Reddit Users Research Articles

Medical Accuracy of Reddit in Patient Discussions of Prospective Ocular Stem Cell Therapies for Retinal Degenerative Diseases.

Purpose: To evaluate the medical accuracy in discussions of stem cell therapy for retinal diseases on a social media internet forum (Reddit). Methods: A cross-sectional study assessed 11 Reddit forums and 411 unique comments from 2017 to 2022. Evaluated participants were all anonymous Reddit users. Discussions were independently examined for medical accuracy by a board-certified, fellowship-trained retina surgeon in active practice. Results: Reddit users self-identified mainly as prospective patients followed by research students and physicians or healthcare workers. Inquiries mostly regarded stem cell treatment for macular degeneration. Thirty-six percent of the forum's content was found to be inaccurate or misleading. When specifically considering factual comments, the inaccuracy rate increased to 54%. Identified gaps in knowledge mostly regarded the current state of the technology (25%) followed by misinterpretation of trial results (16%) and scam treatments (14%). Most inaccurate or misleading comments favored stem cell advancements (72%). Conclusions: This study highlights the gaps in knowledge and areas of medical misinformation regarding ocular stem cell therapies shared by individuals on Reddit.

‘Angry and heartbroken for the failure of the system’

As the number of wrongful conviction media productions released to the public increases, an understanding of their potential impact on viewers is prudent. One such production, When They See Us, depicts the wrongful conviction of five racialized youth, and we investigated the effect of watching this specific wrongful conviction media production on a subset of Reddit users’ online conversations about wrongful convictions and the criminal justice system in general. Following an inductive content analysis of Reddit comments shared to r/WhenTheySeeUs (N = 461), seven coding categories were observed. The ‘Wrongful Conviction Relevant’ coding category was the third most frequently occurring, representing 28% of total comments. Additionally, after conducting a deeper thematic analysis of the ‘Wrongful Conviction Relevant’ comments, the following themes and subthemes were identified: Risk Factors (Individual Characteristics and System Factors), Exoneration and Beyond (Impacts on Exonerees and Changes to System), and the Innocence Movement (Unmet System Expectations and Public Awareness). Users’ ‘Wrongful Conviction Relevant’ comments were situated within the academic literature investigating wrongful conviction correlates, outcomes, and preventative measures, and discussed in relation to viewer reactions to other wrongful conviction media productions.

Analysis of Telehealth Discussion Trends on Reddit (2019-2022).

Introduction: Use of telehealth increased during the COVID-19 pandemic and continues to be a popular health resource. This study analyzed the frequency and sentiment of telehealth discussions on Reddit. Methods: The data set included 13,071 publicly available Reddit submissions containing keywords related to telehealth over a 3-year period. We identified 173 unique subreddit communities, which were coded into mutually exclusive categories: (1) general telehealth, (2) individual care, (3) professional, (4) news, and (5) COVID-19. The Vader lexicon-based machine was used to assign sentiment scores. Results: Most subreddits were coded as individual care (n = 112), professional (n = 26), and news (n = 22). The frequency of submissions increased during the first 2 months of the pandemic and dropped in June 2020, but remained consistent through October 2022. Most Reddit submissions were positive in sentiment (56%). Conclusion: Findings show a mostly positive view of telehealth among Reddit users and an increase in telehealth-related discussions since the COVID-19 pandemic.

An analysis of keloid patient questions on Reddit.

Reddit is one of the world's leading social media platforms, fostering active community discussions on a variety of topics including keloids. The prevalence and reach of conversations on Reddit underscore the need to investigate and understand patient perspectives and gaps in knowledge. Herein, we present an in-depth analysis of questions and concerns of Reddit users on keloids, offering valuable insights into patient experiences, knowledge gaps and treatment preferences. The study presents a distinct approach by harnessing the power of social media data to understand patient perspectives, which may not be readily apparent in clinical settings. All posts on the 'Hot' page of the subreddit r/Keloids were analyzed. Questions were categorized and subcategorized to reveal common themes. A total of 644 questions from 513 posts between 26 March 2017 and 28 August 2023 were identified and analyzed. Reddit users most frequently asked questions regarding keloid management (57.5%). Other common categories included uncertainty regarding diagnosis or symptoms (15.1%), living with keloids (7.5%) and causes or triggers (6.2%). This analysis highlights critical areas of patient knowledge gaps and potential misconceptions regarding keloids. For dermatologists, understanding these patient questions is crucial. Such insights allow for patient-centric education and treatments, ensuring more effective and comprehensive care.

‘Do not parade your ignorance’: Negation as a power tool of toxic geek masculinity

Catering to a primarily male user population, Reddit is often said to be a breeding ground for toxic language use, which has led to the development of multiple computational classification models for the automated identification of toxicity in online conversations. Since negation can play a key role in cyberbullying, functioning as a popular stance marker in the dialogical exchange of dominating views in the digital sphere, it should not be ignored. Likewise, its use as a rhetorical strategy deserves more linguistic attention. Described as Engagement resources of disclaim in Appraisal theory, negated constructions may be employed by Reddit users to disalign themselves from divergent perspectives through emotion-driven ad hominem attacks, not only resulting in a potentially detrimental impact on the users’ mental health, but also perpetuating toxic power relationships. Therefore, this paper examines the use of negation, in combination with various other interpersonal resources, within the Redditors’ dialogical interactions, to explore its possible motivations and role in shaping the online discourse. A computer-assisted appraisal analysis was conducted on 2637 most upvoted comments and replies (49,866 words), extracted from the subreddit of r/AustralianPolitics between 11–14 April and 7–10 May 2022, during the period leading up to the so-called federal climate change election in Australia. The study shows evidence of a seemingly dominant negation pattern characterised by negative judgement and its findings tentatively suggest that some of the male Australian users may use negation as a discursive strategy driven by ideology and emotion. This raises the possibility that the language use encountered on r/AustralianPolitics may be influenced by covert toxic geek masculinity.

'There is no justice in nursing school': A qualitative analysis of nursing students' experiences of discrimination shared on Reddit.

To explore nursing students' experiences of stigma and discrimination within nursing programmes as shared on Reddit, and how other Reddit users offer support and guidance. Qualitative interpretive description. Through a critical social theory lens, this study draws on students' posts from three nursing subreddits: r/studentnurse, r/nursingstudent and r/nursing. Data were collected from March 2013 to March 2023. Reflexive thematic analysis was conducted to generate broad themes of nursing students' experiences of stigma and discrimination, and how other Reddit users offered support and guidance. A total of 43 posts with 1412 associated comments were included in this analysis, which generated three predominant themes of nursing students' experiences. Nursing students faced stigma and discrimination across contexts, including from peers, nurses and other healthcare providers working in clinical practicum sites, and patients. Nursing students' posts described navigating the impacts and consequences of such experiences, including on well-being, and programme and career success. In contexts where students were often alone in their experiences of stigma and discrimination within their programmes and with few identified supports, Reddit users sought support and community through Reddit. While many comments offered validation and support, challenges of this social media platform included conflicting advice and unhelpful, judgmental messages. Despite widely articulated social justice commitments in the profession, nursing students continue to experience stigma and discrimination across contexts within their nursing programmes. Nurses and nurse educators have a responsibility to acknowledge and make visible such experiences, and take direct action to prevent and remediate stigma and discrimination within nursing education. This research contributes to the growing empirical evidence that nursing students' experience stigma and discrimination within nursing programmes and the healthcare system. Adherence to COREQ guidelines was maintained. No patient or public contribution.

What Do My (Online) Friends Think? A Topic Modeling Approach to Identifying Patterns of Response to Self-Injurious Behaviors on Reddit

Objective Approximately 17% of adolescents and young adults will engage in non-suicidal self-injury (NSSI) at least once in their lifetime, leading the World Health Organization to identify self-injury as one of the top five public health concerns for adolescents. Despite the widespread prevalence of this behavior, NSSI continues to be heavily stigmatized in both medical and community settings, deterring many engaged in NSSI from seeking informal support from friends and family as well as formal psychological or psychiatric treatment. In contrast to the low rates of in-person help-seeking for NSSI, online support groups are highly utilized by those engaged in NSSI. Thus, an empirical study of responses to frequent, voluntary disclosure of NSSI on social media is needed to better understand how these communities meet the needs of those who self-injure. Method The current project used latent Dirichlet allocation to identify frequent and favored themes in response to self-injury content in the largest self-injury group on Reddit (over 100,000 members). Reddit, the 9th most visited website in the world, is a chat-based social media platform that has 430+ million active users and billions of site visits, with current estimates suggesting that ∼63% of the U.S. population are Reddit users. Results Identified themes included: (1) recovery encouragement; (2) provision of social and instrumental support; and (3) daily realities of living with NSSI. Responses that encouraged recovery received more upvotes on Reddit than any other type of comment. Conclusion These results can inform evidence-based, person-centered, dimensional treatments for NSSI. HIGHLIGHTS Responses to NSSI that encourage recovery resonated most with members of the group. Group members provided many social and instrumental NSSI supports to each other. Results provide insight into the real-time needs of those engaged in NSSI.

How Reddit Sentiments Influenced Gamestop Stocks

This paper focuses on the language usage in the GameStop (GME) share rally in January 2021. Specifically, I analyze how comments in a leading post of Reddits r/wallstreetbets subreddit reflect the reddit user communitys sentiment and emotion with various linguistic expressions. I analyzed the topics of the comments using topic modeling with Latent Dirichlet allocation algorithm, which objectively identified representative topics in the comments. Results showed that there were six clusters of topics, including two of the topic clusters that represented words that were highly correlated with stock market behaviors, such as buy and sell, one cluster that suggested objective analyses of the stock market, and one cluster that presented complaining emotions. The findings indicate a direct correlation and impact between the sentiments of comments and the performance of the stock market.

Determining PolyCystic Ovarian Syndrome Severity from Reddit Posts using Topic Modelling and Association Rule Mining

Nowadays social media plays a vital role in various real-time applications, especially in healthcare applications. PolyCystic Ovarian Syndrome (PCOS) is a condition that affects females between the ages of 15 and 35 who are of reproductive potential. The symptoms of PCOS are hormonal issues, irregular periods, weight gain, follicles, infertility, excessive hair growth in the skin, hair loss, acne, pimples, dark scars, and depression. Most of the earlier researchers analyzed the PCOS based on clinical text and health records using a machine learning approach. The main motivation of this proposed work is to predict the upcoming PCOS symptoms based on current symptoms and find the severity of the PCOS from Reddit users. This is done by collecting head symptoms from Gynecologists, gathering present symptoms from Reddit users, collecting unstructured data is pre-processed and PCOS sub symptoms are extracted using Bag of Words. The sub symptoms are mapped into head symptoms using Latent Dirichlet Allocation (LDA) for dimension reduction. The major issue in that approach is a single user has experienced the same type of symptom multiple times. This issue is solved by implementing a novel method called Symptom Segmentation and grouping Labeled Latent Dirichlet Allocation (SSG_LLDA) is designed to reduce the dimensionality and map the social media users sub symptoms into head symptoms. Association Rule Mining (ARM) with Apriori is employed to produce the frequent symptoms, and effective rule sets, and form the distinctive symptom patterns. Among several mini-mum support and confidence metrics, 0.02 and 0.1 delivers the best rule sets and symptom patterns. Based on rulesets of symptom patterns and combinations, the severity of PCOS is determined for Reddit users. The novelty of this work is the construction of PCOS symptom patterns from topic modelling results instead of original data so the dimensionality of the features is reduced and more scalable

"O" no: a Reddit analysis of orgasmic dysfunction.

Female Reddit users frequently discussed potential causes of orgasm difficulties and its implications on mental health and relationships. This study aimed to evaluate the experiences of women discussing orgasms on the Internet site Reddit. We sought to qualitatively analyze the topics that arose in users' discussions to better understand the potential causes of orgasm difficulties and its implications on quality of life. Posts on the subreddit r/TwoXChromosomes containing the keywords "orgasm" and "climax" were included in the dataset. Posts and their associated comments were qualitatively analyzed using the grounded theory approach. Two independent researchers coded each thread to identify dominant themes and emergent concepts. The most frequently coded primary topics included: (1) orgasm (32.2% [n = 337]), (2) psychological (17.8% [n = 186]), (3) relationships (15.4% [n = 161]), and (4) treatment (10.7% [n = 112]). Qualitative analysis of 107 threads and approximately 6300 comments resulted in 5 major categories: psychological aspect of orgasms, difficulty orgasming with partners, partners' responses to orgasmic dysfunction, types of orgasms, and treatments for orgasmic dysfunction. Preliminary themes included (1) the presence of an emotional component or history of trauma related to orgasmic difficulty, (2) difficulty orgasming with a partner regardless of ability to orgasm during masturbation and a variety of stimulation required to orgasm, (3) mixed partner responses to orgasmic dysfunction, (4) the definition of a normal orgasm, and (5) self-motivated treatment for orgasmic dysfunction, including clitoral stimulation devices and masturbation techniques. Notably, few posters discussed their orgasmic dysfunction with healthcare providers. The study reveals insights into the possible causes, psychosocial implications, and treatment of orgasm difficulties from a patient perspective, and can guide future research on female orgasms in a more precise, patient-oriented direction. The anonymous nature of the forum allowed for insight into sensitive topics related to female orgasms and sexual trauma. Limitations include the demographic distribution of Reddit users, which was primarily younger women in their 20s and 30s, which restricts generalizability. Reddit provides a medium for individuals with orgasm difficulties to discuss their experiences. Posts addressed users' inability to orgasm, their mental health and relationships, the stimulation required for orgasm, and treatments for orgasmic dysfunction. Interestingly, very few posts discussed healthcare, potentially suggesting that women do not classify their orgasmic dysfunction as a health issue.

Understanding Mental Health Issues in Different Subdomains of Social Networking Services: Computational Analysis of Text-Based Reddit Posts.

Users increasingly use social networking services (SNSs) to share their feelings and emotions. For those with mental disorders, SNSs can also be used to seek advice on mental health issues. One available SNS is Reddit, in which users can freely discuss such matters on relevant health diagnostic subreddits. In this study, we analyzed the distinctive linguistic characteristics in users' posts on specific mental disorder subreddits (depression, anxiety, bipolar disorder, borderline personality disorder, schizophrenia, autism, and mental health) and further validated their distinctiveness externally by comparing them with posts of subreddits not related to mental illness. We also confirmed that these differences in linguistic formulations can be learned through a machine learning process. Reddit posts uploaded by users were collected for our research. We used various statistical analysis methods in Linguistic Inquiry and Word Count (LIWC) software, including 1-way ANOVA and subsequent post hoc tests, to see sentiment differences in various lexical features within mental health-related subreddits and against unrelated ones. We also applied 3 supervised and unsupervised clustering methods for both cases after extracting textual features from posts on each subreddit using bidirectional encoder representations from transformers (BERT) to ensure that our data set is suitable for further machine learning or deep learning tasks. We collected 3,133,509 posts of 919,722 Reddit users. The results using the data indicated that there are notable linguistic differences among the subreddits, consistent with the findings of prior research. The findings from LIWC analyses revealed that patients with each mental health issue show significantly different lexical and semantic patterns, such as word count or emotion, throughout their online social networking activities, with P<.001 for all cases. Furthermore, distinctive features of each subreddit group were successfully identified through supervised and unsupervised clustering methods, using the BERT embeddings extracted from textual posts. This distinctiveness was reflected in the Davies-Bouldin scores ranging from 0.222 to 0.397 and the silhouette scores ranging from 0.639 to 0.803 in the former case, with scores of 1.638 and 0.729, respectively, in the latter case. By taking a multifaceted approach, analyzing textual posts related to mental health issues using statistical, natural language processing, and machine learning techniques, our approach provides insights into aspects of recent lexical usage and information about the linguistic characteristics of patients with specific mental health issues, which can inform clinicians about patients' mental health in diagnostic terms to aid online intervention. Our findings can further promote research areas involving linguistic analysis and machine learning approaches for patients with mental health issues by identifying and detecting mentally vulnerable groups of people online.

Helicopter parenting through the lens of reddit: A text mining study

The study aimed to understand Reddit users’ experience with helicopter parenting through first-hand accounts. Text mining and natural language processing techniques were employed to extract data from the subreddit r/helicopterparents. A total of 713 original posts were processed from unstructured texts to tidy formats. Latent Dirichlet Allocation (LDA), a popular topic modeling method, was used to discover hidden themes within the corpus. The data revealed common environmental contexts of helicopter parenting (i.e., school, college, work, and home) and its implication on college decisions, privacy, and social relationships. These collectively suggested the importance of autonomy-supportive parenting and mindfulness interventions as viable solutions to the problems posed by helicopter parenting. In addition, findings lent support to past research that has identified more maternal than paternal models of helicopter parenting. Further research on the implications of the COVID-19 pandemic on helicopter parenting is warranted.

The social media discourse of engaged partisans is toxic even when politics are irrelevant.

Prevailing theories of partisan incivility on social media suggest that it derives from disagreement about political issues or from status competition between groups. This study-which analyzes the commenting behavior of Reddit users across diverse cultural contexts (subreddits)-tests the alternative hypothesis that such incivility derives in large part from a selection effect: Toxic people are especially likely to opt into discourse in partisan contexts. First, we examined commenting behavior across over 9,000 unique cultural contexts (subreddits) and confirmed that discourse is indeed more toxic in partisan (e.g. r/progressive, r/conservatives) than in nonpartisan contexts (e.g. r/movies, r/programming). Next, we analyzed hundreds of millions of comments from over 6.3 million users and found robust evidence that: (i) the discourse of people whose behavior is especially toxic in partisan contexts is also especially toxic in nonpartisan contexts (i.e. people are not politics-only toxicity specialists); and (ii) when considering only nonpartisan contexts, the discourse of people who also comment in partisan contexts is more toxic than the discourse of people who do not. These effects were not driven by socialization processes whereby people overgeneralized toxic behavioral norms they had learned in partisan contexts. In contrast to speculation about the need for partisans to engage beyond their echo chambers, toxicity in nonpartisan contexts was higher among people who also comment in both left-wing and right-wing contexts (bilaterally engaged users) than among people who also comment in only left-wing or right-wing contexts (unilaterally engaged users). The discussion considers implications for democratic functioning and theories of polarization.

Unmasking the Mask Issue on Reddit: An Investigation of Online Public Deliberation around the Mask Controversy in the United States

The aim of this investigation was to explore U.S. Americans’ mask attitude and their public deliberation processes on Reddit during the COVID-19 pandemic. With the assistance of “Key Features of Deliberative Conversation and Discussion” variables developed by Gastil and Black, the current project used content analysis method to analyze 524 Reddit posts on mask-wearing issues during two time periods of 2020 (February–March and July). The results showed that Reddit users’ attitude changed over time and there was a significant association between user attitude and most analytical and social deliberation process except for creating information base. The above results were generally supported when different time periods were considered. The present study also revealed that at the time of great uncertainty, users demonstrated a “deficit mode,” which results from expertise knowledge deficits and calls for expert enhancement in information dissemination and problem-solving. Besides the positive correlation between public attitude and policy agenda, there was a general lack of interest in social factors, thus harming the quality of online public deliberation processes. Limitations and implications were discussed.

Behaviour and Bot Analysis on Online Social Networks

The internet is home to a multitude of social networks that provide users with a sense of community and connection across the world. Among these, Twitter and Reddit are two of the most popular. While Twitter users follow and interact with other users (tweets), Reddit users follow and interact with communities known as subreddits. In addition to mainstream social networks, alternative platforms such as Parler exist for users who prefer less moderated online environments. However, there are also malicious users, such as bots and trolls, who exploit social networks for malicious purposes. Therefore, separating malicious behaviors from legitimate ones is crucial. This research aims to evaluate Botometer and RepScope systems to analyze the temporal posting behaviors of Twitter, Reddit, and Parler users and to identify bots, trolls, and malicious behaviors.

Investigating COVID-19's Impact on Mental Health: Trend and Thematic Analysis of Reddit Users' Discourse.

The COVID-19 pandemic has resulted in heightened levels of depression, anxiety, and other mental health issues due to sudden changes in daily life, such as economic stress, social isolation, and educational irregularity. Accurately assessing emotional and behavioral changes in response to the pandemic can be challenging, but it is essential to understand the evolving emotions, themes, and discussions surrounding the impact of COVID-19 on mental health. This study aims to understand the evolving emotions and themes associated with the impact of COVID-19 on mental health support groups (eg, r/Depression and r/Anxiety) on Reddit (Reddit Inc) during the initial phase and after the peak of the pandemic using natural language processing techniques and statistical methods. This study used data from the r/Depression and r/Anxiety Reddit communities, which consisted of posts contributed by 351,409 distinct users over a period spanning from 2019 to 2022. Topic modeling and Word2Vec embedding models were used to identify key terms associated with the targeted themes within the data set. A range of trend and thematic analysis techniques, including time-to-event analysis, heat map analysis, factor analysis, regression analysis, and k-means clustering analysis, were used to analyze the data. The time-to-event analysis revealed that the first 28 days following a major event could be considered a critical window for mental health concerns to become more prominent. The theme trend analysis revealed key themes such as economic stress, social stress, suicide, and substance use, with varying trends and impacts in each community. The factor analysis highlighted pandemic-related stress, economic concerns, and social factors as primary themes during the analyzed period. Regression analysis showed that economic stress consistently demonstrated the strongest association with the suicide theme, whereas the substance theme had a notable association in both data sets. Finally, the k-means clustering analysis showed that in r/Depression, the number of posts related to the "depression, anxiety, and medication" cluster decreased after 2020, whereas the "social relationships and friendship" cluster showed a steady decrease. In r/Anxiety, the "general anxiety and feelings of unease" cluster peaked in April 2020 and remained high, whereas the "physical symptoms of anxiety" cluster showed a slight increase. This study sheds light on the impact of COVID-19 on mental health and the related themes discussed in 2 web-based communities during the pandemic. The results offer valuable insights for developing targeted interventions and policies to support individuals and communities in similar crises.

Mental health at different stages of cancer survival: a natural language processing study of Reddit posts.

The purpose of this study was to use text-based social media content analysis from cancer-specific subreddits to evaluate depression and anxiety-loaded content. Natural language processing, automatic, and lexicon-based methods were employed to perform sentiment analysis and identify depression and anxiety-loaded content. Data was collected from 187 Reddit users who had received a cancer diagnosis, were currently undergoing treatment, or had completed treatment. Participants were split according to survivorship status into short-term, transition, and long-term cancer survivors. A total of 72524 posts were analyzed across the three cancer survivor groups. The results showed that short-term cancer survivors had significantly more depression-loaded posts and more anxiety-loaded words than long-term survivors, with no significant differences relative to the transition period. The topic analysis showed that long-term survivors, more than other stages of survivorship, have resources to share their experiences with suicidal ideation and mental health issues while providing support to their survivor community. The results indicate that Reddit texts seem to be an indicator of when the stressor is active and mental health issues are triggered. This sets the stage for Reddit to become a platform for screening and first-hand intervention delivery. Special attention should be dedicated to short-term survivors.

What Are You Anxious About? Examining Subjects of Anxiety during the COVID-19 Pandemic

COVID-19 poses disproportionate mental health consequences to the public during different phases of the pandemic. We use a computational approach to capture the specific aspects that trigger the public's anxiety about the pandemic and investigate how these aspects change over time. First, we identified nine subjects of anxiety (SOAs) in a sample of Reddit posts (N=86) from r/COVID19\_support using the thematic analysis approach. Then, we quantified Reddit users' anxiety by training algorithms on a manually annotated sample (N=793) to annotate the SOAs in a larger chronological sample (N=6,535). The nine SOAs align with items in various recently developed pandemic anxiety measurement scales. We observed that Reddit users' concerns about health risks remained high in the first eight months since the pandemic started. These concerns diminished dramatically despite the surge of cases occurring later. In general, users' language disclosing the SOAs became less intense as the pandemic progressed. However, worries about mental health and the future steadily increased throughout the period covered in this study. People also tended to use more intense language to describe mental health concerns than health risk or death concerns. Our results suggest that the public's mental health condition does not necessarily improve despite COVID-19 as a health threat gradually weakening due to appropriate countermeasures. Our system lays the groundwork for population health and epidemiology scholars to examine aspects that provoke pandemic anxiety in a timely fashion.

Non-polar Opposites: Analyzing the Relationship between Echo Chambers and Hostile Intergroup Interactions on Reddit

Previous research has documented the existence of both online echo chambers and hostile intergroup interactions. In this paper, we explore the relationship between these two phenomena by studying the activity of 5.97M Reddit users and 421M comments posted over 13 years. We examine whether users who are more engaged in echo chambers are more hostile when they comment on other communities. We then create a typology of relationships between political communities based on whether their users are toxic to each other, whether echo chamber-like engagement with these communities has a polarizing effect, and on the communities' political leanings. We observe both the echo chamber and hostile intergroup interaction phenomena, but neither holds universally across communities. Contrary to popular belief, we find that polarizing and toxic speech is more dominant between communities on the same, rather than opposing, sides of the political spectrum, especially on the left; however, this mostly points to the collective targeting of political outgroups.

DEPRESSION DETECTION MODEL USINGWORD AND SENTENCE EMBEDDING WITH DIFFERENT CLASSIFIERS

Determining depression is hugely important because it affects people's wellness. The common practice of diagnosing depression usually involves a physical session with a practitioner, which reduces the possibility of finding patients who are depressed. Instead, they investigate the possibility of detecting depression solely from textual data based on the posts people post on social media profiles. This research work consists of how Reddit users' text messages can be processed to perform a classification task to identify depression. In order to detect depression, we used Word and Sentence Embeddings. Different embeddings are tested with algorithms such as SVM classifiers, KNN classifiers, and decision tree classifiers, and the results are compared to those from various datasets. Evaluation metrics such as accuracy and precision are used to evaluate the performance of classifiers. USE embedding outperformed Word embeddings with a maximum accuracy of 92%, precision of 95%, recall of 96%, and F1 score of 92%. Results from the suggested method are compared to those from other methods and are found to be satisfactory.