Reddit Data Research Articles

Exploring Large Language Models for Detecting Online Vaccine Reactions.

Social media offers a rich source of real-time health data, including potential vaccine reactions. However, extracting meaningful insights is challenging due to the noisy nature of social media content. This paper explores using large language models (LLMs) and prompt engineering to detect personal mentions of vaccine reactions. Different prompting strategies were evaluated on two LLM models (GPT-3.5 and GPT-4) using Reddit data focused on shingles (zoster) vaccines. Zero-shot and few-shot learning approaches with both standard and chain-of-thought prompts were compared. The findings demonstrate that GPT-based models with carefully crafted chain-of-thought prompts could identify the relevant social media posts. Few-shot learning helped GPT4 models to identify more of the marginal cases, although less precisely. The use of LLMs for classification with lightweight supervised pretrained language models (PLMs) found that PLMs outperform LLMs. However, a potential benefit in using LLMs to help identify records for training PLMs was revealed, especially to eliminate false negatives, and LLMs could be used as classifiers when insufficient data exists to train a PLM.

Content analysis of conversations on Reddit: reactions to FDA’s ENDS prioritized enforcement policy

IntroductionOn January 2, 2020, the FDA announced a policy focused in part on prioritizing enforcement of flavored (other than tobacco- or menthol-flavored) cartridge-based electronic nicotine delivery systems (ENDS) without premarket authorization.MethodsWe used a query to identify Reddit conversations relevant to the policy from January 2 to May 6, 2020. Our sample included 576 posts (46 posts and 530 accompanying comments). Two analysts coded posts for mentions of use behaviors (e.g., switching, quitting), purchasing behaviors (e.g., purchasing from retailer new to the user), and flavored products. We summarized frequencies of coded data and provided illustrative quotes.ResultsOnly 21.0% (121/576) of posts mentioned use behavior. Switching behavior was the most common use behavior mentioned (50.4%, 61/121). Most switching behavior posts focused on ENDS-related switching (91.8%, 56/61). The most common ENDS-related switching behaviors mentioned were switching to an open tank (45.9%, 28/61) or device with refillable pods/cartridges (44.3%, 27/61); 8.2% (5/61) mentioned switching to disposables. Just 15.5% (89/576) of posts mentioned purchasing behavior, with the most common being purchasing from a retailer new to the user (32.6%, 29/89). Only 6.8% (39/576) of posts mentioned specific flavors.ConclusionReddit posts about the policy commonly discussed switching to non-cartridge-based ENDS products, such as open tank systems or disposable devices, and purchasing products from different online sources that were still selling these products. Findings suggest that publicly available Reddit data can complement data from traditional sources (e.g., surveys, sales) to understand potential unintended consequences associated with policies by exploring the public’s reactions.

The Use of Natural Language Processing Methods in Reddit to Investigate Opioid Use: Scoping Review.

The growing availability of big data spontaneously generated by social media platforms allows us to leverage natural language processing (NLP) methods as valuable tools to understand the opioid crisis. We aimed to understand how NLP has been applied to Reddit (Reddit Inc) data to study opioid use. We systematically searched for peer-reviewed studies and conference abstracts in PubMed, Scopus, PsycINFO, ACL Anthology, IEEE Xplore, and Association for Computing Machinery data repositories up to July 19, 2022. Inclusion criteria were studies investigating opioid use, using NLP techniques to analyze the textual corpora, and using Reddit as the social media data source. We were specifically interested in mapping studies' overarching goals and findings, methodologies and software used, and main limitations. In total, 30 studies were included, which were classified into 4 nonmutually exclusive overarching goal categories: methodological (n=6, 20% studies), infodemiology (n=22, 73% studies), infoveillance (n=7, 23% studies), and pharmacovigilance (n=3, 10% studies). NLP methods were used to identify content relevant to opioid use among vast quantities of textual data, to establish potential relationships between opioid use patterns or profiles and contextual factors or comorbidities, and to anticipate individuals' transitions between different opioid-related subreddits, likely revealing progression through opioid use stages. Most studies used an embedding technique (12/30, 40%), prediction or classification approach (12/30, 40%), topic modeling (9/30, 30%), and sentiment analysis (6/30, 20%). The most frequently used programming languages were Python (20/30, 67%) and R (2/30, 7%). Among the studies that reported limitations (20/30, 67%), the most cited was the uncertainty regarding whether redditors participating in these forums were representative of people who use opioids (8/20, 40%). The papers were very recent (28/30, 93%), from 2019 to 2022, with authors from a range of disciplines. This scoping review identified a wide variety of NLP techniques and applications used to support surveillance and social media interventions addressing the opioid crisis. Despite the clear potential of these methods to enable the identification of opioid-relevant content in Reddit and its analysis, there are limits to the degree of interpretive meaning that they can provide. Moreover, we identified the need for standardized ethical guidelines to govern the use of Reddit data to safeguard the anonymity and privacy of people using these forums.

WP6.6 - Sentiment Analysis of Social Media Posts by Medical Professional in the UK

Abstract Aims Notoriously described as being controversial in nature, we aim to objectively assess the language used amongst medical professionals in social media (SM). Methods Data was extracted from the Reddit SM website under the sub-page "r/DoctorsUK" forum, through a data-extraction code written in the python language. This was then analysed through a Natural Language Toolkit algorithm to classify language used into positive, negative and neutral sentiments with an overall polarity score ranging from -1 noting most negative to 1 being most positive. Results A total of 992 articles were examined over a 24-day period, as limited by Reddit data application programme interface (API) protocols. A total of 210 positive, 218 negative and 564 neutral articles were noted with an average of 43 articles posted per day. Headlines sentiment on average was overall marginally positive at 0.0154. Comment tree within each headline was also examined, noting a more positive 0.173. Through regression analysis, it was noted that headline and comment sentiments was directly correlated, and this was statistically significant(P=1.24*10-10). It was also noted that more negative headlines had higher “likes” and were therefore featured more prominently within the forum. Conclusions Although SM posts are overall positive in sentiment, it is noted that more negative sentiments are often up-voted and therefore featured more prominently due to their more provocative nature and emotions incited amongst readers. As SM posts elicits strong psychological and psychosocial reactions from a community, there is an urgency to more accurate data driven models in developing future policies to maintain an effective professional morale within the NHS.

Superman, superwoman, or superhero? A thematic analysis of Reddit user discussions of female superheroes

ABSTRACT The superhero film genre has both hypersexualized women and offered potentially empowering social and cultural narratives. Drawing on feminist media studies and critical discourse analysis, this study analyzes themes in online Reddit discussions surrounding female superheroes in Black Widow to better understand how conversations about media representations maintain and subvert dominant gendered discourses. Throughout her portrayal in multiple movies in the Marvel Cinematic Universe, Black Widow’s transformation highlights the shift from a hypersexualized female superhero to a complex character that breaks away from gendered stereotypes. Mirroring an incrementalist approach to social change, Redditors discursively constructed and invoked “realism” as a heuristic for their evaluations of diversified media representations. First, Reddit users centered White, Western women’s experiences in their posts about the film, confronting the trope that women exist as objects to be saved and sexualized by men. However, as they assumed that White, Western women’s struggles are universal, they ignored the intersectional relationship to oppression. Second, Redditors engaged in the male gaze to understand, perpetuate, and disrupt Black Widow’s historic sexualization. The study supports the claims that gender is performative and seemingly mundane conversations can function politically. We conclude with implications for theorizing future post-feminist discourse and methodological implications for researchers using Reddit data.

Intent aware data augmentation by leveraging generative AI for stress detection in social media texts

Stress is a major issue in modern society. Researchers focus on identifying stress in individuals, linking language with mental health, and often utilizing social media posts. However, stress classification systems encounter data scarcity issues, necessitating data augmentation. Approaches like Back-Translation (BT), Easy Data Augmentation (EDA), and An Easier Data Augmentation (AEDA) are common. But, recent studies show the potential of generative AI, notably ChatGPT. This article centers on stress identification using the DREADDIT dataset and A Robustly Optimized BERT Pretraining Approach (RoBERTa) transformer, emphasizing the use of generative AI for augmentation. We propose two ChatGPT prompting techniques: same-intent and opposite-intent 1-shot intent-aware data augmentation. Same-intent prompts yield posts with similar topics and sentiments, while opposite-intent prompts produce posts with contrasting sentiments. Results show a 2% and 3% performance increase for opposing and same sentiments, respectively. This study pioneers intent-based data augmentation for stress detection and explores advanced mental health text classification methods with generative AI. It concludes that data augmentation has limited benefits and highlights the importance of diverse Reddit data and further research in this field.

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.

Paving the way for COVID survivors' psychosocial rehabilitation: Mining topics, sentiments, and their trajectories over time from Reddit.

Objective: This study examined major themes and sentiments and their trajectories and interactions over time using subcategories of Reddit data. The aim was to facilitate decision-making for psychosocial rehabilitation. Materials and Methods: We utilized natural language processing techniques, including topic modeling and sentiment analysis, on a dataset consisting of more than 38,000 topics, comments, and posts collected from a subreddit dedicated to the experiences of people who tested positive for COVID-19. In this longitudinal exploratory analysis, we studied the dynamics between the most dominant topics and subjects' emotional states over an 18-month period. Results: Our findings highlight the evolution of the textual and sentimental status of major topics discussed by COVID survivors over an extended period of time during the pandemic. We particularly studied pre- and post-vaccination eras as a turning point in the timeline of the pandemic. The results show that not only does the relevance of topics change over time, but the emotions attached to them also vary. Major social events, such as the administration of vaccines or enforcement of nationwide policies, are also reflected through the discussions and inquiries of social media users. In particular, the emotional state (i.e., sentiments and polarity of their feelings) of those who have experienced COVID personally. Discussion: Cumulative societal knowledge regarding the COVID-19 pandemic impacts the patterns with which people discuss their experiences, concerns, and opinions. The subjects' emotional state with respect to different topics was also impacted by extraneous factors and events, such as vaccination. Conclusion: By mining major topics, sentiments, and trajectories demonstrated in COVID-19 survivors' interactions on Reddit, this study contributes to the emerging body of scholarship on COVID-19 survivors' mental health outcomes, providing insights into the design of mental health support and rehabilitation services for COVID-19 survivors.

Codeine and promethazine: Exploratory study on "lean" or "sizzurp"using national survey data and an online forum.

The concoction known as "lean" containing codeine and promethazine, holds a prominent cultural presence and is often referenced in mass media platforms (e.g., music and social media). Surprisingly, there's a scarcity of national data characterizing the use of lean. Therefore, the current study investigated the use of lean using national survey data and online forum participant input, and focused on identifying concurrent substance use, exploring co-administration with other substances (e.g., alcohol, cannabis), and determining lean-related experiences. We analyzed data from the National Survey on Drug Use and Health (NSDUH) spanning 2007-2019, identifying persons who used lean (weighted N = 42,275). Additionally, we conducted a Reddit-based study to gather insights about lean consumtion (N = 192). The NSDUH data indicated that lean use was most prevalent among teenagers and young adults (ages 13-21), accounting for 66% of the sample. This trend was more pronounced in male respondents (75%) compared to females. Additionally, the use was predominantly observed among Black/African American (29%), Hispanic (28%), and White (33%) populations, with these groups also reporting higher levels of concurrent alcohol and cannabis use. Similarly, findings from Reddit showed that individuals who used lean were predominantly male (67%) and exhibited elevated concurrent rates of alcohol (83%) and cannabis (46%) use in the past 30 days. Moreover, approximately 66% of respondents met criteria for severe lean use disorder, and 37% acknowledged driving under its influence. The NSDUH data found that mostly young adult males reported consuming lean in the past twelve months, though the racial/ethnic breakdown of persons who used lean was diverse. The Reddit data found that most individuals in the sample met the criteria for a substance use disorder pertaining to their lean consumption. These findings underscore the clinical significance and necessity for further controlled research on lean.

Exploring COVID-related relationship extraction: Contrasting data sources and analyzing misinformation

The COVID-19 pandemic presented an unparalleled challenge to global healthcare systems. A central issue revolves around the urgent need to swiftly amass critical biological and medical knowledge concerning the disease, its treatment, and containment. Remarkably, text data remains an underutilized resource in this context. In this paper, we delve into the extraction of COVID-related relations using transformer-based language models, including Bidirectional Encoder Representations from Transformers (BERT) and DistilBERT. Our analysis scrutinizes the performance of five language models, comparing information from both PubMed and Reddit, and assessing their ability to make novel predictions, including the detection of “misinformation.” Key findings reveal that, despite inherent differences, both PubMed and Reddit data contain remarkably similar information, suggesting that Reddit can serve as a valuable resource for rapidly acquiring information during times of crisis. Furthermore, our results demonstrate that language models can unveil previously unseen entities and relations, a crucial aspect in identifying instances of misinformation.

Remember the Human: A Systematic Review of Ethical Considerations in Reddit Research

Reddit is one of the world's most prominent social media platforms, and also a valuable source of data for internet researchers. However, working with this kind of data also presents novel ethical complications for researchers, including issues around privacy, vulnerable populations, and unintended consequences. This paper describes an analysis of 134 papers that rely on Reddit data while also including some discussion of ethical implications and/or considerations by the researchers. Our analysis of these papers reveals common ethical issues and ethically motivated methodological decisions, as described by the researchers themselves, while also exposing some gaps for further ethical contemplation for researchers relying on Reddit data. Based on these findings, we close with a set of recommendations for ethically-informed methods and reflection for researchers working with social data.

Using Reddit Data to Refine Vaccine Messaging for a Plan-Do-Study-Act Communications Approach.

Language expressed in online forums can highlight which pro-vaccination messages intensify vaccine skepticism and which messages resonate with different populations. This study examined Reddit (an online discussion forum) to analyze what anonymous Canadians disclosed about their rationales for getting vaccinated against COVID-19 during the height of the pandemic. The investigation examined 266 Canadian subreddits (sub-forums on specific topics on Reddit) and evaluated 79 English-language phrases that people commonly use on Reddit to express the reason(s) why they (or someone close to them) chose to get vaccinated/boosted for COVID-19. The findings suggest that machine-learning techniques hold out the promise of a real-time approach toward public health messaging via an iterative Plan-Do-Study-Act cycle.

A platform for connecting social media data to domain-specific topics using large language models: an application to student mental health.

To design a novel artificial intelligence-based software platform that allows users to analyze text data by identifying various coherent topics and parts of the data related to a specific research theme-of-interest (TOI). Our platform uses state-of-the-art unsupervised natural language processing methods, building on top of a large language model, to analyze social media text data. At the center of the platform's functionality is BERTopic, which clusters social media posts, forming collections of words representing distinct topics. A key feature of our platform is its ability to identify whole sentences corresponding to topic words, vastly improving the platform's ability to perform downstream similarity operations with respect to a user-defined TOI. Two case studies on mental health among university students are performed to demonstrate the utility of the platform, focusing on signals within social media (Reddit) data related to depression and their connection to various emergent themes within the data. Our platform provides researchers with a readily available and inexpensive tool to parse large quantities of unstructured, noisy data into coherent themes, as well as identifying portions of the data related to the research TOI. While the development process for the platform was focused on mental health themes, we believe it to be generalizable to other domains of research as well.

Understanding public servants’ perspectives on return to office and digital government: lessons learned from Canada

PurposeThe purpose of this study is to explore public servants’ perspectives on return to office, hybrid work model and digital government. This is done by answering two research questions: what benefits and challenges public servants foresee with return to office and whether hybrid work enables or impedes digital government initiatives.Design/methodology/approachThis research is designed as a mixed methods study of federal and provincial governments in Canada based on the analysis of Reddit data. Research methods include machine-assisted toxicity and sentiment analysis and manual content analysis to identify emerging themes.FindingsThe findings highlight public servants’ mostly discussed concerns with return to office. Other notable discussion topics include resistance to the hybrid work model and identifying the ways how it would be operationalized. Some supported return to office.Research limitations/implicationsThis study’s limitations are related to using Reddit as the data source and user representation on Reddit. The main implications are its contribution to emerging literature on the future of work and digital government.Practical implicationsThis study highlights that perspectives of public servants are paramount for development and implementation of transformational initiatives and offers insights for public sector managers on how to incorporate these into practice while improving the efficiency of digital government initiatives and the system.Originality/valueThis study addresses the gap in literature by seeking to understand the perspectives of public servants in a variety of roles as well as implications of transition to hybrid work on digital government and future of work initiatives.

Exploring Real-Time Sentiment Analysis Prototype for Retail Industry

The rise of social media platforms has revolutionized the way consumers interact with retailers and express their opinions on products and services. Online retailers particularly need to keep a close eye on customer sentiment in real-time to make informed decisions about their offerings and improve customer satisfaction. However, efficiently analysing large volumes of unstructured text data from social media in real-time poses a significant challenge. This research aimed to develop a scalable, real-time sentiment analysis system tailored for online retailers using Reddit as the data source. The system comprises three main components: a data extraction and streaming pipeline, a sentiment analysis model, and a web application with real-time analytics. To address the data extraction challenge, a job queue-based system was implemented using Node.js, ‘BullMQ’, and Redis to create and manage campaigns for data streaming from Reddit. The data was streamed using Kafka, a distributed streaming platform, to enable efficient real-time processing. The sentiment analysis model was developed using a Naive Bayes classifier after experimenting with other machine learning and deep learning techniques. In the conducted study, the sentiment analysis model's performance was evaluated using standard metrics tailored to the context of online retail sentiment analysis. An accuracy of 0.6737 was achieved, reflecting the model's ability to correctly classify approximately 67.37 per cent of the sentiments in the test data. Concurrently, an F1 score of 0.7894 was recorded and the Area Under the Curve (AUC) value on the test data was measured at 0.5468, a metric that, while acceptable, suggests room for further refinement in the model's discriminatory ability between classes. The integration of the Data Version Control (DVC) system provided a mechanism for fine-tuning the model according to specific data requirements of various tenants. These results, taken together, not only validate the feasibility of employing a Naive Bayes classifier for real-time sentiment analysis in the retail context, but also provide a baseline for future research aimed at enhancing both the accuracy and efficiency of sentiment classification. The project’s evaluation focused on the performance of the sentiment analysis model, the efficiency of the Kafka streaming and real-time Spark pre-processing pipeline, and the backend infrastructure, including the job queuing system and WebSocket implementation. Various evaluation techniques, such as graphs and literature comparisons, were used to assess the system’s performance. In conclusion, this project successfully demonstrated the feasibility of a scalable, real-time sentiment analysis system for online retailers using Reddit data. The system has the potential to help retailers better understand customer opinions and make data-driven decisions for their businesses. Future work could include exploring alternative data sources, experimenting with more advanced sentiment analysis techniques, and enhancing the web application’s user interface and analytics capabilities.

RHMD: A Real-World Dataset for Health Mention Classification on Reddit

People on social media share their thoughts and experiences using diseases and symptoms words other than to mention their health, which can introduce biases in data-driven public health applications. For the advancement of HMC research, in this study, we present a Reddit health mention dataset (RHMD), a new dataset of multi-domain Reddit data for the HMC. RHMD is composed of 10 015 manually annotated Reddit posts that include 15 common disease or symptom terms and are labeled with four labels: personal health mentions (HMs), nonpersonal HMs, figurative HMs, and hyperbolic HMs. Empirical evaluation using recently proposed methods demonstrates the challenge of labeling user-generated text across these four types. Contributions to this work include the public release of a robustly annotated Reddit dataset (RHMD) for HM tasks and a comprehensive performance analysis of baseline methods. We expect the release of the dataset, and the evaluations will help facilitate the development of new methods for detecting HMs in the user-generated text. The dataset is available at.

A computational text analysis investigation of the relation between personal and linguistic agency

Previous psycholinguistic findings showed that linguistic framing – such as the use of passive voice - influences the level of agency attributed to other people. To investigate whether passive voice use relates to people’s personal sense of agency, we conducted three studies in which we analyzed existing experimental and observational data. In Study 1 (N = 835) we show that sense of personal agency, operationalized between participants as recalling instances of having more or less power over others, affects the use of agentive language. In Study 2 (N = 2.7 M) we show that increased personal agency (operationalized as one’s social media followership) is associated with more agentive language. In Study 3 and its two replications (N = 43,140) we demonstrate using Reddit data that the language of individuals who post on the r/depression subreddit is less agentive. Together, these findings advance our understanding of the nuanced relationship between personal and linguistic agency.

Sentiment analysis and natural language processing using Reddit data to evaluate patient opinions on hair loss therapeutics (Preprint)

Sentiment analysis and natural language processing using Reddit data to evaluate patient opinions on hair loss therapeutics (Preprint)

Why Majorities Are Silent but Minorities Are Loud: An Empirical Approach to Opinion Interactions in Online Communities

Majority opinions are often observed in online environments. Previous studies have demonstrated that majority opinions are constructed because people with minority opinions have fear of isolation, which forces them to be silent. However, we often observe that online users with different minority opinions fight each other, even though there are only a few. To explain this phenomenon, we developed a new theoretical model and examined it through the analysis of Reddit data. The results show that users in small communities expressed relatively homogeneous and less negative opinions, and constructed majority opinions that were not as strong as those in large communities. Contrarily, users in large communities showed relatively heterogeneous and more negative opinions, and built majority opinions more strongly than those in small communities. This implies that the model can properly explain why and how majority-opinion users are silent and why minority-opinion users are loud.

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.