Patterns Of Engagement Research Articles

Development Of English Education Using Big Data And Learning Analytics

With English education using big data and learning analytics represents a transformative approach to enhancing language learning outcomes and pedagogical practices. By harnessing the power of big data analytics, educational institutions can collect, process, and analyze vast amounts of learner data, including student interactions with digital learning materials, performance on assessments, and engagement metrics. Through sophisticated learning analytics techniques, educators gain valuable insights into students' learning behaviors, preferences, strengths, and areas for improvement. The paper explores the application of big data analytics and learning analytics in English language education within Chinese universities. Utilizing a diverse dataset, including proficiency levels, engagement metrics, and demographic characteristics, the study employs Median Clustering Fuzzy Learning Analytics (MCFLA) to predict students' engagement levels based on language proficiency scores. Findings reveal varying proficiency levels among students, alongside mixed patterns of engagement. The integration of MCFLA proves effective in identifying trends and guiding educational interventions. Median scores provide benchmarks for assessing performance, while demographic factors offer additional insights. Findings reveal varying proficiency levels among students: 40% beginner, 30% intermediate, and 30% advanced. Mixed patterns of engagement are observed, with 50% high engagement, 30% medium, and 20% low. Integration of MCFLA effectively predicts engagement trends, with 75% accuracy. Median scores provide benchmarks for assessing performance: beginner (Vocabulary: 63, Grammar: 66, Reading: 68, Oral Communication: 55), intermediate (Vocabulary: 78, Grammar: 79, Reading: 80, Oral Communication: 72), and advanced (Vocabulary: 90, Grammar: 88, Reading: 95, Oral Communication: 80). Demographic factors such as gender (50% male, 50% female) and socio-economic status (40% middle class, 30% upper class, 30% lower class) offer additional insights.

Frequent Disengagement and Subsequent Mortality Among People With HIV and Hepatitis C in Canada: A Prospective Cohort Study.

The cascade of care, commonly used to assess HIV and hepatitis C (HCV) health service delivery, has limitations in capturing the complexity of individuals' engagement patterns. This study examines the dynamic nature of engagement and mortality trajectories among people with HIV and HCV. We used data from the Canadian HIV-HCV Co-Infection Cohort, which prospectively follows 2098 participants from 18 centers biannually. Markov multistate models were used to evaluate sociodemographic and clinical factors associated with transitioning between the following states: (1) lost-to-follow-up (LTFU), defined as no visit for 18 months; (2) reengaged (reentry into cohort after being LTFU); (3) withdrawn from the study (ie, moved); (4) death; otherwise remained (5) engaged-in-care. A total of 1809 participants met the eligibility criteria and contributed 12 591 person-years from 2003 to 2022. LTFU was common, with 46% experiencing at least 1 episode, of whom only 57% reengaged. One in 5 (n = 383) participants died during the study. Participants who transitioned to LTFU were twice as likely to die as those who were consistently engaged. Factors associated with transitioning to LTFU included detectable HCV RNA (adjusted hazards ratio [aHR], 1.37; 95% confidence interval [CI], 1.13-1.67), evidence of HCV treatment but no sustained virologic response result (aHR, 1.99; 95% CI, 1.56-2.53), and recent incarceration (aHR, 1.94; 95% CI, 1.58-2.40). Being Indigenous was a significant predictor of death across all engagement trajectories. Disengagement from clinical care was common and resulted in higher death rates. People LTFU were more likely to require HCV treatment highlighting a priority population for elimination strategies.

OA41 Exploring patterns of patient engagement with electronic patient reported outcome measures in inflammatory arthritis using latent class growth modelling

Abstract Background/Aims Routine collection of electronic patient reported outcome measures (ePROMs) can facilitate dynamic disease activity monitoring in patients with inflammatory arthritis (IA). In a real-world cohort of patients with IA, we describe patient characteristics associated with ever engaging with ePROMs, evaluate whether there are distinct groups of patients with similar engagement trajectories over time and identify patient characteristics associated with these groups. Methods We conducted a prospective observational study based on routinely collected data from a remote monitoring platform (RMP) at Guy’s and St Thomas’ NHS Foundation Trust in London, UK, between 18 December 2018 and 15 August 2022. Patients with rheumatoid arthritis (RA), psoriatic arthritis (PsA) and axial spondyloarthritis (AxSpA) were sent text message requests to complete 4-weekly disease specific PROMs. A patient was defined as an engager if they completed at least one PROM episode during the study period of 68 weeks. Logistic regression (LR) was used to identify associations between baseline characteristics and any PROM engagement. Latent class growth modelling (LCGM) was used to determine PROM engagement trajectories, with multinomial LR exploring baseline characteristic associations with latent trajectory class membership. Results Of 1,203 patients on RMP in the study period, 1,129 met the inclusion criteria, of which 91.5% (95% CI 89.7% - 93.0%) completed at least one PROM. Mean age was 49 years and 64% were female. 60% had RA, 24.4% had PsA and 15.7% had AxSpA. Overall engagement was relatively high throughout, with continued engagement >60% over the remaining 68 weeks. Older patients (adj OR 0.98, 95% CI 0.96 - 0.99), those from more deprived areas (adj OR 0.80, 95% CI 0.68 - 0.96) and those who attended <80% of their outpatient visits in the preceding 3 years (adj OR 0.48, 95% CI 0.26 - 0.89) were less likely to complete a PROM. Four latent trajectory classes were identified: consistent engagers (38.1%), high-variable engagers (24.3%), low-variable engagers (19.5%) and never & dis-engagers (18.1%), with a mean (SD) overall response rate per class of 96.5% (7.1), 74.1% (12.1), 35.6% (13.8) and 7.5% (12.0) respectively. Compared to the consistent engager class, the two variable engager classes were significantly more likely to be younger at baseline, whilst the never & dis-engager class was more likely to be from a deprived area. Having an IA type of PsA and previous clinic non-attendance was associated with membership of the three lower engaging classes. Conclusion We have described key patient characteristics that influence engagement with ePROMs in a real-world cohort of patients with IA, and patterns of engagement over time. Overall engagement was very high with >90% of patients enrolled engaging with PROM completion. There is still scepticism among clinicians about remote monitoring’s ability to work, and our findings should be reassuring: patients do engage with ePROMs over time. Disclosure N. Arumalla: None. Z. Yang: None. S. Norton: None. M. Crooks: None. A. Khan: None. R. Fitzgerald: None. R. Gilligan: None. E. Lindberg: None. T. Tabibi: None. Y.L. Man: None. S. Subesinghe: None. J.B. Galloway: Consultancies; Abbvie, Biovitrum, BMS, Celgene, Chugai, Galapagos, Gilead, Janssen, Lilly, Pfizer, Novartis, Roche, Sanofi, Sobi, UCB. Honoraria; Abbvie, Biovitrum, BMS, Celgene, Chugai, Galapagos, Gilead, Janssen, Lilly, Pfizer, Novartis, Roche, Sanofi, Sobi, UCB. T. Garrood: Shareholder/stock ownership; Serac healthcare. Honoraria; Abbvie, UCB. Grants/research support; Versus Arthritis, Pfizer, Gilead, Guy's and St Thomas' Charity, NHSX. Other; Fresnius-Kabi.

There, across the border – political scientists and their boundary-crossing work

Scholars may be diversely engaged in boundary-crossing work, either staying more confined in academic settings, or reaching out to the policy world. Despite growing theoretical attention, there is little empirical knowledge on the extent to which scholars indeed engage in boundary-crossing activities, and the conditions that foster or jeopardize this. We use original survey data from European political scientists to investigate how frequently they ‘travel’ to the policy world, what patterns of engagement are visible, and how these are determined. The article introduces a typology capturing different boundary-crossing profiles and shows that political scientists are rather frequent travelers across the border between academia and the policy world. Yet, individual characteristics matter. Having (had) a position outside of academia has an especially strong effect on the likelihood of boundary-crossing. Our results also reveal that not every scholar has the same chance to be involved, depending on gender or seniority.

Social and joint attention during shared book reading in young autistic children: a potential marker for social development.

Atypical patterns of social engagement and joint attention behaviors are diagnostic criteria for people with autism spectrum disorder. Experimental tasks using eye-tracking methodologies have, however, shown inconsistent results. The development of tasks with greater ecological validity and relevance for developmentally appropriate social milestones has been identified as important for the field. We developed a novel, dynamic eye-tracking task emulating a shared book reading (SBR) scenario. Four SBR videos of an adult reader engaging with the viewer while reading a children's picture book and including sequenced bids for joint attention were developed. Participants included 90 children (N = 56 autistic children, N = 34 neurotypical children; aged 3-12). Social attention was also measured in a live free play task between participants and an experimenter. Compared to neurotypical children, autistic children displayed reduced attention to socially salient stimuli including the reader's face and picture book across SBR videos and during joint attention bids specifically. In contrast, they showed increased attention to nonsalient background stimuli compared to their neurotypical peers. These attention patterns in autistic children were associated with reduced verbal and nonverbal cognitive skills and increased symptoms associated with autism. Interestingly, positive correlations in the frequency of eye gaze between SBR and free play suggested a potential predictive value for social attention in live social interactions. Findings highlight the utility of SBR eye-tracking tasks in understanding underlying divergences in social engagement and joint attention between autistic and neurotypical children. This commonly practiced early childhood activity may provide insights into the relationship between social engagement and learning to reveal how such attentional patterns might influence broader developmental and educational outcomes.

Evaluation the COVID-19 related posts of the Official Facebook Page of the World Health Organization

Objective: This study aims to analyze the COVID-19-related posts on the official Facebook page of the World Health Organization (WHO). Specifically, we aim to evaluate the engagement metrics such as views, comments, shares, and reactions received by categorized posts. Additionally, we seek to identify the topics that generate the highest levels of engagement in terms of comments and sharing reactions. Material and Methods: This retrospective observational research project was designed to evaluate COVID-19-related posts on the official WHO Facebook Page between March 15, 2020, and March 15 2021. The data was analyzed using IBM SPSS Statistics 25.0. Result: 2021 COVID-19-related posts on the Official Facebook Page of the WHO (n=877) were examined. 23.7% of the shares were about protective equipment, 22.2% were about the pandemic, 12.6% were about personal protective equipment, 11.5% were about social distancing, and 10.3% were media briefings. It was observed that they were about COVID-19??, 9.9% were about syndemic, 6.5% were about vaccine, 1.9% were about medicine, 0.9% were about mutation. An examination of posts related to drugs, vaccines, and media briefings revealed that the most prevalent response was 'like'. Statistical analysis indicated that the number of 'like' reactions was significantly higher for vaccine-related posts compared to non-vaccine related posts. Conversely, the number of angry reactions was lower for vaccine-related posts than for non-vaccine related posts. Conclusion: This study provides valuable insights into the engagement patterns of COVID-19-related posts on the official Facebook page of the World Health Organization (WHO). Our analysis highlights the prevalence of topics such as protective equipment, the pandemic, personal protective equipment, social distancing, and media briefings among the examined posts. Notably, vaccine-related posts garnered a significantly higher number of 'like' reactions compared to non-vaccine related posts, underscoring the importance of vaccine-related communication efforts. These findings emphasize the significance of accurate and informative content dissemination by authoritative institutions like the WHO during global health crises such as the COVID-19 pandemic.

Interaction of initiating prompts and the patterns of student engagement in higher education biology classrooms

Interaction of initiating prompts and the patterns of student engagement in higher education biology classrooms

The Role of Native Speaker Peers on Language Learners’ Fear of Negative Evaluation and Language Anxiety

This study investigated graduate-level international students’ linguistic and learning experiences in classrooms predominated by native English-speaking (NES) students. The central objective was to discern the impact of NES students’ presence on the fear of negative evaluation experienced by international students speaking English as a second language in an American university. A survey was initially administered to 22 graduate-level international students, and five international students, distinguished by heightened levels of anxiety associated with negative evaluation in classrooms primarily dominated by NES students, were interviewed. The interview data were analyzed with domain analysis. The findings elucidated that international students’ fear of negative evaluation depended mainly on instructors’ practices and international students’ perceptions of NES peers’ attitudes. Moreover, the increased fear of negative evaluation adversely impacted their linguistic behaviors and overall engagement with instructional content. The findings implied a need for more inclusive and safe learning environments in graduate schools.

Which storytelling people prefer? Mapping news topic and news engagement in social media

Nations engage in a nuanced competition to achieve soft power through social media on a global scale. Exploring the intricate dynamics, research endeavors focus on unraveling the mechanisms by which national media endeavors not only captivate a diverse international audience but also amplify their influence through carefully crafted narratives. However, extant research falls short in both theoretical and empirical examinations of the intricate interplay between news content and news engagement. This research examines in depth the news content strategy employed by China's national media for global communication, focusing on 6632 YouTube videos of CGTN (China Global Television Network) from May 2021 to August 2023. Specifically, utilizing BERTopic machine learning techniques, we identified 16 distinct news topics and associated with news engagement metrics (e.g., number of views, likes, and comments). Our results reveal a pattern of selective engagement and avoidance by international audiences, particularly in their significant interest in geopolitical and technological topics, while showing less interest in Chinese cultural and social news. This study offers valuable insights into the effectiveness of national media's digital storytelling strategy, shedding light on audience preferences and areas for potential recalibration in content dissemination.

Communicating Europe: a computational analysis of the evolution of the European Commission’s communication on Twitter

Social media is an important means of communication for political agencies, which makes it possible to engage with large sectors of the public. For institutions which are not directly elected by voters, such as the European Commission (EC), social media can be a strategic tool for increasing perceived legitimacy and citizen engagement, especially in contexts of high politicization. In this paper, we use natural language processing techniques to provide a comprehensive overview of how EC communication on Twitter has evolved between 2010 and 2022, with respect to both its topics and its style. Our analyses show that, over time, the focus of EC communication has shifted substantially from economy-, finance- and governance-related topics, towards social policy, digital and environmental policy, and identity. These changes have progressively differentiated the EC’s profile from that of other institutions (especially more technocratic ones) and contributed to better alignment with engagement patterns of its social media audience. In addition, EC communication has become less neutral (in favor of more positive sentiment), simpler, and more readable, all features which are associated with more accessible and engaging messaging. Yet, while the EC currently scores better than most other reference agencies on several descriptors of accessibility, its style is still lexically more complex, less concrete and less action-oriented than that of other institutions. Alongside providing novel insights on how the EC’s online communication and projected political identity have changed over time, this study lays the foundations for future experimental and hypothesis-driven work combining social media data with external data sources.

Audience engagement in outrage factors embedded in COVID‐19 news: A content analysis of South Korean news articles and reader comments

AbstractThe risk perception of lay people is believed to be guided by qualitative characteristics of risk they perceive. Outrage factors refer to perceived characteristics of risk that arouse emotional responses and thereby influence risk perception. Conducting a content analysis of coronavirus disease 2019 online news coverage, we identified outrage factors embedded in news stories to determine how those factors are associated with audience engagement, measured by counting news consumer comments and clicks on emotional expressions on each news story. As a result, the average outrage score for a news story was significantly associated with the number of clicks on emotional expressions and comments. When it comes to individual outrage factors, a news story covering a case of violation against social distancing or other governmental guidelines was found to be accompanied by more comments and more emotional expressions, indicating the strongest power of moral nature. The outrage factor of voluntariness, human origin, accident history and controllability were also associated with one or both types of the audience responses. The news stories of business owners who involuntarily shut down their businesses or those comparing the current pandemic to Middle East respiratory syndrome that broke out in 2015 in the nation showed a similar pattern of audience engagement. Furthermore, liberal media were more likely to convey outrage factors than media with a less liberal orientation, which was manifested in the outrage score for each story. We discuss the potential of outrage factors as a new content‐based factor for audience engagement in online risk communication.

Scrolls and self-perception, navigating the link between social networks and body dissatisfaction in preadolescents and adolescents: a systematic review

In recent years, the widespread adoption of social media has immersed users in content dominated by conventional beauty ideals and the relentless pursuit of perfection. This pervasive influence has significantly altered the perceptual landscape for young individuals, particularly pre-adolescents and adolescents, shaping their self-evaluations and contributing to distorted notions of beauty. The virtual realm, saturated with carefully curated and idealized images promoting unattainable beauty standards, has intensified concerns about body image. This study aims to comprehensively examine the intricate interplay between social media use and the body image of preadolescents and adolescents. Through a meticulous systematic review of 16 studies, a consistent consensus emerges, highlighting a noteworthy correlation between key variables such as the duration of social media usage, problematic engagement patterns, specific activities within these platforms, and heightened levels of body dissatisfaction.

Variations in Pattern of Social Media Engagement between Individuals with Chronic Conditions and Mental Health Conditions

The use of the internet and supported apps is at historically unprecedented levels for the exchange of health information. The increasing use of the internet and social media platforms can affect patients’ health behavior. This study aims to assess the variations in patterns of social media engagement between individuals diagnosed with either chronic diseases or mental health conditions. Data from four iterations of the Health Information National Trends Survey Cycle 4 from 2017 to 2020 were used for this study with a sample size (N) = 16,092. To analyze the association between the independent variables, reflecting the presence of chronic conditions or mental health conditions, and various levels of social media engagement, descriptive statistics and logistic regression were conducted. Respondents who had at least one chronic condition were more likely to join an internet-based support group (Adjusted Odds Ratio or AOR = 1.5; Confidence Interval, CI = 1.11–1.93) and watch a health-related video on YouTube (AOR = 1.2; CI = 1.01–1.36); respondents with a mental condition were less likely to visit and share health information on social media, join an internet-based support group, and watch a health-related video on YouTube. Race, age, and educational level also influence the choice to watch a health-related video on YouTube. Understanding the pattern of engagement with health-related content on social media and how their online behavior differs based on the patient’s medical conditions can lead to the development of more effective and tailored public health interventions that leverage social media platforms.

Recurrent Neural Network Research on a Flipped Classroom Online Collaborative Teaching Platform

A Recurrent Neural Network (RNN) is a type of artificial neural network designed to effectively process sequential data by maintaining a memory of past inputs. A Collaborative Online Teaching platform is a digital space designed to facilitate interactive and engaging learning experiences between educators and students in virtual environments. It integrates various tools and features to enable real-time communication, content sharing, and collaborative activities. The collaborative features such as breakout rooms, group projects, and interactive whiteboards promote teamwork and active participation among students. Assessment tools within the platform facilitate the evaluation of student progress through quizzes, assignments, and grading functionalities. This research investigates the integration of Problem Based Learning (PBL) and cooperative instructional methods within flipped classrooms, aiming to examine their impact on students' intrinsic motivation to learn. The study endeavors to develop a tutorial strategy aligned with the concept of "flipped schools" to enhance students' self-directed study, problem-solving abilities, academic achievement, and interest in instruction. First-year students from Southern Taiwan College of Science and Technology's School of Information Management participated, focusing on education in C#. The participants were divided into a trial group, experiencing the proposed teaching tactics, and an identical control group with randomly selected groups. Pre- and post-test scores from computing classes were analyzed to assess learning outcomes. Additionally, a survey explored students' perceptions of the flipped classroom approach, project-based schooling, and collaborative learning. Recurrent Neural Network (RNN) processes were employed to analyze sequential data, such as student performance over time and engagement patterns. Results indicate that the recommended instructional strategies led to improved comprehension levels, with the experimental group showing significantly greater progress compared to the control.

Examining learners' engagement patterns and knowledge outcome in an experiential learning intervention for youth's social media literacy

Social media has become an integral part of youth's daily lives. Though it brings many benefits such as creative self-expression and opportunities for social connection and support, studies have revealed that exposure to cyberbullying, misinformation and disinformation, or phishing and scams pose great risks to youth's mental health and long-term development. There is no lack of education programs designed to teach youth media literacy, but very few offer experiential learning environments to support youth's development of social media literacy. Youth learners' engagement patterns and learning outcomes in such environments remain unknown. This study seeks to fill in this gap by examining how learners' engagement patterns predict learning outcomes (social media literacy) in a simulated environment that embodies the core components of experiential learning. Two types of data were collected from: 1) n = 150 youth participants in a controlled environment (“data from the classroom”), and 2) n = 3552 participants on the internet (“data in the wild”). The findings revealed learners' engagement patterns (e.g., time spent, completion rate of actions etc.) in different phases of experiential learning, and highlighted the importance of active participation (taking recommended actions instead of passively viewing the course content) in predicting better learning outcomes. This study contributes to understanding the relationship between learners' engagement patterns in experiential learning environments and their knowledge outcomes in social media literacy, and offers practical implications for the improvement of instructional design to enhance experiential learning.

Textual Poachers and Fandom Dynamics: A Study of Filipino College Students

In the realm of contemporary culture, fandom, particularly among the youth, has become a focal point due to its participatory nature, fostering active engagement and creative expression. Coined by Henry Jenkins in 1992, fan culture, or "Textual Poaching," highlights fans' dual roles as consumers and creators within various media landscapes. This research investigates the engagement patterns of Filipino college students within their respective fandoms, employing Jenkins' framework of participatory culture as a theoretical scaffold. Utilizing a qualitative methodology, the study scrutinized 175 student artifacts to elucidate how members of fandoms articulate their affiliations and negotiate communal dynamics. Findings suggest students' decisions to affiliate with particular fandoms depend on their immediate needs, interests, and social standing. Fandoms centered around online gaming, entertainment media, and cinematic or televisual narratives are deemed valuable by students, providing inclusive spaces for social interaction, and constructing personal identity.

Understanding Heterogeneous Patterns of Family Engagement With Educational Technology to Inform School-Family Communication in Linguistically Diverse Communities

We leverage log data from an educational app and two-way text message records from over 3,500 students during the summers of 2019 and 2020 and in-depth interviews in Spanish and English to identify patterns of family engagement with educational technology. Based on the type and timing of technology use, we identify several distinct profiles of engagement, which we group into two categories: independent users who engage with technology-based educational software independently and interaction-supported users who use two-way communications to support their engagement. We also find that as the demands of families from schools increased during the COVID-19 pandemic, Spanish-speaking families were significantly more likely than English-speaking families to engage with educational technology across all categories of families, particularly as interaction-supported users.

Computer-Aided Technology in Product Communication and Promotion Strategy

In today's competitive marketplace, effective in-product communication and promotion strategies are essential for capturing consumer attention and driving product adoption. These strategies involve utilizing various channels within the product itself to convey key messages, showcase features, and encourage user engagement. Computer-aided promotion strategies leverage digital tools and technologies to enhance marketing efforts and reach target audiences more effectively. By utilizing data analytics, automation, and digital advertising platforms, businesses can optimize promotional campaigns, personalize messaging, and target specific demographic segments with precision. This paper proposes an innovative approach to product communication and promotion strategy utilizing computer-aided technology, specifically tailored for the Weibo platform, named the Weibo Stacked Communication Classification Strategy (WeSCCS). The WeSCCS framework integrates advanced computational techniques with Weibo's communication dynamics to classify and categorize promotional content effectively. With machine learning algorithms and natural language processing, the system analyzes user engagement patterns, content preferences, and social interactions to identify optimal communication strategies. WeSCCS classifies user interactions with promotional content such as "positive engagement" (70% of interactions), "neutral engagement" (20%), and "negative engagement" (10%). Additionally, automated content generation and sentiment analysis enable marketers to generate relevant promotional content with a sentiment score of +0.8, indicating high positivity. Real-time monitoring facilitates dynamic adjustments to promotional strategies based on user feedback, ensuring responsiveness to evolving consumer preferences. By integrating computer-aided technology, WeSCCS empowers marketers to maximize promotional impact, increase audience engagement, and drive conversions effectively on Weibo and similar social media platforms.

Hybrid Gamification and AI Tutoring Framework using Machine Learning and Adaptive Neuro-Fuzzy Inference System

Although technology has significantly improved the teaching and learning process, it has not been able to increase students' self-motivation and engagement at the same level. The lack of self-motivation and intermittent engagement is currently one of the primary challenges faced by educators. This new approach to learning called the hybrid gamification framework uses a combination of artificial intelligence (AI), machine learning (ML), and the Adaptive Neuro-Fuzzy Inference System (ANFIS) to create a more engaging and personalized learning experience. By tracking students' interactions and performance, the system can allocate rewards based on their progress, which helps to increase their motivation and engagement. This technology makes it possible for educators to collect and analyse data related to students' engagement patterns, quiz scores, time spent on learning activities, participation in discussion forums, and much more. This data analysis enables educators to identify struggling students and high achievers, allowing them to provide tailored support and instruction to maximize student success. A pilot implementation of this system involving 200 computer science students successfully demonstrated the effectiveness of this technology. This research provides a comprehensive understanding of gamification's impact by combining quantitative data with qualitative insights.

Online polarization and identity politics: An analysis of Facebook discourse on Muslim and LGBTQ+ communities in Finland

AbstractThis study employs a mixed‐methods approach to investigate the nature of Facebook posts related to Muslims and LGBTQ+ individuals in Finland, spanning a period of 4 years. Through the use of the CrowdTangle platform, the researchers extracted and analyzed Facebook posts that encompassed predetermined keywords indicative of potential hate speech. The findings underscored divergent patterns of engagement and sentiment toward these two groups, with implications for the different levels of societal acceptance and tolerance exhibited. Posts related to Muslims typically elicited controversy and were often depicted as threats, whereas posts about the LGBTQ+ community generally advocated for inclusivity. However, persistent negative stereotypes about the LGBTQ+ community were also evident. The analysis also brought to light how political parties strategically used these discourses to steer conversations, consolidate their ideological positions, and mobilize their respective supporters. Grounded in the social identity theory, this study sheds light on the complex dynamics of online political discourse, revealing its far‐reaching impacts on societal attitudes, intergroup relations, and formation of group identities. The nuanced understanding derived from these observations suggests that interventions fostering healthier public discussions on social media platforms could contribute significantly to combating societal division, prejudice, and bias. This research underscores the importance of scrutinizing online discourses to address issues of societal cohesion and social acceptance.