFFG’s visual activism based on illustrative examples and provides an interpretive reading about the implications of certain ways of seeing and showing climate action. The conclusion puts the findings in a wider political context, highlighting the importance of visualization in the (self-)legitimation of FFFG in debates about global climate governance.

The Yearning to Be Everything to Everyone: Examining the Homogenization of Social Media Platforms in the Context of Convergence

Social media platforms are continually adding new features and enhancing existing ones to attract users and extend their screen time. These platforms constantly innovate to capture users' attention and gain an edge in the competitive landscape of social media. This study focuses on identifying the similarities between features of popular social media platforms and examines whether these similarities lead to homogenization. Specifically, presenting the chronological development of these features over time is crucial for understanding the historical development and evolution of social media. The framework of this research is based on the concept of convergence. The convergence concept is used to assess whether there are commonalities in the tools, interfaces, and interaction modes offered by the platforms to their users. In this context, the study investigates how the features provided by social media platforms become similar over time and how user experiences become homogenized. The 'walkthrough method' was employed to analyze the interfaces of the selected platforms. This method involves a step-by-step examination of user experiences on the platforms to understand how features are presented and how they are utilized by users. Data were collected through notes and recordings made by researchers while using the platforms. The results indicate a significant similarity and convergence in the features offered by popular social media platforms. For instance, features such as story sharing, live streaming, filter usage, and the explore tab have become widespread across different platforms. Platforms that consider users' habits and preferences offer familiar and easily accessible experiences through these similar features. Consequently, it can be said that social media platforms create a form of homogenization in user experiences by providing similar features over time. This phenomenon offers important insights into the evolution and future development of social media. Additionally, the study suggests that the concept of convergence not only encompasses the merging of features from different technologies but also indicates a tendency for similar platforms to converge internally.

Unsupervised detection of coordinated fake-follower campaigns on social media

AbstractAutomated social media accounts, known as bots, are increasingly recognized as key tools for manipulative online activities. These activities can stem from coordination among several accounts and these automated campaigns can manipulate social network structure by following other accounts, amplifying their content, and posting messages to spam online discourse. In this study, we present a novel unsupervised detection method designed to target a specific category of malicious accounts designed to manipulate user metrics such as online popularity. Our framework identifies anomalous following patterns among all the followers of a social media account. Through the analysis of a large number of accounts on the Twitter platform (rebranded as X after the acquisition of Elon Musk), we demonstrated that irregular following patterns are prevalent and are indicative of automated fake accounts. Notably, we found that these detected groups of anomalous followers exhibited consistent behavior across multiple accounts. This observation, combined with the computational efficiency of our proposed approach, makes it a valuable tool for investigating large-scale coordinated manipulation campaigns on social media platforms.

Empowering Digital Civility with an NLP Approach for Detecting Twitter Cyberbullying through Boosted Ensembles

As the number of social networking sites grows, so do cyber dangers. Cyberbullying is harmful behaviour that uses technology to intimidate, harass or harm someone, often on social media platforms like Twitter. Machine learning is the optimal approach for cyberbullying detection on Twitter to process large amounts of data, identify patterns of offensive behaviour, and automate the detection process for corpus of tweets. To identify cyber threats using a trained model, the Boosted Ensemble (BE) technique is assessed with various machine learning algorithms such as Convolutional Neural Network (CNN), Long Short-Term Memory (LSTM), Naive Bayes (NB), Decision Tree (DT), Support Vector Machine (SVM), Bidirectional LSTM (BILSTM), Recurrent Neural Network LSTM (RNN-LSTM), Multi-Modal Cyberbullying Detection (MMCD) and Random Forest (RF). These classifiers are trained on the vectorized data to classify the tweets to identify cyberbullying threats. The proposed framework can detect cyberbullying cases precisely on tweets. The significance of the work lies in detecting and mitigating cyber threats in real-time and it impacts in enhancing the safety and well-being of social media users by reducing instances of cyberbullying and other cyber threat. The comparative analysis is done using metrices like accuracy, precision, recall and F1-score and the comparison results shows that boosted ensemble technique outperforms other compared algorithms with its overall performance. The accuracy rate of CNN, LSTM, NB, DT, SVM, RF, BILSTM and BE (92.5%, 93.5%, 84.6%, 88%, 89.3%, 92%, 93.75% and 96%), precision rate of CNN, LSTM, NB, DT, SVM, RF, RNN-LSTM and BE (90.2%, 91.3%, 88%, 85%, 86%, 91.6%, 92.1% and 94%), recall rate of CNN, LSTM, NB, DT, SVM, RF, BILSTM and BE (89.8%, 90.7%, 90%, 82%, 88.67%, 89%, 91.04% and 93.7%) and F1 score of CNN, LSTM, NB, DT, SVM, RF, MMCD and BE (90.6%, 91.8%, 85%, 84.56% 87.2%, 90%, 84.6% and 94.89%).

Designing Games for GDPR: Negotiating Understandings of GDPR in EU Schools.

In this study we combine a participatory design process with a citizen science framing, to foster a dialogue on GDPR in the school. We focus on the perspective of students, approaching them as co-researchers, in designing and evaluating a series of apps, aimed at spreading knowledge on GDPR. We have created a design space between secondary school students in Denmark and Italy, to gather insights across different countries. Results show that approaching students as designers and testers enabled them to provide honest and concrete insights on their experience with GDPR. We identified a detachment related to their posting activities, caused by how social media platforms (SoMe) challenge the boundaries between their personal and school-related ecologies.

Mental illness detection through harvesting social media: a comprehensive literature review

Mental illness is a common disease that at its extremes leads to personal and societal suffering. A complicated multi-factorial disease, mental illness is influenced by a number of socioeconomic and clinical factors, including individual risk factors. Traditionally, approaches relying on personal interviews and filling out questionnaires have been employed to diagnose mental illness; however, these manual procedures have been found to be frequently prone to errors and unable to reliably identify individuals with mental illness. Fortunately, people with mental illnesses frequently express their ailments on social media, making it possible to more precisely identify mental disease by harvesting their social media posts. This study offers a thorough analysis of how to identify mental illnesses (more specifically, depression) from users’ social media data. Along with the explanation of data acquisition, preprocessing, feature extraction, and classification techniques, the most recent published literature is presented to give the readers a thorough understanding of the subject. Since, in the recent past, the majority of the relevant scientific community has focused on using machine learning (ML) and deep learning (DL) models to identify mental illness, so the review also focuses on these techniques and along with their detail, their critical analysis is presented. More than 100 DL, ML, and natural language processing (NLP) based models developed for mental illness in the recent past have been reviewed, and their technical contributions and strengths are discussed. There exist multiple review studies, however, discussing extensive recent literature along with the complete road map on how to design a mental illness detection system using social media data and ML and DL classification methods is limited. The review also includes detail on how a dataset may be acquired from social media platforms, how it is preprocessed, and features are extracted from it to employ for mental illness detection. Hence, we anticipate that this review will help readers learn more and give them a comprehensive road map for identifying mental illnesses using users’ social media data.

Making decisions about antidepressant use during pregnancy: a qualitative interview study of a sample of women in the United Kingdom.

Background An increasing number of pregnant women now take antidepressants. Many pregnant women experience 'decisional conflict' when deciding whether to take antidepressants, but little is known about the attitudes and experiences influencing these decisions. Aim To explore the attitudes and experiences influencing women's decisions about antenatal antidepressant use. Design and setting A qualitative study using in-depth interviews with a sample of UK women who experienced antenatal depression or took antidepressants antenatally within the preceding three years. Method Recruitment adverts were placed by a perinatal mental health charity and on parenting forums and social media platforms, resulting in a convenience sample. Interview data was coded and analysed with thematic analysis using QSR NVivo. Results Twenty-two women were interviewed; half had taken antidepressants during pregnancy. Most women had concerns about adverse effects and viewed antidepressants as adjunctive to non-pharmacological treatments, which were reported as difficult to access. Some women reported that professional advice was insufficiently detailed. Women described the need to cope with their symptoms, their baby, and existing responsibilities, and related their decisions to their perceived ability to cope. This perception was influenced by physical and emotional challenges relating to pregnancy. Women's decisions were influenced by their previous experiences and by the perceived societal expectations placed on pregnant women. Conclusion Decision-making is a complex and dynamic process, personal to each woman's circumstances. Perceived ability to cope is an important factor in decision-making. Detailed information should be offered to women to support with decision-making in relation to antenatal medication use.

Impacts of social media on adolescent mental health: a rapid systematic review

The increasing use of Instagram and other social media platforms has been bringing up concerns regarding its impacts on adolescents' mental health, particularly in relation to screen time, social interaction and what is viewed, which can negatively impact their emotional wellbeing. The objective of this study is to evaluate the impacts of using Instagram and other social media platforms in adolescents mental health. For this purpose, we performed a quick systematic revision of recent studies (2020-2023) using the descriptors: Adolescents AND Instagram AND Mental Health. After analysing 113 articles, the results show that excessive usage, need for social validation and comparisons with idealistic images contribute to developing depressive symptoms. The way adolescents interact with these platforms as well as social and emotional factors also play an important role. We find that the sensible use of these technologies together with comfortable school and family environments contribute to building a sense of belonging, emotional support and safety.

Playful, coded, and emotional language: Discursive strategies of online criticism on Chinese social media

Language has always been essential in collective actions. Because offline collective actions are highly controlled in China, Chinese people often resort to online criticism, which heavily relies on the creative use of language to circumvent the government’s control of public communication. This study collects and analyzes the data of the #JiangshanjiaoandHongqiman# public discourse on Weibo to investigate the discursive characteristics of Chinese people’s online expressions. In February 2020, a mass organization of the government launched two official virtual idols on Weibo, one of the most widely used social media platforms in China. Amidst widespread discontent with the management of the pandemic, this intervention in fandom culture triggered a backlash among the Chinese Internet users. This study shows that the Chinese netizens expressed criticism through coded, playful, and emotional language. This article contributes to the literature on discursive strategies of online public discourse in China.

The legitimation of screenshots as visual evidence in social media: YouTube videos spreading misinformation and disinformation

This article explores the use of screenshots as a form of visual evidence on social media platforms. It considers their role in YouTube videos that spread misinformation and disinformation about the Notre Dame Cathedral Fire and an internet hoax, the Momo Challenge. The article draws on two social semiotic frameworks, legitimation (Van Leeuwen in ‘Legitimation in discourse and communication, 2007) and affiliation (Knight in ‘Evaluating experience in funny ways’, 2013, and Zappavigna in ‘Searchable Talk and Social Media Metadiscourse’, 2018), to analyse how screenshots and accompanying voiceovers construe technological authority and propagate social values. Seven key forms of screenshots are identified in the dataset, alongside the key social bonds that are made visually salient in the screenshots. Overall, this research contributes to how we understand the role of screenshots in instances of misinformation and disinformation, highlighting the importance of identifying the affiliation potential of the screenshot in order to determine its veracity.

Knowledge of Dentists regarding Antibiotic Prescription in Endodontics in Tripoli

The aim of this study was to assess how well dentists understand and follow guidelines for prescribing antibiotic in endodontics in Tripoli. A questionnaire link was sent to all participants via various social media platforms. All answers from the online form were automatically imported in an Excel file and were then appropriately analyzed. Descriptive statistics (such as frequencies and percentages) were calculated, and Chi-square test were used to compare between the groups. The majority (67.6%) of the respondents were general dental practitioners and most of them were females (80.4%). A smaller group (10.8%) were endodontic specialists while 21.6% belong to other specialties. Diffuse swelling was the most common condition for which antibiotics were prescribed (81.4%) and in patient with swelling & difficulty in swallowing (73.5%). Antibiotics were always prescribed by 65,7% of respondents for patient exhibiting systemic symptoms such as fever and malaise and 6.9% of respondents always prescribed antibiotics in prolonged root canal treatment. The Majority of respondents selected Amoxicillin with Clavulanate as the first choice for patients without medication allergies (72.7%) and Amoxicillin ranked as the second most preferred antibiotic (39.4%) (Table 3). On the other hand, the majority of respondents preferred clindamycin as the first choice for patients with penicillin allergy (43.6%). The majority of the participants believe that the typical duration of antibiotics course is five to seven days (98%). The majority (95.1%) of respondents were aware of drug resistance, while a very small percentage (4.9%) were not aware. The data reveal that critical importance of selecting antibiotics based on patient-specific factors, such as allergies, to ensure effective and safe management of endodontic infections. The knowledge and attitude of dentists regarding antibiotic prescription for endodontic treatments require further improvement.

We are stronger when we are connected: Queer counterpublics and the Korean Queer Culture Festival

This paper examines the emergence of queer counterpublics in South Korea through a case study on the opening ceremony of the Korean Queer Culture Festival (KQCF) in 2015. Due to an outbreak of the Middle East Respiratory Syndrome (MERS), the organizers live-streamed the event via YouTube instead of inviting the participants to the Seoul City Plaza, where they set up the stage. During the opening ceremony, social media platforms momentarily became alternative spaces for queers to interact with each other on a real-time basis. In analyzing a series of concerted actions, such as lining up in front of a police station for a permit, setting up a stage, writing a statement, shooting a video for broadcast, watching the event on YouTube, posting messages and pictures on social media, and exchanging messages with friends, I discuss how queer counterpublics emerge, contesting the heteronormative and heteropatriarchal public sphere in South Korea.

Toward a computational mixed methods framework to measure online deliberative discourse

This article proposes and tests a reproducible framework for a computational method to measure social media-based deliberative discourse by analyzing commentary surrounding the Canadian convoy protests of COVID-19 vaccine mandates and restrictions. Employing a combination of analytic calculations, alongside tools such as Google Perspective and Linguistic Inquiry and Word Count (LIWC), this article assesses the quality of online deliberative discourse using established measures of deliberation including the variables rationality, interactivity, equality, and civility. We propose computational approaches to measuring these variables, and work toward validating our approach by observing correlations between an established computational measure of online deliberation-cognitive complexity. This computational approach is tested using Twitter and Reddit commentary related to the convoy protests that took place in Ottawa, Canada, during February 2022, which influenced the emergence of similar protests around the world. In addition to testing our proposed online deliberative discourse measurement framework, this case study provides insight into the deliberative characteristics of the Twitter and Reddit social media platforms.

Modelling the Predictors of Social Media Marketing on Consumer Buying Behavior in Malaysia

Despite the rapid increase in internet and mobile users in Malaysia, the growth of online purchases through social media platforms has not kept pace with this broader digital trend. This study investigates the key predictors that influence consumer buying behavior through social media marketing, with a specific focus on the roles of Perceived Ease of Use (PEOU) and Perceived Usefulness (PU) of these platforms. Employing a quantitative cross-sectional research design, data were collected from 115 respondents in Malaysia who have experience making online purchases via mobile applications. The study also examines variations in consumer experiences based on demographic factors, including age, gender, education level, and geographic region. The findings indicate that both PEOU and PU are significant predictors that shape consumer buying behavior on social media platforms. This research provides valuable insights for marketers seeking to enhance the effectiveness of their social media marketing strategies. By better understanding the factors driving consumer behavior in the Malaysian market, businesses can design more targeted and personalized campaigns for specific consumer segments, ultimately improving their competitive advantage in this digital landscape.