Latent Topic Model Research Articles

Exploring trends in blockchain publications with topic modeling: Implications for forecasting the emergence of industry applications

AbstractTechnological innovation generates products, services, and processes that can disrupt existing industries and lead to the emergence of new fields. Distributed ledger technology, or blockchain, offers novel transparency, security, and anonymity characteristics in transaction data that may disrupt existing industries. However, research attention has largely examined its application to finance. Less is known of any broader applications, particularly in Industry 4.0. This study investigates academic research publications on blockchain and predicts emerging industries using academia‐industry dynamics. This study adopts latent Dirichlet allocation and dynamic topic models to analyze large text data with a high capacity for dimensionality reduction. Prior studies confirm that research contributes to technological innovation through spillover, including products, processes, and services. This study predicts emerging industries that will likely incorporate blockchain technology using insights from the knowledge structure of publications.

Unsupervised title and abstract screening for systematic review: a retrospective case-study using topic modelling methodology

BackgroundThe importance of systematic reviews in collating and summarising available research output on a particular topic cannot be over-emphasized. However, initial screening of retrieved literature is significantly time and labour intensive. Attempts at automating parts of the systematic review process have been made with varying degree of success partly due to being domain-specific, requiring vendor-specific software or manually labelled training data.Our primary objective was to develop statistical methodology for performing automated title and abstract screening for systematic reviews. Secondary objectives included (1) to retrospectively apply the automated screening methodology to previously manually screened systematic reviews and (2) to characterize the performance of the automated screening methodology scoring algorithm in a simulation study.MethodsWe implemented a Latent Dirichlet Allocation-based topic model to derive representative topics from the retrieved documents’ title and abstract. The second step involves defining a score threshold for classifying the documents as relevant for full-text review or not. The score is derived based on a set of search keywords (often the database retrieval search terms). Two systematic review studies were retrospectively used to illustrate the methodology.ResultsIn one case study (helminth dataset), 69.83% sensitivity compared to manual title and abstract screening was achieved. This is against a false positive rate of 22.63%. For the second case study (Wilson disease dataset), a sensitivity of 54.02% and specificity of 67.03% were achieved.ConclusionsUnsupervised title and abstract screening has the potential to reduce the workload involved in conducting systematic review. While sensitivity of the methodology on the tested data is low, approximately 70% specificity was achieved. Users ought to keep in mind that potentially low sensitivity might occur. One approach to mitigate this might be to incorporate additional targeted search keywords such as the indexing databases terms into the search term copora. Moreover, automated screening can be used as an additional screener to the manual screeners.

Public opinion evaluation on social media platforms: a case study of High Speed 2 (HS2) rail infrastructure project.

Public opinion evaluation is becoming increasingly significant in infrastructure project assessment. The inefficiencies of conventional evaluation approaches can be improved with social media analysis. Posts about infrastructure projects on social media provide a large amount of data for assessing public opinion. This study proposed a hybrid model which combines pre-trained RoBERTa and gated recurrent units for sentiment analysis. We selected the United Kingdom railway project, High Speed 2 (HS2), as the case study. The sentiment analysis showed the proposed hybrid model has good performance in classifying social media sentiment. Furthermore, the study applies latent Dirichlet allocation topic modelling to identify key themes within the tweet corpus, providing deeper insights into the prominent topics surrounding the HS2 project. The findings from this case study serve as the basis for a comprehensive public opinion evaluation framework driven by social media data. This framework offers policymakers a valuable tool to effectively assess and analyse public sentiment.

Comparison of Research Topics on Transportation Decarbonization Between Asian and Non-Asian Regions: Using Topic Modeling and Machine Learning Algorithms

The growing global interest to decarbonize the transportation industry has resulted in numerous scientific publications. This study reviews the rapidly expanding body of research and identifies the knowledge gaps in transport decarbonization between regions. This study employs a hybrid approach combining topic modeling and machine learning to identify research topics and their knowledge structures, and then compares the main debated topics between Asia and non-Asian regions. A dataset of 777 articles, including 410 Asian and 367 non-Asian articles, published between 1990 and 2022 was extracted from the Scopus database. The latent Dirichlet allocation topic modeling results showed that five potential topics were derived from Asia, while six were derived from non-Asian regions, and the knowledge structure of each topic differed between the two regions. The K-nearest neighbor machine learning algorithm results indicated a 92% accuracy for Asian topics and an 89% accuracy for non-Asian topics. The findings suggest that the Asian studies focused on “energy use in transportation” and “drivers of CO2 emissions in transportation,” while the non-Asian studies focused on “electric vehicles” and “fuel consumption.” This paper will keep academics and practitioners updated on the paradigm shift in the research on transportation decarbonization.

Applying Latent Allocation Topic Model to Explore the Policy Changes of Teachers’ Morality Development for Teacher Educational Sustainability in China

The policy changes of teachers’ morality development in Chinese colleges and universities have depicted the evolution of teachers’ morality policy. However, there are few previous studies on applying the content analysis to examine the educational policy changes of teacher morality development from the insight of China’s higher education system. Thus, this study applies the LDA topic model as one content analysis approach to provide high-frequency word analysis, analysis of centrality, and cluster analysis to explore the overall policy trends of teachers’ morality development in China. It is found that the policy objectives and contents of teachers’ ethics construction in colleges and universities are constantly clear and definite. The policy content of teachers’ morality construction is continuously clear. The policy direction and policy value of the construction of teachers’ ethics in colleges and universities have been continuously broadened and deepened. The policy evaluation and supervision of teachers’ ethics construction in colleges and universities have been continuously optimized and improved. In addition, the conclusions and suggestions have been offered in the final section.

Stronger than partisanship and motivated reasoning: news exposure and news frames predicting US state-level preventive behaviours against COVID-19

The COVID-19 pandemic has become a partisan issue rather than an independent public health issue in the US. This study examined the behavioural consequences of motivated reasoning and framing by investigating the impacts of COVID-19 news exposure and news frames, as apparent through a Latent Dirichlet topic modelling analysis of local news coverage, on state-level preventive behaviours as understood through a nationally representative survey. Findings suggested that the media effects on various preventive behaviours differed. The overall exposure rate to all COVID-19 news articles increased mask-wearing but did not significantly impact other preventive behaviours. Four news frames significantly increased avoiding contact or avoiding public or crowded places. However, news articles discussing anxiety and stay at home order triggered resistance and countereffects and led to risky behaviours. ‘Solid Republican’ state residents were less likely to avoid contact, avoid public or crowded places, and wear masks. However, partisan leanings did not interfere with the impact of differing local COVID-19 news frames on reported preventive behaviours. Plus, statements regarding pre-existing trust in Trump did not correlate with reported preventive behaviour. Attention to effect sizes revealed that news exposure and news frames could have a bigger impact on health behaviours than motivated reasoning.

Analysis of the impact of investor sentiment on stock price using the latent dirichlet allocation topic model

Investor sentiment has always been an active research topic in finance. In recent years, text mining, machine learning and sentiment analysis have been very fruitful, and researchers can extract valuable information from social platforms more promptly and accurately. Unsupervised learning avoids the subjective human selection of data while reducing the workload. This paper uses the primary model for the unsupervised learning total probability generative model LDA (Latent Dirichlet Allocation). Natural language processing and word-splitting tools empirically analyze text data from a well-known financial and stock information website. An attempt is made to explore the correlation with stock excess return. The significant findings are as follows. First, investor sentiment classified by theme is positively correlated with excess return. Second, different themes have different degrees of influence, with “broad market sentiment” affecting the short term, corporate development involving a long time, and “corporate dividends” affecting both. Third, there is an asymmetric effect of investor sentiment on excess return.

Latent Dirichlet allocation topic modeling of free-text responses exploring the negative impact of the early COVID-19 pandemic on research in nursing.

To derive latent topics from free-text responses on the negative impact of the pandemic on research activities and determine similarities and differences in the resulting themes between academic-based and clinical-based researchers. We performed a secondary analysis of free-text responses from a cross-sectional online survey conducted by the Japan Academy of Nursing Science of its members in early 2020. The participants were categorized into two groups by workplace (academic-based and clinical-based researchers). Latent Dirichlet allocation (LDA) topic modeling was used to extract latent topics statistically and list important keywords/text associated with the topics. After organizing similar topics by principal component analysis (PCA), we finally derived topic-associated themes by reading the keywords/texts and determining the similarity and differences of the themes between the two groups. A total of 201 respondents (163 academic-based and 38 clinical-based researchers) provided free-text responses. LDA identified eight and three latent topics for the academic-based and clinical-based researchers, respectively. While PCA re-grouped the eight topics derived from the former group into four themes, no merging of the topics from the latter group was performed resulting in three themes. The only theme common to the two groups was "barriers to conducting research," with the remaining themes differing between the groups. Using LDA topic modeling with PCA, we identified similarities and differences in the themes described in free-text responses about the negative impact of the pandemic between academic-based and clinical-based researchers. Measures to mitigate the negative impact of pandemics on nursing research may need to be tailored separately.

Analyzing Public Conversations About Heart Disease and Heart Health on Facebook From 2016 to 2021: Retrospective Observational Study Applying Latent Dirichlet Allocation Topic Modeling.

Heart disease continues to be the leading cause of death in men and women in the United States. The COVID-19 pandemic has further led to increases in various long-term cardiovascular complications. This study analyzed public conversations related to heart disease and heart health on Facebook in terms of their thematic topics and sentiments. In addition, it provided in-depth analyses of 2 subtopics with important practical implications: heart health for women and heart health during the COVID-19 pandemic. We collected 34,885 posts and 51,835 comments spanning from June 2016 to June 2021 that were related to heart disease and health from public Facebook pages and groups. We used latent Dirichlet allocation topic modeling to extract discussion topics illuminating the public's interests and concerns regarding heart disease and heart health. We also used Linguistic Inquiry and Word Count (Pennebaker Conglomerates, Inc) to identify public sentiments regarding heart health. We observed an increase in discussions related to heart health on Facebook. Posts and comments increased from 3102 and 3632 in 2016 to 8550 (176% increase) and 14,617 (302% increase) in 2021, respectively. Overall, 35.37% (12,340/34,885) of the posts were created after January 2020, the start of the COVID-19 pandemic. In total, 39.21% (13,677/34,885) of the posts were by nonprofit health organizations. We identified 6 topics in the posts (heart health promotion, personal experiences, risk-reduction education, heart health promotion for women, educational information, and physicians' live discussion sessions). We identified 6 topics in the comments (personal experiences, survivor stories, risk reduction, religion, medical questions, and appreciation of physicians and information on heart health). During the pandemic (from January 2020 to June 2021), risk reduction was a major topic in both posts and comments. Unverified information on alternative treatments and promotional content was also prevalent. Among all posts, 14.91% (5200/34,885) were specifically about heart health for women centering on local event promotion and distinctive symptoms of heart diseases for women. Our results tracked the public's ongoing discussions on heart disease and heart health on one prominent social media platform, Facebook. The public's discussions and information sharing on heart health increased over time, especially since the start of the COVID-19 pandemic. Various levels of health organizations on Facebook actively promoted heart health information and engaged a large number of users. Facebook presents opportunities for more targeted heart health interventions that can reach and engage diverse populations.

Research on the promotion of new energy vehicles based on multi-source heterogeneous data: consumer and manufacturer perspectives.

Under the background of green development, new energy vehicles, as an important strategic emerging industry, play a crucial role in energy conservation and emission reduction. In the post-epidemic era, steadily promoting the promotion of new energy vehicles will be a hot topic. Based on multi-source heterogeneous data, combined with the latent Dirichlet allocation topic model, social network analysis, and econometric methods, this paper explores whether individual purchase decisions and company-level cooperative research and development will promote the promotion of new energy vehicles. The results show thatwhether it isbattery electric vehicles, hybrid electric vehiclesor plug-in hybrid electric vehicles, users are more concerned about space dimension, power performance, and design style. Patent collaboration network analysis indicates that new energy vehicle enterprises are establishing close partnerships, which will urge the promotion of new energy vehicles. An interesting test result found that for short-term innovation, new energy vehicles enterprises should invest more patent research and development in battery electric vehicles and hybrid electric vehicles models to better accelerate the promotion of new energy vehicles.

Using Natural Language Processing to Explore "Dry January" Posts on Twitter: Longitudinal Infodemiology Study.

Dry January, a temporary alcohol abstinence campaign, encourages individuals to reflect on their relationship with alcohol by temporarily abstaining from consumption during the month of January. Though Dry January has become a global phenomenon, there has been limited investigation into Dry January participants' experiences. One means through which to gain insights into individuals' Dry January-related experiences is by leveraging large-scale social media data (eg, Twitter chatter) to explore and characterize public discourse concerning Dry January. We sought to answer the following questions: (1) What themes are present within a corpus of tweets about Dry January, and is there consistency in the language used to discuss Dry January across multiple years of tweets (2020-2022)? (2) Do unique themes or patterns emerge in Dry January 2021 tweets after the onset of the COVID-19 pandemic? and (3) What is the association with tweet composition (ie, sentiment and human-authored vs bot-authored) and engagement with Dry January tweets? We applied natural language processing techniques to a large sample of tweets (n=222,917) containing the term "dry january" or "dryjanuary" posted from December 15 to February 15 across three separate years of participation (2020-2022). Term frequency inverse document frequency, k-means clustering, and principal component analysis were used for data visualization to identify the optimal number of clusters per year. Once data were visualized, we ran interpretation models to afford within-year (or within-cluster) comparisons. Latent Dirichlet allocation topic modeling was used to examine content within each cluster per given year. Valence Aware Dictionary and Sentiment Reasoner sentiment analysis was used to examine affect per cluster per year. The Botometer automated account check was used to determine average bot score per cluster per year. Last, to assess user engagement with Dry January content, we took the average number of likes and retweets per cluster and ran correlations with other outcome variables of interest. We observed several similar topics per year (eg, Dry January resources, Dry January health benefits, updates related to Dry January progress), suggesting relative consistency in Dry January content over time. Although there was overlap in themes across multiple years of tweets, unique themes related to individuals' experiences with alcohol during the midst of the COVID-19 global pandemic were detected in the corpus of tweets from 2021. Also, tweet composition was associated with engagement, including number of likes, retweets, and quote-tweets per post. Bot-dominant clusters had fewer likes, retweets, or quote tweets compared with human-authored clusters. The findings underscore the utility for using large-scale social media, such as discussions on Twitter, to study drinking reduction attempts and to monitor the ongoing dynamic needs of persons contemplating, preparing for, or actively pursuing attempts to quit or cut down on their drinking.

Extending artificial intelligence research in the clinical domain: a theoretical perspective.

Academic research to the utilization of artificial intelligence (AI) has been proliferated over the past few years. While AI and its subsets are continuously evolving in the fields of marketing, social media and finance, its application in the daily practice of clinical care is insufficiently explored. In this systematic review, we aim to landscape various application areas of clinical care in terms of the utilization of machine learning to improve patient care. Through designing a specific smart literature review approach, we give a new insight into existing literature identified with AI technologies in the clinical domain. Our review approach focuses on strategies, algorithms, applications, results, qualities, and implications using the Latent Dirichlet Allocation topic modeling. A total of 305 unique articles were reviewed, with 115 articles selected using Latent Dirichlet Allocation topic modeling, meeting our inclusion criteria. The primary result of this approach incorporates a proposition for future research direction, abilities, and influence of AI technologies and displays the areas of disease management in clinics. This research concludes with disease administrative ramifications, limitations, and directions for future research.

Sexually Transmitted Disease-Related Reddit Posts During the COVID-19 Pandemic: Latent Dirichlet Allocation Analysis.

Sexually transmitted diseases (STDs) are common and costly, impacting approximately 1 in 5 people annually. Reddit, the sixth most used internet site in the world, is a user-generated social media discussion platform that may be useful in monitoring discussion about STD symptoms and exposure. This study sought to define and identify patterns and insights into STD-related discussions on Reddit over the course of the COVID-19 pandemic. We extracted posts from Reddit from March 2019 through July 2021. We used a topic modeling method, Latent Dirichlet Allocation, to identify the most common topics discussed in the Reddit posts. We then used word clouds, qualitative topic labeling, and spline regression to characterize the content and distribution of the topics observed. Our extraction resulted in 24,311 total posts. Latent Dirichlet Allocation topic modeling showed that with 8 topics for each time period, we achieved high coherence values (pre-COVID-19=0.41, prevaccination=0.42, and postvaccination=0.44). Although most topic categories remained the same over time, the relative proportion of topics changed and new topics emerged. Spline regression revealed that some key terms had variability in the percentage of posts that coincided with pre-COVID-19 and post-COVID-19 periods, whereas others were uniform across the study periods. Our study's use of Reddit is a novel way to gain insights into STD symptoms experienced, potential exposures, testing decisions, common questions, and behavior patterns (eg, during lockdown periods). For example, reduction in STD screening may result in observed negative health outcomes due to missed cases, which also impacts onward transmission. As Reddit use is anonymous, users may discuss sensitive topics with greater detail and more freely than in clinical encounters. Data from anonymous Reddit posts may be leveraged to enhance the understanding of the distribution of disease and need for targeted outreach or screening programs. This study provides evidence in favor of establishing Reddit as having feasibility and utility to enhance the understanding of sexual behaviors, STD experiences, and needed health engagement with the public.

Interpretable tourism demand forecasting with temporal fusion transformers amid COVID-19.

An innovative ADE-TFT interpretable tourism demand forecasting model was proposed to address the issue of the insufficient interpretability of existing tourism demand forecasting. This model effectively optimizes the parameters of the Temporal Fusion Transformer (TFT) using an adaptive differential evolution algorithm (ADE). TFT is a brand-new attention-based deep learning model that excels in prediction research by fusing high-performance prediction with time-dynamic interpretable analysis. The TFT model can produce explicable predictions of tourism demand, including attention analysis of time steps and the ranking of input factors’ relevance. While doing so, this study adds something unique to the literature on tourism by using historical tourism volume, monthly new confirmed cases of travel destinations, and big data from travel forums and search engines to increase the precision of forecasting tourist volume during the COVID-19 pandemic. The mood of travelers and the many subjects they spoke about throughout off-season and peak travel periods were examined using a convolutional neural network model. In addition, a novel technique for choosing keywords from Google Trends was suggested. In other words, the Latent Dirichlet Allocation topic model was used to categorize the major travel-related subjects of forum postings, after which the most relevant search terms for each topic were determined. According to the findings, it is possible to estimate tourism demand during the COVID-19 pandemic by combining quantitative and emotion-based characteristics.

From #BlackLivesMatter to #StopAsianHate: Examining Network Agenda-Setting Effects of Hashtag Activism on Twitter

With large, representative, and comparable data scraped from Twitter, this study tries to provide comprehensive understanding of the salient topics under #BlackLivesMatter and #StopAsianHate online movements. Employing semi-supervised Latent Dirichlet allocation topic modeling, five topics have been extracted from 3-month tweets data after George Floyd’s death in 2020. Six topics have been extracted from 3-month tweets data after the Atlantic spa shooting tragedy in 2021. Both movements reflected salient topics on the tragedy that just took place during the data collection period. In addition, general violence, collective actions, community support, and criticism on White racism are all identified as important issues of the counter-racism discourse flooded on social media. In addition, our study explores the network agenda-setting effects of hashtag activisms. The results show that issue networks of the first 2 weeks’ counter-racism discourses after the crime could not set network agenda for the next 2 weeks’ discourses. However, the network agenda-setting effects became significant after the first 2 weeks and stayed stable as time went on. In addition, we do not find a significant relationship between issue networks of the two movements under study. It counter-argues any assumption that one counter-racism movement online could trigger similar movements among different groups.

The application of network agenda setting model during the COVID-19 pandemic based on latent dirichlet allocation topic modeling.

Based on Network Agenda Setting Model, this study collected 42,516 media reports from Party Media, commercial media, and We Media of China during the COVID-19 pandemic. We trained LDA models for topic clustering through unsupervised machine learning. Questionnaires (N = 470) and social network analysis methods were then applied to examine the correlation between media network agendas and public network agendas in terms of explicit and implicit topics. The study found that the media reports could be classified into 14 topics by the LDA topic modeling, and the three types of media presented homogeneity in the topics of their reports, yet had their own characteristics; there was a significant correlation between the media network agenda and the public network agenda, and the We Media reports had the most prominent effect on the public network agenda; the correlation between the media agenda and the implicit public agenda was higher than that of the explicit public agenda. Overall, findings showed a significant correlation between network agendas among different media.

Trends in wildfire-related bills in California, 2001–2020

California has recently experienced severe and destructive wildfire seasons, prompting new policy proposals from the California State Legislature. Here, we examine proposed state wildfire-related legislation from the ten two-year legislative sessions between 2001–2002 and 2019–2020 to track trends and factors influencing wildfire-related legislative priorities. We first use Latent Dirichlet allocation topic modeling to identify 29 wildfire topics from the text of 294 bills. We next determine the degree to which legislators’ backgrounds, legislative procedures, and bill intent are correlated with bill topic. We find individual correlations but no overarching correlations between legislators’ personal and political backgrounds (e.g. political party, legislative experience, home district, bipartisanship), legislative procedures (i.e. committee review, state mandates, urgency measures), and bill intent (i.e. wildfire stage, commemorative bill, focus on specific geographic area or vulnerable group) with bill topics. However, we find that successful bills are correlated with review by Appropriations Committees, review by multiple committees, and a focus on specific geographic location. In the 2019–2020 legislative session, bill passage was correlated with Democratic sponsorship. Legislators tend to propose bills related to the dominant challenge or crisis of that legislative session (such as loss of homeowners’ insurance coverage or need for more fuels management), indicating the reactiveness of the legislature to statewide issues with new proposed legislation. These findings provide important insights into how legislative priorities change over time and the policy enablers and barriers for urgent climate change adaptation. Our results indicate a potentially fundamental policy shift in response to recent wildfires in California.

Experiences of Women With Interstitial Cystitis/Bladder Pain Syndrome: What Can We Learn From Women's Online Discussions?

Interstitial cystitis/bladder pain syndrome is a debilitating chronic condition that disproportionately affects women at a ratio of 5:1. We sought to capture women's experiences with interstitial cystitis/bladder pain syndrome by conducting a large-scale digital ethnographic analysis of anonymous posts on Internet forums. Online posts were identified using condition-specific keywords and data mining extraction services. Once posts were identified, a random sample of 200 online posts was coded and analyzed by hand using qualitative methods. A Latent Dirichlet Allocation probabilistic topic model was applied to the complete dataset to substantiate the qualitative analysis and allow for further thematic discovery. A total of 6,842 posts written by 3,902 unique users from 224 websites were identified. There was a significant overlap between the hand coding and Latent Dirichlet Allocation themes. Our analysis yielded the following themes: online community engagement, triggers and disease etiologies, medical comorbidities, quality of life impact, patient experience with medical care, and alternative therapies and self-management strategies. Additionally, our population appeared to have a high burden of nonurological associated syndromes. We identified barriers to patient-centered care and found that online peer support was important for women. Our digital ethnographic analysis is a novel application of qualitative methods using online sources. Social media analytics appears to capture a broader patient population than that typically included in clinic-based qualitative studies, such as patient interviews and focus groups. Understanding patient behaviors and concerns are important to guide strategies for improving care and the overall experience with this difficult-to-treat condition.

Exploring the nexus between food and veg*n lifestyle via text mining-based online community analytics

Attention towards veg*nism is increasing as the impact of food choices on health and sustainability as well as ethical concerns regarding animal welfare emerge. Although online user analysis is an effective tool to obtain practical insights without geographical constraints, implementation on a large veg*n population has been carried out within a limited scope. This study investigated two veg*n subreddits, r/Vegan and r/Vegetarian, using multiple text mining techniques to classify users' interests and preferences. Based on K-means and term frequency-inverse document frequency, six clusters were identified: Food, Perception, Health, Altruism, Emotion, and Situation. The proportion of each cluster and keywords representing the clusters were obtained. Being a major sector, further assessment of the Food cluster was conducted using Latent Dirichlet Allocation topic modeling technique. Confusion was observed in relation to being pressured with sudden changes in dietary patterns, including meal composition, preparation, and shopping routines. The results also revealed barriers to transition for individuals who have recently started veg*n diets, and those wishing to switch to stricter dietary patterns. In addition to difficulties relating to economic and social aspects, our findings suggest that the establishment of detailed guidelines may help accommodate the various dietary compositions across the veg*n spectrum, and clear information relating to veg*n food products is needed from manufacturers.

Investigating Topic Modeling Techniques to Extract Meaningful Insights in Italian Long COVID Narration.

Through an adequate survey of the history of the disease, Narrative Medicine (NM) aims to allow the definition and implementation of an effective, appropriate, and shared treatment path. In the present study different topic modeling techniques are compared, as Latent Dirichlet Allocation (LDA) and topic modeling based on BERT transformer, to extract meaningful insights in the Italian narration of COVID-19 pandemic. In particular, the main focus was the characterization of Post-acute Sequelae of COVID-19, (i.e., PASC) writings as opposed to writings by health professionals and general reflections on COVID-19, (i.e., non-PASC) writings, modeled as a semi-supervised task. The results show that the BERTopic-based approach outperforms the LDA-base approach by grouping in the same cluster the 97.26% of analyzed documents, and reaching an overall accuracy of 91.97%.