Reddit Comments Research Articles

Application of Sentiment Analysis to Explore the Connection Between Public Sentiment and Stock Price Movement by Python

This study aims to research the relationship between sentiments of public and the trend of the stock. Specially, it focuses on analyzing how positive or negative sentiment correlated to the trend of stocks. In methodology, this research investigates the predictive power of public sentiment on stock market trend by analyzing users comments on Reddit by utilizing Python Reddit API Wrapper (PRAW) to systematically web-scrape relevant financial discussion from a subreddit called stocks. After the data collecting, preprocessing step is conducted to removing noise and standardize text. Eventually, sentiment analysis is applied to analyze the sentiments of comments by using Natural Language Processing (NLP) techniques. This approach allows for an in-depth examination of the correlation between the sentiments expressed in Reddit comments and subsequent stock price fluctuations by comparing the outcome and the actual price movement. Eventually, through the comparison between the sentiment analysis result and actual stocks movement, the sentiment of public comments shares the same trend with the market movement.

Emotion Recognition in Reddit Comments Using Recurrent Neural Networks

Background: Reddit comments are a valuable source of natural language data where emotion plays a key role in human communication. However, emotion recognition is a difficult task that requires understanding the context and sentiment of the texts. In this paper, we aim to compare the effectiveness of four recurrent neural network (RNN) models for classifying the emotions of Reddit comments. Methods: We use a small dataset of 4,922 comments labeled with four emotions: approval, disapproval, love, and annoyance. We also use pre-trained Glove.840B.300d embeddings as the input representation for all models. The models we compare are SimpleRNN, Long ShortTerm Memory (LSTM), bidirectional LSTM, and Gated Recurrent Unit (GRU). We experiment with different text preprocessing steps, such as removing stopwords and applying stemming, removing negation from stopwords, and the effect of setting the embedding layer as trainable on the models. Results: We find that GRU outperforms all other models, achieving an accuracy of 74%. Bidirectional LSTM and LSTM are close behind, while SimpleRNN performs the worst. We observe that the low accuracy is likely due to the presence of sarcasm, irony, and complexity in the texts. We also notice that setting the embedding layer as trainable improves the performance of LSTM but increases the computational cost and training time significantly. We analyze some examples of misclassified texts by GRU and identify the challenges and limitations of the dataset and the models Conclusion: In our study GRU was found to be the best model for emotion classification of Reddit comments among the four RNN models we compared. We also discuss some future directions for research to improve the emotion recognition task on Reddit comments. Furthermore, we provide an extensive discussion of the applications and methods behind each technique in the context of the paper.

A Comparative Analysis of Random Forest, XGBoost, and LightGBM Algorithms for Emotion Classification in Reddit Comments

This research aims to compare the performance of three classification algorithms, namely Random Forest, XGBoost, and LightGBM, in classifying emotions in Reddit comments. Emotion classification in Reddit comments is a complex classification problem due to its numerous variations and ambiguities. This research utilizes the GoEmotions Fine-Grained dataset, filtered down to 7,325 Reddit comments with 5 different basic emotion labels. In this study, data preprocessing steps, feature extraction using CountVectorizer and TF-IDF, and hyperparameter tuning using GridSearchCV for each algorithm are conducted. Subsequently, model evaluation is performed using Cross-Validation and confusion matrix. The results of the study indicate that Random Forest outperforms the XGBoost and LightGBM algorithm with an accuracy of 75.38% compared to XGBoost with 69.05% accuracy and LightGBM with 66.63% accuracy.

Mapping Great Britain's semantic footprints through a large language model analysis of Reddit comments

Observed regional variation in geotagged social media text is often attributed to dialects, where features in language are assumed to exhibit region-specific properties. While dialects are seen as a key component in defining the identity of regions, there are a multitude of other geographic properties that may be captured within natural language text. In our work, we consider locational mentions that are directly embedded within comments on the social media website Reddit, providing a range of associated semantic information, and enabling deeper representations between locations to be captured. Using a large corpus of geoparsed Reddit comments from UK-related local discussion subreddits, we first extract embedded semantic information using a large language model, aggregated into local authority districts, representing the semantic footprint of these regions. These footprints broadly exhibit spatial autocorrelation, with clusters that conform with the national borders of Wales and Scotland. London, Wales, and Scotland also demonstrate notably different semantic footprints compared with the rest of Great Britain.

Exploring equity perception of electric vehicles from a social media perspective

Electric vehicles (EVs) are considered a promising solution for climate change. However, their introduction has raised uncertainties in transportation equity, reflecting the diverse public perceptions and discussions on this issue. Understanding these perceptions is crucial for integrating equity into EV adoption effectively. To bridge this gap, our study used a social media approach to conduct a cross-platform equity analysis on two popular online social networks (OSNs): Twitter and Reddit. We identified 22,533 tweets and 12,318 Reddit comments relevant to equity from all EV-related discussions from 2011 to 2022. Our analysis utilized different features of the two OSNs, including verified account identity and Reddit communities, to present a comprehensive picture of equity perception in social media. Additionally, we used machine learning methods to infer Twitter users’ demographic attributes and integrate user demographics into equity analysis. Our findings show that political accounts are the center of attention on Twitter, but they are accompanied by negative attitudes. Many fringe communities are highly active in equity discussions on Reddit. Our study also found that younger people pay less attention and hold more negative sentiments towards equity discussions, while male and female users have similar interests in equity and similar sentiment. Notably, the proportion of equity discussions remains low in the overall EV corpus, despite a significant increase in recent years.

Impact of COVID-19 Pandemic on Social Determinants of Health Issues of Marginalized Black and Asian Communities: A Social Media Analysis Empowered by Natural Language Processing.

This study aims to understand the impact of the COVID-19 pandemic on social determinants of health (SDOH) of marginalized racial/ethnic US population groups, specifically African Americans and Asians, by leveraging natural language processing (NLP) and machine learning (ML) techniques on race-related spatiotemporal social media text data. Specifically, this study establishes the extent to which Latent Dirichlet Allocation (LDA) and Gibbs Sampling Dirichlet Multinomial Mixture (GSDMM)-based topic modeling determines social determinants of health (SDOH) categories, and how adequately custom named-entity recognition (NER) detects key SDOH factors from a race/ethnicity-related Reddit data corpus. In this study, we collected race/ethnicity-specific data from 5 location subreddits including New York City, NY; Los Angeles, CA; Chicago, IL; Philadelphia, PA; and Houston, TX from March to December 2019 (before COVID-19 pandemic) and from March to December 2020 (during COVID-19 pandemic). Next, we applied methods from natural language processing and machine learning to analyze SDOH issues from extracted Reddit comments and conversation threads using feature engineering, topic modeling, and custom named-entity recognition (NER). Topic modeling identified 35 SDOH-related topics. The SDOH-based custom NER analyses revealed that the COVID-19 pandemic significantly impacted SDOH issues of marginalized Black and Asian communities. On average, the Social and Community Context (SCC) category of SDOH had the highest percent increase (366%) from the pre-pandemic period to the pandemic period across all locations and population groups. Some of the detected SCC issues were racism, protests, arrests, immigration, police brutality, hate crime, white supremacy, and discrimination. Reddit social media platform can be an alternative source to assess the SDOH issues of marginalized Black and Asian communities during the COVID-19 pandemic. By employing NLP/ML techniques such as LDA/GSDMM-based topic modeling and custom NER on a race/ethnicity-specific Reddit corpus, we uncovered various SDOH issues affecting marginalized Black and Asian communities that were significantly worsened during the COVID-19 pandemic. As a result of conducting this research, we recommend that researchers, healthcare providers, and governments utilize social media and collaboratively formulate responses and policies that will address SDOH issues during public health crises.

Understanding the Unintended Effects of Human-Machine Moderation in Addressing Harassment within Online Communities

ABSTRACT We set out to explore the unintended effects of human-machine moderation in mitigating harassment within online communities. We examine communities that use a block-list type bot to prevent harassment from the source of harassment. Drawing from social categorization and selective exposure theories, we theorize that employing a machine alongside humans for community moderation will create unintended adverse effects. Specifically, within the moderated focal community, we hypothesize an emboldening effect characterized by an increase in harassment among community members directed at their outgroup members. Additionally, we expect a disengaging effect, that is, a downward trend in the focal community’s membership. Finally, in neighboring communities that share the same topic of discussion, we expect a spillover effect, that is, an increase in harassment. Employing Detoxify, a Bidirectional Encoder Representations from Transformers (BERT)-based model, we evaluate harassment scores in the focal community by analyzing 4 million Reddit comments across various communities. These scores serve as inputs for Bayesian Structural Time Series analysis, revealing evidence for both disengaging and spillover effects. For the emboldening effect, we use community-specific keywords in a predefined computer-assisted document classification approach, Keyword Assisted Topic Model (keyATM), to identify the target of harassment. We use mean comparison and regression discontinuity to assess the change in the level of harassment targeting outgroup members before and after the human-machine moderation implementation.

‘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.

Deconstructing cultural appropriation in online communities: A multilayer network analysis approach

In this study, we introduce a novel multilayer network model designed to analyze complex social phenomena in online communities. The model captures intricate relationships between users, content, and specific aspects of social phenomena, providing a comprehensive framework for understanding these interactions. We applied this model to a dataset of over 1 million Reddit comments from January to April 2022, filtered for cultural appropriation-related keywords. Our quantitative analyses, based on Social Network Analysis techniques, revealed significant findings. For instance, a subreddit exhibited the highest user interaction, indicating a substantial level of engagement on this topic. Furthermore, the distribution of key contents across different subreddits was non-uniform, suggesting diverse levels of engagement across communities. The results of this research underscore the potential of our approach in providing a nuanced understanding of social phenomena in online communities, thereby contributing to future research in this field.

Mapping cognitive place associations within the United Kingdom through online discussion on Reddit

AbstractThis paper explores cognitive place associations; conceptualised as a place‐based mental model that derives subconscious links between geographic locations. Utilising a large corpus of online discussion data from the social media website Reddit, we experiment on the extraction of such geographic knowledge from unstructured text. First we construct a system to identify place names found in Reddit comments, disambiguating each to a set of coordinates where possible. Following this, we build a collective picture of cognitive place associations in the United Kingdom, linking locations that co‐occur in user comments and evaluating the effect of distance on the strength of these associations. Exploring these geographies nationally, associations were shown to be typically weaker over greater distances. This distance decay is also highly regional, rural areas typically have greater levels of distance decay, particularly in Wales and Scotland. When comparing major cities across the UK, we observe distinct distance decay patterns, influenced primarily by proximity to other cities.

Navigating pathways to automated personality prediction: a comparative study of small and medium language models.

Recent advancements in Natural Language Processing (NLP) and widely available social media data have made it possible to predict human personalities in various computational applications. In this context, pre-trained Large Language Models (LLMs) have gained recognition for their exceptional performance in NLP benchmarks. However, these models require substantial computational resources, escalating their carbon and water footprint. Consequently, a shift toward more computationally efficient smaller models is observed. This study compares a small model ALBERT (11.8M parameters) with a larger model, RoBERTa (125M parameters) in predicting big five personality traits. It utilizes the PANDORA dataset comprising Reddit comments, processing them on a Tesla P100-PCIE-16GB GPU. The study customized both models to support multi-output regression and added two linear layers for fine-grained regression analysis. Results are evaluated on Mean Squared Error (MSE) and Root Mean Squared Error (RMSE), considering the computational resources consumed during training. While ALBERT consumed lower levels of system memory with lower heat emission, it took higher computation time compared to RoBERTa. The study produced comparable levels of MSE, RMSE, and training loss reduction. This highlights the influence of training data quality on the model's performance, outweighing the significance of model size. Theoretical and practical implications are also discussed.

IS IT (MICRO)CHEATING? HOW SOCIAL MEDIA CONFOUND ASSUMPTIONS IN ROMANTIC RELATIONSHIPS

Social media have changed the ways we communicate, meet others, and form intimate relationships. However, technology can also mediate intimate partner surveillance and abuse (Muise, 2009; Tokunaga, 2010). One of the most explicit ways to understand these shifts is through the transgressing of relationship boundaries, defined and enforced by settler-colonial notions of compulsory monogamy (TallBear, 2020). Anxieties around cheating have evolved along with our technologies, as evidenced by ambiguous new terms like “microcheating” and “emotional cheating” (Lusinski, 2018). In this in-progress, mixed-methods study, we examine new definitions of cheating through analyzing discussions about potential transgressions on Reddit. Specifically, we investigate 1) which behaviors cause uncertainty in emerging forms of social media-enabled infidelity and 2) the degree to which relationship discourse online naturalizes the extension of compulsory monogamy into online space. For our pilot analysis, we used computational techniques to elicit common subjects within subreddit posts to then analyze qualitatively. We began with Latent Dirichlet Allocation (LDA), an unsupervised natural language processing tool, to organize Reddit posts and comments by topic (Blei, 2012). Then we qualitatively analyzed Reddit discourse by using critical discourse analysis. Our pilot analysis suggested a belief that proof of (in)fidelity can be found on a partner’s smartphone, such as by reading texts. This orientation toward evidence then justifies surveillance and hacking of a partner’s phone and computer presence, construing the invasion of privacy as the right to truth. This preliminary finding suggests that discourse around transgressive behaviors on social media likely reiterates compulsory monogamy and settler sexuality.

Communication work about chronic pain: A mixed methods application and extension of the integrative theory of communication work

ABSTRACT Guided by the integrative theory of communication work, we present a computational and qualitative analysis of Reddit comments authored by patient-users living with chronic overlapping pain conditions. We first employed computational text analysis to compress the data corpus via structural topic modeling. The community detection algorithm Louvain was employed to identify communities of topics within the data. Then, we extracted a subset of the data and performed a computer-aided qualitative thematic co-occurrence analysis. Topic modeling revealed 35 topics and five thematic communities. The qualitative analysis revealed that these topics mapped onto and extended the typology of communication work and contributing factors and identified co-occurrence patterns both among and across research questions. Our findings offer methodological, theoretical, and practical implications.

Characterizing Gender-Affirming Voice Intervention Discussion on Social Media: A Cross-Platform Analysis

Gender-affirming voice treatments, such as voice training and surgery, are highly impactful for transgender patients experiencing vocal dysphoria and may be discussed on social media platforms including Twitter and Reddit. Our goal was to characterize the content and sentiment of social media posts pertaining to gender-affirming voice interventions to better understand the needs of this patient population. Retrospective data-mining study. A total of 18,695 Tweets from 2001 to 2021 and 23,742 r/Transvoice Reddit submissions and comments from 2009 to 2020 were extracted via publicly available application programming interfaces and analyzed using language processing and sentiment analysis techniques. One thousand eighty-six highly emotive r/Transvoice posts related to voice modification treatments were manually reviewed for further classification. Online discussion of gender-affirming voice has increased over time and is centered on vocal feminization. Recurrent themes included use of online training resources, singing voice, and barriers to care such as cost and variable experiences with health care providers. Sentiment analysis demonstrated that posts discussing gender-affirming voice training had higher average sentiment scores than those discussing voice surgery, on both Twitter (0.252 vs 0.161; P<0.001) and Reddit (0.349 vs 0.301; P<0.001). Frequently appearing themes in highly negative surgery posts included mixed outcomes (9.3%), surgical complications (9.3%), and recovery time (8.5%). Common themes shared by the positive subgroup analysis included peer support, vocal quality, and importance of practice. Gender-diverse patients share various concerns and resources relating to voice intervention in the online communities of Twitter and Reddit. The discussion has been growing over the past decade and is mostly positive, with significant social support and resource-sharing within the community. Aggregated online sentiment toward gender-affirming voice surgery is more negative than voice training, largely due to concerns about surgical outcomes and variability, risks, and recovery period.

Analyzing Social Media Data Using Sentiment Mining and Bigram Analysis for the Recommendation of YouTube Videos

In this work we combine sentiment analysis with graph theory to analyze user posts, likes/dislikes on a variety of social media to provide recommendations for YouTube videos. We focus on the topic of climate change/global warming, which has caused much alarm and controversy over recent years. Our intention is to recommend informative YouTube videos to those seeking a balanced viewpoint of this area and the key arguments/issues. To this end we analyze Twitter data; Reddit comments and posts; user comments, view statistics and likes/dislikes of YouTube videos. The combination of sentiment analysis with raw statistics and linking users with their posts gives deeper insights into their needs and quest for quality information. Sentiment analysis provides the insights into user likes and dislikes, graph theory provides the linkage patterns and relationships between users, posts, and sentiment.

Reddit and Google Activity Related to Non-COVID Epidemic Diseases Surged at Start of COVID-19 Pandemic: Retrospective Study.

Resources such as Google Trends and Reddit provide opportunities to gauge real-time popular interest in public health issues. Despite the potential for these publicly available and free resources to help optimize public health campaigns, utilization for this purpose has been limited. To determine whether early public awareness of COVID-19 correlated with elevated public interest in other infectious diseases of public health importance. Google Trends search data and Reddit comment data were analyzed from 2018 through 2020 for the frequency of keywords "Chikungunya", "Ebola", "H1N1", "MERS", "SARS", and "Zika", six highly publicized epidemic diseases in recent decades. After collecting Google Trends relative popularity scores for each of these six terms, unpaired T-tests were used to compare the 2020 weekly scores for each term to its average level over the 3-year study period. The number of Reddit comments per month with each of these six terms was collected and then adjusted for total estimated Reddit monthly comment volume to derive a measure of relative usage, analogous to the Google Trends popularity score. The relative monthly incidence of comments with each search term was then compared to the corresponding search term's pre-COVID monthly comment data, again using unpaired T-tests. P-value cutoffs for statistical significance were determined a priori with a Bonferroni correction. Google Trends and Reddit data both demonstrate large and statistically significant increases in the usage of each evaluated disease term through at least the initial months of the pandemic. Google searches and Reddit comments that included any of the evaluated infectious disease search terms rose significantly in the first months of 2020 above their baseline usage, all peaking in March 2020. Google searches for "SARS" and "MERS" remained elevated for the entirety of the 2020 calendar year, as did Reddit comments with the words "Ebola", "H1N1", "MERS" and "SARS" (P<.001, for each weekly or monthly comparison, respectively). Google Trends and Reddit can readily be used to evaluate real-time general interest levels in public health related topics, providing a tool to better time and direct public health initiatives that require a receptive target audience. The start of the COVID-19 pandemic correlated with increased public interest in other epidemic infectious diseases. We have demonstrated that for six distinct infectious causes of epidemics over the last two decades, public interest rose substantially and rapidly with the outbreak of COVID-19. Our data suggests that for at least several months after the initial outbreak, the public may have been particularly receptive to dialogue on these topics. Public health officials should consider utilizing Google Trends and social media data to identify to patterns of engagement with public health topics in real-time, and to optimize timing of public health campaigns.

'It is Really Interesting how that Small Robot Impacts Humans.' The Exploratory Analysis of Human Attitudes Toward the Social Robot Vector in Reddit and Youtube Comments"

We present the results of an exploratory analysis of human attitudes toward the social robot Vector. The study was conducted on the natural language data (2,635 comments) retrieved from Reddit and YouTube. We describe the tag-set used and the (manual) annotation procedure. We present and compare attitudes structure revealed from Reddit and YouTube data. Two main findings are described and discussed: almost 20% of comments from both Reddit and YouTube consist of various manifestations of attitudes toward Vector (mainly attribution of autonomy and declaration of feelings toward Vector); Reddit and YouTube comments differ when it comes to revealed attitudes structure -- the data source matters for attitudes studies.

The role of the big geographic sort in online news circulation among U.S. Reddit users

Past research has attributed the circulation of online news to two main factors—individual characteristics (e.g., a person’s information literacy) and social media effects (e.g., algorithm-mediated information diffusion)—and has overlooked a third one: the critical mass created by the offline self-segregation of Americans into like-minded geographical regions such as states (a phenomenon called ‘The Big Sort’). We hypothesized that this latter factor matters for the online spreading of news not least because online interactions, despite having the potential of being global, end up being localized: interaction probability is known to rapidly decay with distance. Upon analysis of more than 8M Reddit comments containing news links spanning four years, from January 2016 to December 2019, we found that Reddit did not work as an ‘hype machine’ for news (as opposed to what previous work reported for other platforms, circulation was not mainly caused by platform-facilitated network effects). Rather, news circulation in Reddit worked as a supply-and-demand system: news items scaled linearly with the number of users in each state (with a scaling exponent beta approx 1, and a goodness of fit R^2approx 0.95). Furthermore, deviations from such a universal pattern were best explained by state-level personality and cultural factors (R^2approx {0.12, 0.39}), rather than socioeconomic conditions (R^2approx {0.15, 0.29}) or political characteristics (R^2approx {0.06, 0.21}). Higher-than-expected circulation of any type of news was found in states characterised by residents who tend to be less diligent in terms of their personality (low in conscientiousness) and by loose cultures understating the importance of adherence to norms (low in cultural tightness). Interestingly, the combination of those factors with low levels of education was then associated with the circulation of a particular type of news, that is, misinformation. These results suggest that online interactions are geographically bounded and, as such, news circulation cannot be studied purely as an Internet phenomenon but should be grounded into a user’s offline cultural environment, which has become increasingly segregated over the decades, and is admittedly hard to change.

Disruptions in the Cystic Fibrosis Community's Experiences and Concerns During the COVID-19 Pandemic: Topic Modeling and Time Series Analysis of Reddit Comments.

The COVID-19 pandemic disrupted the needs and concerns of the cystic fibrosis community. Patients with cystic fibrosis were particularly vulnerable during the pandemic due to overlapping symptoms in addition to the challenges patients with rare diseases face, such as the need for constant medical aid and limited information regarding their disease or treatments. Even before the pandemic, patients vocalized these concerns on social media platforms like Reddit and formed communities and networks to share insight and information. This data can be used as a quick and efficient source of information about the experiences and concerns of patients with cystic fibrosis in contrast to traditional survey- or clinical-based methods. This study applies topic modeling and time series analysis to identify the disruption caused by the COVID-19 pandemic and its impact on the cystic fibrosis community's experiences and concerns. This study illustrates the utility of social media data in gaining insight into the experiences and concerns of patients with rare diseases. We collected comments from the subreddit r/CysticFibrosis to represent the experiences and concerns of the cystic fibrosis community. The comments were preprocessed before being used to train the BERTopic model to assign each comment to a topic. The number of comments and active users for each data set was aggregated monthly per topic and then fitted with an autoregressive integrated moving average (ARIMA) model to study the trends in activity. To verify the disruption in trends during the COVID-19 pandemic, we assigned a dummy variable in the model where a value of "1" was assigned to months in 2020 and "0" otherwise and tested for its statistical significance. A total of 120,738 comments from 5827 users were collected from March 24, 2011, until August 31, 2022. We found 22 topics representing the cystic fibrosis community's experiences and concerns. Our time series analysis showed that for 9 topics, the COVID-19 pandemic was a statistically significant event that disrupted the trends in user activity. Of the 9 topics, only 1 showed significantly increased activity during this period, while the other 8 showed decreased activity. This mixture of increased and decreased activity for these topics indicates a shift in attention or focus on discussion topics during this period. There was a disruption in the experiences and concerns the cystic fibrosis community faced during the COVID-19 pandemic. By studying social media data, we were able to quickly and efficiently study the impact on the lived experiences and daily struggles of patients with cystic fibrosis. This study shows how social media data can be used as an alternative source of information to gain insight into the needs of patients with rare diseases and how external factors disrupt them.

Public discourse and sentiment during Mpox outbreak: an analysis using natural language processing

ObjectivesMpox has been declared a Public Health Emergency of International Concern by the World Health Organization on July 23, 2022. Since early May 2022, Mpox has been continuously reported in several endemic countries with alarming death rates. This led to several discussions and deliberations on the Mpox virus among the general public through social media and platforms such as health forums. This study proposes natural language processing techniques such as topic modeling to unearth the general public's perspectives and sentiments on growing Mpox cases worldwide. Study designThis was a detailed qualitative study using natural language processing on the user-generated comments from social media. MethodsA detailed analysis using topic modeling and sentiment analysis on Reddit comments (n = 289,073) that were posted between June 1 and August 5, 2022, was conducted. While the topic modeling was used to infer major themes related to the health emergency and user concerns, the sentiment analysis was conducted to see how the general public responded to different aspects of the outbreak. ResultsThe results revealed several interesting and useful themes, such as Mpox symptoms, Mpox transmission, international travel, government interventions, and homophobia from the user-generated contents. The results further confirm that there are many stigmas and fear of the unknown nature of the Mpox virus, which is prevalent in almost all topics and themes unearthed. ConclusionsAnalyzing public discourse and sentiments toward health emergencies and disease outbreaks is highly important. The insights that could be leveraged from the user-generated comments from public forums such as social media may be important for community health intervention programs and infodemiology researchers. The findings from this study effectively analyzed the public perceptions that may enable quantifying the effectiveness of measures imposed by governmental administrations. The themes unearthed may also benefit health policy researchers and decision-makers to make informed and data-driven decisions.