Social Media Platforms Research Articles

Infrastructures of (Im)mobility: maps, mines, militarization, and the making of remote borderland in Kargil

ABSTRACT This article critiques the dominant paradigm of mobility in borderland studies by foregrounding the concept of im/mobility as a crucial lens for understanding the marginalization of border regions. Employing ethnographic methods in Kargil, it posits that infrastructural development, specifically road construction, constitutes an extension of the border-making process, functioning as an instrument of state control and surveillance. These infrastructural regimes impose a distinct territoriality, disrupting established socio-spatial practices and engendering a condition of remoteness, effectively transforming the region into a ‘captive’ borderland. By enhancing connectivity to previously inaccessible areas, these road networks facilitate state surveillance and cartographic practices, rendering the region legible to state power. However, the article also acknowledges the agency of local populations in negotiating and, at times, transgressing these state-imposed structures, notably through the utilization of social media platforms.

Exploring the Effect of Brief Preventive Videos on Mental Health Help-Seeking for Early Psychosis in a Young Community Sample.

A key factor influencing the duration of untreated psychosis is that young individuals typically do not seek help during their initial psychotic experiences. This online study aimed to explore the efficacy of preventive video interventions providing information on psychosis on the attitudes towards seeking mental health care among young adults from the general population. Participants (N = 147) were randomised to one of the following online conditions: a short 3-min video of an empowered patient or of a psychiatrist describing different aspects of mental illness, a short control video or no video. Then, participants answered the Inventory of Attitudes to Seeking Mental Health Services (IARSSM) to measure attitudes towards seeking mental health. A Kruskal-Wallis one-way ANOVA on the total IARSSM score revealed no significant effect of the group on attitude towards mental health care (χ2(3) = 6.52, p = 0.09). A small but statistically significant effect was found for the IARSSM factor "indifference to stigma" (χ2(3) = 8.50, p = 0.04), with slightly lower levels of indifference to stigma in the patient video group (M = 20.5, SD = 6.50) compared to the psychiatry video group (M = 24.5, SD = 4.35). Emphasising nonconformity with mental health stereotypes, portraying positive aspects and utilising short video formats on social media platforms can potentially reduce stigma in the short term. Long-term effectiveness and identification of specific factors optimising attitudes towards mental health help-seeking warrant further investigation.

Fear and anxiety of medical sciences students during the COVID-19 pandemic in Iran: A web-based cross-sectional study

Since the start of the epidemic, Iran has been severely impacted by the Coronavirus Disease 2019 (COVID-19). Therefore, measuring the level of fear and anxiety in students can effectively help policymakers and educational administrators to be able to take practical actions to reduce fear and anxiety in students by using existing mechanisms. This research investigated the anxiety and fear in Iranian university students during the COVID-19 pandemic and explored the potential influential factors. Data on participants was collected using a web-based cross-sectional survey using an online questionnaire distributed via social media platforms. The Corona Disease Anxiety Scale and Fear of COVID-19 Scale measured the presence of anxiety and fear in participants. The Odds Ratio (95% CI) was calculated using logistic regression models. A total of 1,146 individuals with a mean age of 22 (SD=3.67) years participated in the study. The prevalence of anxiety among participants was 30.9%. The mean anxiety and fear of COVID-19 score among the participants was 13.71 (SD=4.51). According to this study, the adjusted odds of having psychological symptoms, physical symptoms, and total anxiety increased in subjects who were not single and Ministry of Science or Research and Technology (MSRT) students. The adjusted odds of psychological symptoms and total anxiety were also decreased in the individuals who sometimes and rarely followed COVID-19 news. Physical symptoms were reduced in some of the family members who did not have COVID-19 and in those who were not concerned about universities being opened and the adverse effects of COVID-19. University students have been significantly affected by the COVID-19 confinement. It has been suggested that universities must prioritize the mental health of their students during such challenging times.

Quantum-inspired framework for big data analytics: evaluating the impact of movie trailers and its financial returns

In the context of the growing influence of businesses and marketers on social media platforms, understanding the impact of emotionally charged content on consumer behavior has become increasingly crucial. This study proposes a novel framework, leveraging quantum computing principles, to assess the emotional impact of movie trailers. The framework incorporates big data analytics and utilizes Quantum Walk andQuantum Time Series models to investigate the relationship between a movie trailer's emotional intensity and its financial performance. Unlike sequential problem-solving approach of traditional computing models, Quantum superposition allows exploring multiple options at once. An analysis of 141 movie trailers released after January 1, 2022, revealed a positive correlation between a trailer's emotive score and its financial success. These findings suggest that trailers evoking a stronger emotional response tend to achieve greater box office returns compared to those with a lower emotional impact. This research underscores the pivotal role of emotionally resonant content in shaping consumer behavior and cinematic outcomes. It would offer valuable insights for filmmakers and marketers to optimize audience engagement and financial returns.

Tumor infiltrating lymphocytes (TILs) - Pathologia, quo vadis? - A global survey.

Tumor-infiltrating lymphocytes (TILs) and the tumor microenvironment have become increasingly important in cancer research, and immunotherapy has achieved major breakthroughs in improving patient outcomes. Despite the significant role of the pathologist in identifying, subtyping and reporting TILs, the implementation and assessment of TILs in pathology routine remains vague. To assess the actual use of TILs in routine clinical practice, a formal standardized questionnaire was disseminated on two social media platforms ("X" and LinkedIn) and by email in June 2024. Based on the results, we conducted a literature review on TILs via Medline/Pubmed in the two most scored and reported entities, namely malignant melanoma and colorectal cancer (CRC). 77 participants from 24 different countries around the world, mostly pathologists (n = 63, 82.0 %), completed the survey. More than half of the participants do not assess or report TILs in their daily (clinical) practice, a trend consistent across the countries included in the study. A variety of methods are used to report TILs, ranging from Artificial Intelligence (AI)-based scoring algorithms to quantification by eyeballing. Despite recognizing the importance of TIL assessment in clinical routine, many participants find it time-consuming and express a strong preference for AI-based quantification. Our survey reflects the perspective of mostly early career pathologists who recognize the importance of TILs in cancer but face challenges in implementation. The development of AI tools and consensus guidelines could alleviate these barriers. In addition, increasing the visibility and understanding of the role of pathologists within the medical community remains critical.

Unlocking Potential Score Insights of Sentimental Analysis with a Deep Learning Revolutionizes

Online hate has emerged as a rapidly growing issue worldwide, often stemming from differences in opinion. It is crucial to use appropriate language and words on social media platforms, as inappropriate communication can negatively impact others. Consequently, detecting hate speech is of significant importance. While manual methods are commonly employed to identify hate and offensive content on social media, they are time-consuming, labor-intensive, and prone to errors. Therefore, AI-based approaches are increasingly being adopted for the effective classification of hate and offensive speech. The proposed model incorporates various text preprocessing techniques, such as removing extraneous elements like URLs, emojis, and blank spaces. Following preprocessing, tokenization is applied to break down the text into smaller components or tokens. The tokenization technique utilized in this study is TF-IDF (Term Frequency–Inverse Document Frequency). After tokenization, the model performs the classification of hate and offensive speech using the proposed BiLSTM-based SM-CJ (Scalable Multi-Channel Joint) framework. The BiLSTM-based SM-CJ model is particularly effective in detecting hate, offensive, and neutral tweets due to its ability to capture both forward and backward contexts within a given text. Detecting hate speech requires a comprehensive understanding of the text and the identification of patterns spanning across multiple words or phrases. To achieve this, the LSTM component of the BiLSTM model is designed to capture long-term dependencies by utilizing information from earlier parts of the text. The proposed SM-CJ framework further aligns the input sequence lengths fetched from the input layer, enabling the model to focus on specific segments of the input sequence that are most relevant for hate speech detection. This approach allows the model to accurately capture derogatory language, and subtle nuances present in hate speech. Finally, the performance of the proposed framework is evaluated using various metrics, including accuracy, recall, F1-score, and precision. The results are compared with state-of-the-art approaches, demonstrating the effectiveness of the proposed model. Doi: 10.28991/ESJ-2025-09-01-03 Full Text: PDF

Which factors influence emotional overeating among workers and students? Is online food delivery consumption involved? Results from the DELIvery Choice In OUr Society (DELICIOUS) cross-sectional study.

Emotional overeating (EO) is a complex behavior that involves consuming excessive food in response to emotions, presenting health risks due to poor dietary choices. Research indicates a notable prevalence of EO, especially among females, and its association with disordered eating behaviors like binge eating (BE). The rise of online food delivery (OFD) services has raised questions about their potential role in exacerbating EO. This study aimed to explore EO behaviors among Italian workers and students, assessing the influence of various personal, socio-demographic, and lifestyle factors, including OFD. This study is a secondary data analysis utilizing the DELICIOUS survey data, conducted nationwide through social media platforms from July 12, 2022, to February 1, 2023. Participants completed validated questionnaires, including the EO Questionnaire (EOQ-5). The sample was stratified according to worker/student status. Statistical analyses, including descriptive statistics (chi-squared tests, Mann-Whitney tests) and multivariable logistic regression, were performed to assess the influence of key variables on EO. The sample comprised 2853 workers and 898 students, with almost 75% exhibiting EO behavior above the threshold. Several factors were significantly related to a higher occurrence of EO. Positive associations included bad health literacy, female gender, occasional smoking, higher BMI, depression risk for students, and low work-life balance, job dissatisfaction, screen time while eating, and depression risk for workers. Surprisingly, OFD usage showed no relationship with EO. While some findings align with previous research, like the depression-EO link, others, such as the absence of a link between OFD and EO, were unexpected. The results suggest that personal, socio-demographic, and lifestyle factors significantly influence EO, with OFD serving more as a means of food access influenced by individual characteristics, rather than a direct EO risk factor.

Integration of data sets for modelling gender violence and perception of insecurity.

The dataset offers a comprehensive information to analyse cities and neighbourhood that are potentially unsafe for women, this information has been collected for four cities: Toluca (Mexico), Valencia (Spain), Dublin (Ireland) and San Francisco (USA). The collection includes quantitative and qualitative variables obtained and processed from open data, georeferenced publications from a social media platform, and points located through participatory mapping sessions. The data is structured in raw format, organized by country and city, and categorized according to the data source used while processing, which allows unrestricted access with most data analysis software and it does not depend on specific licenses. This format includes both geometric information and associated attributes allowing reusability and analysis in different environments. Additionally, the release of this data allows developing models tailored to specific local contexts and represents a significant advance in open data access as stated in the Sustainable Development Goal 5 (SDG 5), especially in relation to indicator 5.2.2. In general, this indicator faces a lack of sufficient data for accurate measurement, which limits the ability to accurately assess and address gender-based violence. By providing an open and flexible resource, the dataset not only facilitates comparative research and informed policymaking, it also supports the international commitment for transparency and contributes to filling existing gaps in information on violence and insecurity.

Analysis of Suicide-related Tweets During the COVID-19 Pandemic

Background: The COVID-19 virus started in 2019 and badly affected the different sectors of many countries around the world. Based on this, financial difficulties, loss of loved ones, sudden anger, relationships, family disputes, and psychological distress increased, and individuals were stalled from carrying out their lifestyle in a normal way, and some individuals were even motivated to commit suicide. Objective: It is important to reduce the number of suicides and identify the reasons for this situation. Through this research, the focus is on identifying the main topics discussed relevant to suicides during the COVID-19 pandemic. Methods: Individuals use Twitter, a social media platform, to share their ideas freely and publically. We collected 9750 primary data through Twitter API (Application Programming Interface). After preprocessing and feature extraction by TF-IDF (Term Frequency-Inverse Document Frequency), we applied the LDA (Latent Dirichlet Allocation) and Probabilistic Latent Semantic Analysis (PLSA) topic modeling algorithms to identify topics. Results: Based on the LDA results, we extracted ten different topics under the three themes, such as the impact of COVID-19, human feelings, getting support, and having awareness. Intertopic Distance Map, Most Salient Terms, and Word Clouds Visualization are used to check the results. The coherence score and perplexing value are used to measure how interpretable the extracted topics are to humans. PLSA also extracted 25 topics with their probabilities, and Kullback–Leibler (KL) divergence was used to check the results. Conclusion: We were able to gain insight into human emotions and the main motivations behind suicide attempts using the topics we extracted. Expert feedback proved that LDA results were better than PLSA. Based on that, we found the main impact of COVID-19 on human lives, how human feelings were changed positively and negatively during that period, what supporting and awareness methods people used, and what they preferred. The required measures can then be taken by those responsible authorities and individuals to prevent, reduce, and get ready for this kind of suicidal incident in the future.

Social media activism and women's health: Endometriosis awareness and support.

While endometriosis is estimated to affect 1 in 10 women globally, awareness of the disease as well as research and funding to fight the disease remains lacking as compared with other chronic diseases. This study examines how social media users utilized Instagram to raise awareness of and mobilize activism around endometriosis by analyzing prominent topics, word associations, and feminism themes in endometriosis-tagged posts on Instagram. We used a mixed-method approach of combining computational analyses (topic modeling and word association) and human coding (qualitative thematic analysis) of Instagram posts on endometriosis. Our results show that while Instagram users discussed a variety of issues related to the disease on the social media platform, these issues tend to focus on four topic areas: (i) living with endometriosis, (ii) pregnancy and endometriosis, (iii) awareness and empowerment, and (iv) women's health and wellbeing. In addition, social media users widely incorporated three feminism themes: (i) bringing attention to invisible disability, (ii) solidarity and support among women, and (iii) advocacy and activism around women's health. People with endometriosis use Instagram to create an online public sphere for raising awareness of the disease, offering networking and support to other endometriosis patients, and creating a movement for enhancing women's health and wellness. In doing so, they are also addressing sexism and misconceptions about endometriosis that have traditionally contributed to normalizing women's experience of pain associated with endometriosis and consequently delaying endometriosis diagnoses.

Do you have depression? A summative content analysis of mental health-related content on TikTok.

TikTok is a global social media platform with over 1 billion active users. Presently, there are few data on how TikTok users navigate the platform for mental health purposes and the content they view. This study aims to understand the patterns of mental health-related content on TikTok and assesses the accuracy and quality of the advice and information provided. We performed a summative content analysis on the top 1000 TikTok videos with the hashtag #mentalhealth between October 12 and 16, 2021. Six content themes were developed to code the data: (1) a personal story, perspective, or confessional, (2) advice and information, (3) emoting, (4) references to death, (5) references to science or research, and (6) a product or service for sale. Advice and information were further assessed by clinical experts. A total of 970 mental health-related videos were pulled for our analysis (n = 30 removed due to non-English content). The most prevalent content themes included a personal story, perspective, or confessional (n = 574), advice and information (n = 319), emoting (n = 198), references to death (n = 128). Advice and information were considered misleading in 33.0% of videos (n = 106), with misleading content performing better. Few videos included references to scientific evidence or research (n = 37). Healthcare practitioners and researchers may consider increasing their presence on the platform to promote the dissemination of evidence-based information to a wider and more youth-targeted population. Interventions to reduce the amount of misinformation on the platform and increase people's ability to discern between anecdotal and evidence-based information are also warranted.

Integrating social media platforms into expatriate training and development programs: an experiential learning perspective

PurposeSocial media platforms, although often vilified in popular media, carry several potential benefits for expatriate training. This study set out to discuss in some depth whether social media platforms can be employed to enhance expatriate training outcomes, such as performance, adjustment and cultural integration.Design/methodology/approachDrawing on insights from experiential learning theory, this study develops propositions and a conceptual model relating to the incorporation of social media platforms into expatriate training.FindingsJoining insights from the expatriate training literature with experiential learning theory, this study proposes several potential avenues for social media platforms as training tools. While social media use is not likely to be efficacious in itself, this study argues that social media platforms can be leveraged to enhance expatriate language training, acculturation and professional networking. These mediating relationships enhance social media’s effects on expatriate performance and adjustment.Originality/valueThis study paves the way for future empirical works regarding the implementation of novel tools in the pursuit of expatriate excellence. This paper develops a comprehensive model illustrating how social media platforms can positively impact expatriate training outcomes.

Are YouTube videos a useful source of information on avoidant/restrictive food intake disorder?

Background: Social media platforms are widely used to share health information via videos. Avoidant/restrictive food intake disorder (ARFID) is a new eating disorder term that is one of the most searched topics online. This study aims to determine the quality and content of videos about ARFID on YouTube. Methods: On May 6, 2024, the term “Avoidant/restrictive food intake disorder and/or ARFID” was searched on YouTube. The videos’ quality was assessed using three scoring systems: DISCERN, Global Quality Score (GQS), and the Journal of the American Medical Association (JAMA). DISCERN scores defined the top 25% of videos as the most reliable and top-quality (Q1). Results: Videos (N = 295) were assessed, and 192 videos met our inclusion criteria. The most common video topic was the symptoms and diagnoses of ARFID (64.6%). Videos associated with ARFID were fair to poor quality according to DISCERN (90.6%) and GQS (79.7%). 80.2% of the videos targeted patients, and they had lower quality scores than those targeting healthcare providers. 68.42% of videos for healthcare providers were high-quality (Q1), while only 14.29% of videos targeting patients were Q1. The GQS and JAMA scores showed a negative correlation with the viewer interaction scores of the videos. Conclusions: Our findings underline the low quality of YouTube videos about ARFID. It is concerning to find that viewer interaction with the videos increases as video quality decreases. The present study highlights the risk of spreading poor-quality information via YouTube videos to the public, particularly patients.

The Impact of Social Media Platforms on Art Marketing

The Impact of Social Media Platforms on Art Marketing

Sentiment Classification from Social Media Data with Gaussian Conditional Rule-Based Deep Convolutional Neural Network

Social media platforms share several emotions in the form of text, audio, and images, where the text-shared data are less convincing to understand the emotions. The classification of the sentiments should be accurate to achieve a better understanding of the values in the commercial industry. Twitter has proven to be an effective instrument for text-based sentiment analysis when compared to other traditional assessments like surveys and stock market performance records. Though the existing research on sentiment analysis achieved good results of performance, the techniques had drawbacks such as long-term sentence prediction, less robustness, high computational resources, lack of complementary feature extraction, and so on. In this research, the Gaussian conditional rule-based deep convolutional neural network (DCNN) (Gaussian conditional rule-based DCNN) is proposed to tackle the limitation of the existing techniques, where the Gaussian distribution is applied for the selection of the kernel filters in the formation of the feature maps, and the classifier parameters are adaptively trained using the proposed adaptive nature optimizer (ANO). Through the integration of the conditionality of the random forest algorithm into the developed architecture, the model attains the potential to adapt the processing concerning the supplementary contextual information or features. Utilizing the prominent word embedding approach maximizes the input quality, converts words into continuous vector representations in a high-dimensional space, and captures both syntactic and semantic associations between words. In addition, the Graph embedding preserves some of the graph’s structural characteristics and effectively captures the syntactic dependencies, co-occurrences, semantic similarities, as well as pertinent linguistic correlations among the texts. Specifically, the proposed ANO algorithm and the conditional rule exploited in the model provide stable as well as robust performance in the classification of sentiments. The experimental results demonstrate that the Gaussian conditional rule-based DCNN model achieves 98.36 %, 98.80%, and 97.44% for accuracy, specificity, and sensitivity, respectively.

Watching the right video at the right time - A study of the effect of temporal landmarks on personal branding preferences for short videos.

Thanks to the development of digital technology, short-videos have become fully integrated into users' daily lives. Time, as an intangible yet important environmental variable, subtly influences individuals' cognition and behavior in various ways. Among the existing studies on short-video platforms, there has been limited focus on the impact of temporal landmarks on social media users' behavior and preferences. To address this gap, this paper examines how beginning or ending temporal landmarks in short-videos activate a sense of starting or finishing within a social media context. It explores the mechanisms of user preferences for personal branding under different temporal perceptions and further examines the boundary role of users' personal traits. The results show that the main effects of temporal landmarks on personal brand preferences, as well as the mediating role of attributional needs and uniqueness needs and the moderating role of self-construal, are significant. Specifically, for users with independent self-construal, those activated by beginning temporal landmarks show a higher preference for competence-based personal brands; for users with interdependent self-construal, those in scenarios with ending temporal landmarks exhibit a stronger preference for warmth-based personal brands. This paper enriches research on user behavior related to temporal landmarks in social media, expands the applicability of the stereotype content model in personal branding, and provides suggestions for short-video social media platforms to attract, convert, and monetize user traffic. It also offers marketing insights for businesses on arranging activities according to consumer traits and temporal factors.

Perceived impact of physiotherapy-related debates on the social platform “X” on physiotherapists’ professional development and knowledge acquisition: a cross-sectional study

BackgroundIn Saudi Arabia, the social media platform “X” (formerly known as “Twitter”) is widely utilized by healthcare professionals. This study aimed to assess the perceived impact of physiotherapy-related debates on X on the professional development and knowledge acquisition of physiotherapists.MethodsA cross-sectional, online survey-based study was conducted among licensed physiotherapists in Saudi Arabia. The study followed established international reporting guidelines, including the Strengthening the Reporting of Observational Studies in Epidemiology, Checklist for Reporting Results of Internet E-Surveys, and Checklist for Reporting Results of Internet E-Surveys.ResultsA total of 188 physiotherapists participated, with the majority (n = 143;76.1%) actively using X. Many participants indicated positive impacts on their perspectives or approaches to physiotherapy, enhancement of their critical thinking skills, and acquisition of new knowledge or insights (n = 73; 51.0% agreed, n = 26; 18.2% strongly agreed). Furthermore, participants reported gaining knowledge of research findings (n = 45; 31.5% agreed, n = 44; 30.8% strongly agreed) and new treatment techniques (n = 48; 33.6% agreed, n = 42; 29.4% strongly agreed). Additionally, 30.1% (n = 43) agreed and 6.3% (n = 9) strongly agreed that these debates influenced their professional development, whereas 46.2% (n = 66) remained neutral.ConclusionsPhysiotherapy-related debates on the social media platform X positively impacted physiotherapists’ perspectives by enhancing critical thinking and knowledge acquisition. Such online debates may serve as valuable tools for professional development in physiotherapy practice.

Artificial Intelligence's Detrimental Effects on Digital Marketing via Social Media Platforms and Case Studies

Digital marketing has seen a rapid transformation due to artificial intelligence (AI), particularly on social media platforms. Although it has many benefits, such as data-driven decision making, automation, and personalized experiences, the drawbacks are frequently outweighed by the positives. The negative consequences of AI on digital marketing strategies, namely through social media channels, are examined in this study. It looks at topics including privacy issues, the propagation of false information, moral dilemmas, and the decline of real human connection in marketing tactics. The effects of integrating AI into these platforms are demonstrated through case studies from businesses such as Facebook, Twitter, and YouTube

Professional Whats App Blast: Best practice direct digital marketing 4.0 to improve students’ religious moderation implementation

There were no final literatures both national and international research concerning direct digital marketing of religious moderation using application. One of the solutions for direct digital marketing of religious moderation is using professional Whats App Blast. The study was conducted to determine the effect of professional Whats App Blast to improve the implementation of religious moderation. Mixed method acts as a method in this research. Data collection techniques using tests and interviews. The number of sample is 150 students from three campuses in West Java Province. Random sampling technique was used as a sampling technique. The calculation of data analysis shows that Asymp. Sig. (2-tailed) =0.000 less than Asymp. Sig. (2-tailed) <0.05 which means H0 is rejected. The results of the data analysis are reinforced by the calculation of the average value after the post test is higher than the pre test. The average student pre test is 52.53, while the average student after post test is 74.77. In addition, the author took other strength data from interviews. In conclusion, Professional Whats App Blast has a very positive contribution in improving the implementation of religious moderation of students in West Java Province. The next researcher is recommended to use other social media platforms besides Professional WA Blast and also the religious moderation information content is not limited to flyers.

Exploring the Relationship between Social Media Usage and Civic Engagement in the Digital Era: A Mediation Moderation Analysis

Researchers investigate the relationship between social media activities and civic involvement in today's digital environment by using mediation-moderation analytical approaches. Today's social media platforms Facebook Twitter and Instagram deliver transformative communication tools that support civic participation by empowering voting and supporting volunteering and advocacy endeavors. Users connect emotionally on social media which translates into civic participation through Online Political Efficacy as the mediating factor. Digital literacy functions as a tuning element that helps users improve their online information assessment and navigation skills thus, they gain better results from social media engagement in civic activities. Study authors bridge existing research gaps through their consolidated model which analyzes Online Political Efficacy together with digital literacy. This study analyzed the relationship between social media usage patterns and civic engagement levels using responses from 200 university student participants and active social media users through dedicated questionnaires. Among the research findings are sizeable positive correlations establishing Online Political Efficacy and digital literacy as fundamental influences. Online Political Efficacy increases social media usage's relationship to civic participation according to mediation results and digital literacy stands out as a primary factor according to moderation results. The study presents real-world advice designed to improve civic engagement through investments in digital literacy education and emotionally compelling content creation for policymakers. Educators join forces with social media platforms to put this guidance into action. The research extends knowledge about social media through its inspection of societal participation barriers such as misinformation and digital inequality.