Latent Dirichlet Allocation Analysis Research Articles

Insights into sustainability and circular economy trends in luxury fashion: A LinkedIn topic modelling study

The fashion industry plays a pivotal role in societal evolution and economic growth, yet it also stands as a significant contributor to environmental degradation. In response, the concept of slow fashion has been gaining increased attention in recent years, offering sustainable and circular alternatives to fast fashion. Luxury fashion, traditionally known for its heritage, exclusivity, and craftsmanship, exhibits elements similar to slow fashion, thereby exerting considerable influence on industry trends and consumer behaviour. This study explores the drivers shaping the luxury fashion landscape and the evolving circular economy and sustainability trends within the UK, a key global influencer in the luxury fashion domain. Luxury fashion's impact extends beyond its niche market, often setting benchmarks for innovation and sustainable practices that influence the broader fashion industry. Leveraging readily available qualitative and quantitative data from LinkedIn profiles, this research employs Latent Dirichlet Allocation analysis to uncover relevant topics within a dataset comprising 1115 companies registered in the “Apparel and Fashion” and “Textiles” industries. Five key drivers emerge, including Artful Creations in Fashion, Time-Honoured Craftsmanship, The Art of Style and Design, The Pinnacle of Retail Excellence, and A Personalised Style Experience. Additionally, utilising the Google Collab Platform and Python language, this study identifies circular economy and sustainability trends within luxury fashion. While approximately 20% of the companies studied actively emphasise sustainability, circular economy requires comparable attention. This research provides industry stakeholders and scholars with valuable insights into sustainable and circular practices, thus guiding innovation and informed decision-making in the fashion industry.

Exploring Guilin's Destination Image Via User-Generated Content: A Computational Methods

This study quantify mainland China's destination image through international tourists' user-generated content (UGC), with a specific focus on Guilin, utilizing advanced computational techniques such as Latent Dirichlet Allocation (LDA) for topic modeling and sentiment analysis. It systematically evaluates online travel reviews to distill key themes and emotional sentiments that shape Guilin's image, uncovering a rich tapestry of cognitive perceptions and affective reactions related to its natural scenery, cultural engagements, service standards, and logistical facets. The application of topic modeling and sentiment analysis provides a nuanced understanding of the cognitive and affective dimensions defining Guilin's destination image. While positive sentiments largely highlight the region's aesthetic and experiential allure, negative sentiments reveal critical areas for improvement, such as perceived value and infrastructure, which are vital for enhancing tourist satisfaction and reinforcing Guilin's appeal in the global tourism market. This research applies LDA and sentiment analysis to interpret UGC offer a calculable methodological approach for broader application in destination image studies. By aligning perceptual and emotional insights with destination marketing strategies, the findings offer actionable intelligence to optimize tourist experiences and enhance destination loyalty. This approach not only enriches the academic understanding of destination images but also provides practical frameworks for destination managers to harness the full potential of UGC in shaping and refining the global image of tourism locales.

Exploring the Polarization of American Elites Based on the Analyses of Candidates’ Statements During the 2020 Presidential Election

ABSTRACT The article consists of three parts. The first contains a theoretical analysis of the phenomenon of US political polarization and discusses the main approaches to its explanation, primarily through the prism of the influence of political elites. The authors note a logical contradiction in the existing concepts, which postulate the existence of an “asymmetry” in the influence of elites on polarization in the United States, arguing that the contribution of the Republicans is more destructive than the contribution of representatives of the Democratic Party. The authors find the growth of political polarization, regardless of party preferences (while noting the importance of elite behavior as one of the central factors of political polarization). The second part examines the phenomenon of affective (or emotional polarization), based on the socio-psychological rejection of representatives of an alien political camp. Based on the results of a network analysis of a two-modal network consisting of pre-election messages and topics selected by the authors themselves, the author notes that all candidates, without exception, are prone to emotional affect as part of their election campaign. The third part is devoted to the study of positional (or ideological) polarization, in which the author, on the basis of Latent Dirichlet Allocation analysis, divides the corpus of campaign messages of candidates into separate topics and, through a comparative analysis, finds a significant distance from “bipartisan” topics (i.e. from the political center) among all candidates.

Sustainable Body Positivity Movement: Analysis of the Discourse on Body Image in Korean Society

In contemporary society, the discourse on body image is increasingly emerging as a notable social issue. In particular, the body positivity movement is promoting healthy body image and self-esteem through various means. This study was conducted to analyze the discourse on sustainable body image in Korean society. User-generated content from 1 January 2014 to 31 July 2023 underwent data refinement and term frequency (TF), TF–inverse document frequency (TF–IDF), and Latent Dirichlet Allocation (LDA) analyses. The number of blog posts in 2020 was nearly triple the number in 2019. Thus, the analysis period was divided into first (from 2014 to 2019) and second (from 2020 to 31 July 2023) periods. The TF–IDF analysis showed that shooting, photo, diet, exercise, goal, and challenge were among the top words in the first period, while Instagram-related words were mosr frequent in the second period. This finding suggested that social distancing policies significantly affected social media usage. The LDA analysis revealed five topics that were common in the first and second periods and three topics that emerged in the second period. Overall, while Western societies tend to idealize specific body types, body image discourse in Korea is centered around exercise as a means to achieve “photography” or “photo shoot”-related goals. Exercise is perceived as an activity performed for pleasure rather than attaining a particular body shape. Furthermore, there is a desire to document one’s body beautifully and maintain exercise habits in the long run. The results of this study could serve as foundational material for establishing and sustaining a positive body image culture.

Horizon 2020 Project Analysis by Using Topic Modelling Techniques in the Field of Transport

Abstract Understanding the main research directions in transport is crucial to provide useful and relevant insights. The analysis of Horizon 2020, the largest research and innovation framework, has been already realized in a few publications but rarely for the field of transport. Thus, this article is devoted to fill this gap by introducing a novel application of topic modelling techniques, specifically the Latent Dirichlet Allocation (LDA), in the Horizon 2020 framework for transport projects. The method is using the Mallet software with pre-examined code optimizations. As the first step, a corpus is created by collecting 310 project abstracts; afterward, the texts of abstracts are prepared for the LDA analysis by introducing stop words, optimization criteria, the number of words per topics, and the number of topics. The study successfully uncovers the following five main underlying topics: road and traffic safety, aviation and aircraft, mobility and urban transport, maritime industry and shipping, open and real-time data in transport. Besides that, the main trends in transport are identified based on the frequency of words and their occurrence in the corpus. The applied approach maximizes the added value of the Horizon 2020 initiatives by revealing insights that may be overlooked using traditional analysis methods.

Sentiment and thematic analysis of faculty responses: Transition to online learning

This project aims to understand physics faculty responses to transitioning to online teaching during the COVID-19 pandemic. We surveyed 662 physics faculty from the United States following the Spring 2020 term; of these, 258 completed a follow-up survey after the Fall 2020 term. We used natural language processing to measure the sentiment scores of 364 Spring 2020 responses and another 134 Fall 2020 responses of physics faculty who completed an optional written prompt. Additionally, we determined the change in sentiment scores of the 100 individuals who responded to both surveys. These sentiment scores measured between −1 and 1 for completely negative and completely positive, respectively. Sentiment scores after Spring 2020 were slightly positive with a median value of 0.2347. The distribution of sentiment changes was approximately normally distributed with a mean centered near zero. Analysis suggests the average sentiment did not change from the initial to follow-up surveys. To identify major topics within the responses for both surveys, latent Dirichlet allocation analysis was applied to the data. The topic distribution for the initial survey is given as course modifications and technology, negative aspects of the transition—primarily with labs and cheating, exam and evaluation difficulties, and difficulties with student understanding. The topics were noticeably different in the follow-up survey with differences between Fall and Spring, cooperative learning strategies, strategies that worked in the remote space, and benefits of in-person labs. Published by the American Physical Society 2024

A Latent Dirichlet Allocation approach to understanding students’ perceptions of Automated Writing Evaluation

Automated writing evaluation (AWE) has shown promise in enhancing students’ writing outcomes. However, further research is needed to understand how AWE is perceived by middle school students in the United States, as they have received less attention in this field. This study investigated U.S. middle school students’ perceptions of the MI Write AWE system. Students reported their perceptions of MI Write's usefulness using Likert-scale items and an open-ended survey question. We used Latent Dirichlet Allocation (LDA) to identify latent topics in students’ comments, followed by qualitative analysis to interpret the themes related to those topics. We then examined whether these themes differed among students who agreed or disagreed that MI Write was a useful learning tool. The LDA analysis revealed four latent topics: (1) students desire more in-depth feedback, (2) students desire an enhanced user experience, (3) students value MI Write as a learning tool but desire greater personalization, and (4) students desire increased fairness in automated scoring. The distribution of these topics varied based on students’ ratings of MI Write's usefulness, with Topic 1 more prevalent among students who generally did not find MI Write useful and Topic 3 more prominent among those who found MI Write useful. Our findings contribute to the enhancement and implementation of AWE systems, guide future AWE technology development, and highlight the efficacy of LDA in uncovering latent topics and patterns within textual data to explore students’ perspectives of AWE.

Analysis of Suicide-related Tweets During the COVID-19 Pandemic

Background:: The COVID-19 virus started in 2019 and badly affected the different sectors of many countries around the world. Based on this, financial difficulties, loss of loved ones, sudden anger, relationships, family disputes, and psychological distress increased, and individuals were stalled from carrying out their lifestyle in a normal way, and some individuals were even motivated to commit suicide. Objective:: It is important to reduce the number of suicides and identify the reasons for this situation. Through this research, the focus is on identifying the main topics discussed relevant to suicides during the COVID-19 pandemic. Methods:: Individuals use Twitter, a social media platform, to share their ideas freely and publically. We collected 9750 primary data through Twitter API (Application Programming Interface). After preprocessing and feature extraction by TF-IDF (Term Frequency-Inverse Document Frequency), we applied the LDA (Latent Dirichlet Allocation) and Probabilistic Latent Semantic Analysis (PLSA) topic modeling algorithms to identify topics. Results:: Based on the LDA results, we extracted ten different topics under the three themes, such as the impact of COVID-19, human feelings, getting support, and having awareness. Intertopic Distance Map, Most Salient Terms, and Word Clouds Visualization are used to check the results. The coherence score and perplexing value are used to measure how interpretable the extracted topics are to humans. PLSA also extracted 25 topics with their probabilities, and Kullback–Leibler (KL) divergence was used to check the results. Conclusion:: We were able to gain insight into human emotions and the main motivations behind suicide attempts using the topics we extracted. Expert feedback proved that LDA results were better than PLSA. Based on that, we found the main impact of COVID-19 on human lives, how human feelings were changed positively and negatively during that period, what supporting and awareness methods people used, and what they preferred. The required measures can then be taken by those responsible authorities and individuals to prevent, reduce, and get ready for this kind of suicidal incident in the future.

Industry 4.0: how digital transformation affects stock prices of Chinese and American companies

PurposeThe goal of this work is to evaluate how digital transformation disclosure in corporate news and press releases affects stock prices. We examine American and Chinese companies from the energy and industry sectors for two periods: pre-COVID-19 and during the COVID-19 pandemic.Design/methodology/approachTo estimate the effects of disclosure of information related to digital transformation, we applied the bag-of-words (BOW) method. As the benchmark dictionary, we used Kindermann et al. (2021), with the addition of original dictionaries created via Latent Dirichlet allocation (LDA) analysis. We also employed panel regression analysis and random forest.FindingsFor USA energy sector, all aspects of digital transformation were insignificant in pre-COVID-19 period, while sustainability topics became significant during the pandemic. As for the Chinese energy sector, digital strategy implementation was significant in pre-pandemic period, while digital technologies adoption and business model innovation became relevant in COVID-19 period. The results show the greater significance of digital transformation aspects for industrials sectors compared to the energy sector. The result of random forest analysis proves the efficiency of the authors’ dictionary which could be applied in practice. The developed methodology can be considered relevant.Originality/valueThe research contributes to the existing literature in theoretical, empirical and methodological ways. It applies signaling and information asymmetry theories to the financial markets, digital transformation being used as an instrument. The methodological contribution of this article can be described in several ways. Firstly, our data collection process differs from that in previous papers, as the data are gathered “from investor’s point of view”, i.e. we use all public information published by the company. Secondly, in addition to the use of existing dictionaries based on Kindermann et al. (2021), with our own modifications, we apply the original methodology based on LDA analysis. The empirical contribution of this research is the following. Unlike past works, we do not focus on particular technologies (Hong et al., 2023) connected with digital transformation, but try to cover all multi-dimensional aspects of the transformational process and aim to discover the most significant one.

A content analysis: analyzing topics of conversation under the #sustainability hashtag on Twitter

Abstract This study aimed to identify and understand the major topics of discussion under the #sustainability hashtag on Twitter (now known as “X”) and understand user engagement. The sharp increase in social media usage combined with a rise in climate anomalies in recent years makes the area of sustainability with respect to social media a critical topic. Python was used to gather Twitter posts between January 1, 2023, and March 1, 2023. User engagement metrics were analyzed using a variety of statistical analysis methods, including keyword-frequency analysis and Latent Dirichlet Allocation (LDA), which were used to identify significant topics of discussion under the #sustainability hashtag. Additionally, histograms and scatter plots were used to visualize user engagement. LDA analysis was conducted with 7 topics after trials were run with various topics and results were analyzed to determine which number of topics best fit the dataset. The frequency analysis provided a basic overview of the discourse surrounding #sustainability with the topics of technology, business and industry, environmental awareness, and discussion of the future. The LDA model provided a more comprehensive view, including additional topics such as Environmental, Social, and Governance (ESG) and infrastructure, investing, collaboration, and education. These findings have implications for researchers, businesses, organizations, and politicians seeking to align their strategies and actions with the major topics surrounding sustainability on Twitter to have a greater impact on their audience. Researchers can use the results of this study to guide further research on the topic or contextualize their study with existing literature within the field of sustainability.

A thematic analysis of code of ethics disclosures in SEC 8‐K Item 5.05

AbstractThe Securities and Exchange Commission requires the disclosure of changes to or waivers of corporate codes of ethics. Because the nature of amendments or waivers can vary, we expect the text of Item 5.05 to include different topics within different filings. We examine the population of these disclosures in Item 5.05 8‐K filings from 2004 to 2020. While previous studies utilized small samples (fewer than 50 observations) to examine limited aspects of these filings, we use the population of these filings from 2004 to 2020 (2121 8‐K filings) to elucidate the nature and details of the disclosures. We assess whether Latent Dirichlet Allocation—a computational linguistics technique—can help discern the underlying topics represented in filings. While the Securities and Exchange Commission identifies two topics—amendments and waivers—the Latent Dirichlet Allocation analysis reveals four topics (three related to amendments): (1) code updates and clarifications; (2) combining codes of conduct for all officers and employees, or splitting codes to include a code directly applicable to senior financial officers; (3) codes of ethics waivers; and (4) substantive code changes. The overall trend is for fewer 8‐Ks to be filed in recent years, with updates and clarifications becoming the predominant filing type. Our results further indicate that Item 5.05s related to updates and clarifications, and those related to combined or split codes, use fewer words and are more easily readable than those disclosing waivers or material code changes. Although we find no significant price reaction to Item 5.05 8‐K filings, we find significant trading volume and volatility reactions, suggesting that these disclosures could give rise to differences in opinion among investors, which is consistent with the US Senate's assertion that these disclosures are of interest to investors.

Geospatial vaccine misinformation risk on social media: Online insights from an English/Spanish natural language processing (NLP) analysis of vaccine-related tweets

BackgroundMisinformation is known to affect norms, attitudes, and intentions to engage with healthy behaviors. Evidence strongly supports that Spanish speakers may be particularly affected by misinformation and its outcomes, yet current insights into the scope and scale of misinformation is primarily ethnocentric, with greater emphasis on English-language design. ObjectiveThis study applies Natural Language Processing (NLP) to analyze a corpus of English/Spanish tweets about vaccines, broadly defined, for misinformation indicators. MethodsWe analyzed NEnglish = 247,140 and NSpanish = 104,445 tweets using Latent Dirichlet Allocation (LDA) topic models with Coherence score calculation (model fit) with a Mallet adjustment (topic optimization). We used informal coding to name computer-identified topics and compare misinformation scope and scale between languages. ResultsThe LDA analysis yielded a 12-topic solution for English and a 14-topic solution for Spanish. Both corpora contained overlapping misinformation, including uncertainty of research guiding policy recommendations or standing in support of antivax movements. However, the Spanish data were positioned in a global context, where misinformation was directed at government equity and disparate vaccine distribution. ConclusionOur findings support that misinformation is a global issue. However, misinformation may vary depending on culture and language. As such, tailored strategies to combat misinformation in digital planes are strongly encouraged.

Challenges of IT freelancers on digital labor platforms: A topic model approach

Freelancers working on digital labor platforms have to deal with many challenges. IT freelancers should be considered separately due to special characteristics such as high skill level or collaborative nature of IT work. Through a Latent Dirichlet Allocation analysis of 2804 forum posts with over 20,000 comments from IT freelancers and additional qualitative analysis, we assign themes to each identified topic, cluster them into aggregated dimensions, and illustrate the interrelationships in a model of IT freelancing. Thus, we examine the challenges of IT freelancers and synthesize them. We extend the challenges of online freelancing already outlined in the literature and identify four IT-specific challenges for IT freelancers. Therefore, we improve our understanding of how IT work is performed on digital labor platforms and which particular aspects should be considered in future research. Furthermore, we highlight implications for practitioners, i.e., IT freelancers on the one hand and platform owners on the other hand.

Artificial intelligence in higher education: a bibliometric analysis and topic modeling approach

ABSTRACT Artificial intelligence (AI) has brought unprecedented growth and productivity in every socioeconomic sector. AI adoption in education is transformational through reduced teacher workload, individualized learning, intelligent tutors, profiling and prediction, high-precision education, collaboration, and learner tracking. This paper highlights the trajectory of AI research in higher education (HE) through bibliometric analysis and topic modeling approaches. We used the PRISMA guidelines to select 304 articles published in the Scopus database between 2012 and 2021. VOSviewer was used for visualization and text-mining to identify hotspots in the field. Latent Dirichlet Allocation analysis reveals distinct topics in the dynamic relationship between AI and HE. Only 9.6% of AI research in HE was achieved in the first seven years, with the last three years contributing 90.4%. China, the United States, Russia and the United Kingdom dominated publications. Four themes emerged – data as the catalyst, the development of AI, the adoption of AI in HE and emerging trends and the future of AI in HE. Topic modeling on the abstracts revealed the 10 most frequent topics and the top 30 most salient terms. This research contributes to the literature by synthesizing AI adoption opportunities in HE, topic modeling and future research areas.

Eliciting Insights From Chat Logs of the 25X5 Symposium to Reduce Documentation Burden: Novel Application of Topic Modeling.

Addressing clinician documentation burden through "targeted solutions" is a growing priority for many organizations ranging from government and academia to industry. Between January and February 2021, the 25 by 5: Symposium to Reduce Documentation Burden on US Clinicians by 75% (25X5 Symposium) convened across 2 weekly 2-hour sessions among experts and stakeholders to generate actionable goals for reducing clinician documentation over the next 5 years. Throughout this web-based symposium, we passively collected attendees' contributions to a chat functionality-with their knowledge that the content would be deidentified and made publicly available. This presented a novel opportunity to synthesize and understand participants' perceptions and interests from chat messages. We performed a content analysis of 25X5 Symposium chat logs to identify themes about reducing clinician documentation burden. The objective of this study was to explore unstructured chat log content from the web-based 25X5 Symposium to elicit latent insights on clinician documentation burden among clinicians, health care leaders, and other stakeholders using topic modeling. Across the 6 sessions, we captured 1787 messages among 167 unique chat participants cumulatively; 14 were private messages not included in the analysis. We implemented a latent Dirichlet allocation (LDA) topic model on the aggregated dataset to identify clinician documentation burden topics mentioned in the chat logs. Coherence scores and manual examination informed optimal model selection. Next, 5 domain experts independently and qualitatively assigned descriptive labels to model-identified topics and classified them into higher-level categories, which were finalized through a panel consensus. We uncovered ten topics using the LDA model: (1) determining data and documentation needs (422/1773, 23.8%); (2) collectively reassessing documentation requirements in electronic health records (EHRs) (252/1773, 14.2%); (3) focusing documentation on patient narrative (162/1773, 9.1%); (4) documentation that adds value (147/1773, 8.3%); (5) regulatory impact on clinician burden (142/1773, 8%); (6) improved EHR user interface and design (128/1773, 7.2%); (7) addressing poor usability (122/1773, 6.9%); (8) sharing 25X5 Symposium resources (122/1773, 6.9%); (9) capturing data related to clinician practice (113/1773, 6.4%); and (10) the role of quality measures and technology in burnout (110/1773, 6.2%). Among these 10 topics, 5 high-level categories emerged: consensus building (821/1773, 46.3%), burden sources (365/1773, 20.6%), EHR design (250/1773, 14.1%), patient-centered care (162/1773, 9.1%), and symposium comments (122/1773, 6.9%). We conducted a topic modeling analysis on 25X5 Symposium multiparticipant chat logs to explore the feasibility of this novel application and elicit additional insights on clinician documentation burden among attendees. Based on the results of our LDA analysis, consensus building, burden sources, EHR design, and patient-centered care may be important themes to consider when addressing clinician documentation burden. Our findings demonstrate the value of topic modeling in discovering topics associated with clinician documentation burden using unstructured textual content. Topic modeling may be a suitable approach to examine latent themes presented in web-based symposium chat logs.

Patients' perspectives on irritable bowel syndrome: a qualitative analysis based on social media in China.

To explore the perspectives, experience, and concerns of patients with irritable bowel syndrome (IBS) in China. We used data mining to investigate posts shared in Baidu Tieba concerned with IBS; we collected the data through the crawler code, and mined the cleaned data's themes based on Latent Dirichlet allocation (LDA) and the Grounded theory. We found 5746 network posts related to IBS. LDA analysis generated 20 topics, and grounded theory analysis established eight topics. Combining the two methods, we finally arranged the topics according to five concepts: difficulty in obtaining disease information; serious psychosocial problems; dissatisfied with the treatment; lack of social support; and low quality of life. Social media research improved patient-centric understanding of patients' experiences and perceptions. Our study may facilitate doctor-patient communication and assist in the formulation of medical policies.

Identifying memorable hotel experiences: analysis of TripAdvisor reviews

Abstract This article aims to (1) identify guests’ memorable experiences based on reviews posted on TripAdvisor, (2) identify the differences in memorable experiences due to hotel location, evaluation on TripAdvisor and consumer sentiment. The study used quantitative methods: text mining, topic modelling, and sentiment analysis. All reviews (n = 34,992) for all Warsaw hotels included on TripAdvisor (N = 99) were analysed. Seven topics of memorable experiences were identified via Latent Dirichlet Allocation analysis: five were very positive and two very negative. The content analysis of those topics allowed us to extract six positive factors which include: (1) view from the floor, (2) hotel staff and service, (3) breakfast and restaurant, (4) location and atmosphere, (5) pool, lounge, gym and spa, (6) price (value for money), as well as two negative factors: (1) noise at night, (2) booking and check-in. In addition, it was found that memorable hotel experiences differ depending on the location of the hotel in the city.

Reddit Users' Questions and Concerns about Anesthesia

Background. Patients utilize social media in search of support networks. Reddit is one of the most popular social media sites and allows users to anonymously connect. Anesthesia patients are actively using Reddit to discuss their treatment options and experiences within the medical system. Methods. Posts published on an active Reddit forum on Anesthesia (i.e., /r/Anesthesia) were used. Big Query was used to collect posts from /r/Anesthesia. We collected 3,288 posts published between December 2015 and August 2019. We collected a control group of 3,288 posts from a Reddit forum not related to Anesthesia. Using latent Dirichlet allocation (LDA) we extracted 20 topics from our data set. The LDA topic themes most associated with posts in /r/Anesthesia compared to the control group were determined. Results. LDA analysis of posts in /r/Anesthesia relative to a control group produced 6 distinct categories of posts (Table 1). The posts most associated with /r/Anesthesia when compared to a control group were posts belonging to the “Physician-Patient Experience” category (Cohen’s d= 0.389) while the posts least associated with /r/Anesthesia were from the “Uncertainties” category of posts (Cohen’s d= 0.147). Example experiences from members of the /r/Anesthesia forum highlight subjective experiences of patients undergoing anesthesiology. Conclusions. The language used on social media can provide insights into an individual's experience with anesthesia and inform physicians about patient concerns. Anesthesiologists are poised to address these concerns and prevent anonymous misinformation by providing verified physician insights on the forum /r/Anesthesia.

Reddit Users' Questions and Concerns about Anesthesia

Background: Patients utilize social media in search of support networks. Reddit is one of the most popular social media sites and allows users to anonymously connect. Anesthesia patients are actively using Reddit to discuss their treatment options and experiences within the medical system. Methods: Posts published on an active Reddit forum on Anesthesia (i.e., /r/Anesthesia) were used. Big Query was used to collect posts from /r/Anesthesia. We collected 3,288 posts published between December 2015 and August 2019. We collected a control group of 3,288 posts from a Reddit forum not related to Anesthesia. Using latent Dirichlet allocation (LDA) we extracted 20 topics from our data set. The LDA topic themes most associated with posts in /r/Anesthesia compared to the control group were determined. Results: LDA analysis of posts in /r/Anesthesia relative to a control group produced 6 distinct categories of posts (Table 1). The posts most associated with /r/Anesthesia when compared to a control group were posts belonging to the “Physician-Patient Experience” category (Cohen’s d= 0.389) while the posts least associated with /r/Anesthesia were from the “Uncertainties” category of posts (Cohen’s d= 0.147). Example experiences from members of the /r/Anesthesia forum highlight subjective experiences of patients undergoing anesthesia. Conclusions: The language used on social media can provide insights into an individual's experience with anesthesia and inform physicians about patient concerns. Anesthesiologists are poised to address these concerns and prevent anonymous misinformation by providing verified physician insights on the forum /r/Anesthesia.

Exploring the research landscape of the past, present, and future of thyroid nodules.

The purpose of this study was to assess the landscape of thyroid nodules research during the last 22 years using machine learning and text analysis. In November 2021, we obtained from PubMed all works indexed under the Medical Subject Headings (MeSH) subject line "thyroid nodules." The entire set of search results was retrieved in XML format, and metadata such as title, abstract, keywords, MeSH words, and year of publication were extracted for bibliometric evaluation from the original XML files. To increase the specificity of the investigation, the Latent Dirichlet allocation (LDA) topic modeling method was applied. Our study included 5,770 research papers. By using frequency analysis of MeSH terms, research on thyroid nodules was divided into two categories: clinical and basic. The proportion of clinical research is nearing 89% and is dominated by the differential diagnosis of thyroid nodules. In contrast, the proportion of MeSH terms relating to basic research was just 11%, with DNA mutation analysis being the most common topic. Following this, LDA analysis revealed the thyroid nodule study had three clusters: Imaging Studies, Biopsy and Diagnosis, and Epidemiology and Screening of Thyroid Cancer. The result suggests that current thyroid nodule research appears to have focused on ultrasonography and histological diagnosis, which are tightly correlated. Molecular biomarker research has increased, therefore enhancing the diagnostic precision of thyroid nodules. However, inflammation, anxiety, and mental health disorders related to thyroid nodules have received little attention. Basic research on thyroid nodules has unmet research requirements. Future research could focus on developing strategies to more efficiently identify malignant nodules, exploring the mechanism of thyroid nodule development, and enhancing the quality of life of thyroid patients.