Refueling a Quiet Fire: Old Truthers and New Discontent in the Wake of COVID-19.

This article investigates the factors that contributed to the proliferation of online COVID skepticism on Twitter across Italian municipalities in 2020. We demonstrate that sociodemographic factors were likely to mitigate the emergence of skepticism, whereas populist political leanings were more likely to foster it. Furthermore, pre-COVID anti-vaccine sentiment, represented by "old truthers" on Twitter, amplified online COVID skepticism in local communities. Additionally, exploiting the spatial variation in restrictive economic policies with severe implications for suspended workers in nonessential economic sectors, we find that COVID skepticism spreads more in municipalities significantly affected by the economic lockdown. Finally, the diffusion of COVID skepticism is positively associated with COVID vaccine hesitancy.

Exploring Public Emotions on Obesity During the COVID-19 Pandemic Using Sentiment Analysis and Topic Modeling: Cross-Sectional Study.

BACKGROUND: Obesity is a chronic, multifactorial, and relapsing disease, affecting people of all ages worldwide, and is directly related to multiple complications. Understanding public attitudes and perceptions toward obesity is essential for developing effective health policies, prevention strategies, and treatment approaches. OBJECTIVE: This study investigated the sentiments of the general public, celebrities, and important organizations regarding obesity using social media data, specifically from Twitter (subsequently rebranded as X). METHODS: The study analyzes a dataset of 53,414 tweets related to obesity posted on Twitter during the COVID-19 pandemic, from April 2019 to December 2022. Sentiment analysis was performed using the XLM-RoBERTa-base model, and topic modeling was conducted using the BERTopic library. RESULTS: The analysis revealed that tweets regarding obesity were predominantly negative. Spikes in Twitter activity correlated with significant political events, such as the exchange of obesity-related comments between US politicians and criticism of the United Kingdom's obesity campaign. Topic modeling identified 243 clusters representing various obesity-related topics, such as childhood obesity; the US President's obesity struggle; COVID-19 vaccinations; the UK government's obesity campaign; body shaming; racism and high obesity rates among Black American people; smoking, substance abuse, and alcohol consumption among people with obesity; environmental risk factors; and surgical treatments. CONCLUSIONS: Twitter serves as a valuable source for understanding obesity-related sentiments and attitudes among the public, celebrities, and influential organizations. Sentiments regarding obesity were predominantly negative. Negative portrayals of obesity by influential politicians and celebrities were shown to contribute to negative public sentiments, which can have adverse effects on public health. It is essential for public figures to be mindful of their impact on public opinion and the potential consequences of their statements.

TRACKING THE SHIFT IN PUBLIC DISCOURSE ON FOREST AND LAND FIRES IN INDONESIA THROUGH SOCIAL MEDIA: A SIX-YEAR LONGITUDINAL STUDY.

This study analyzes the distribution of content posted on Twitter in response to a specific event or crisis from 2014 to 2019. The aim of the study is to identify any shifts in the focus of the content and to explore the possible reasons for these changes. The findings suggest a shift from a disaster arena to a political arena over the six-year period. The initial years were dominated by content related to reporting on the situation, requesting help, and coordinating relief efforts, while the latter years saw an increase in content related to criticizing the government, appreciating government effort, and discussing social and political issues. The study provides insights into the changing nature of public responses to events and crises, and highlights the role of social media as a platform for political discussions.

Nous avons analysé les Tweets concernant un événement ou une crise au long des années 2014 à 2019. Le but était de rechercher des changements de réaction et, s'il y en avait, d'en rechercher les causes possibles. Il semble y avoir un glissement de l'approche catastrophique vers un point de vue politique. Dans les premières années, les contenus relataient la situation, demandaient de l'aide et essayaient de coordonner la reconstruction quand par la suite ils critiquaient ou félicitaient le gouvernement et discutaient des problèmes sociaux et politiques. Cette étude montre le changement des commentaires du public en cas de crise et souligne l'intérêt de l'espace de discussion offert par les réseaux sociaux.

Sentiment analysis using graph-based Quickprop method for product quality enhancement.

The degree to which customers express satisfaction with a product on Twitter and other social media platforms is increasingly used to evaluate product quality. However, the volume and variety of textual data make traditional sentiment analysis methods challenging. The nuanced and context-dependent nature of product-related opinions presents a challenge for existing tools. This research addresses this gap by utilizing complex graph-based modelling strategies to capture the intricacies of real-world data. The Graph-based Quickprop Method constructs a graph model using the Sentiment140 dataset with 1.6 million tweets, where individuals are nodes and interactions are edges. Experimental results show a significant increase in sentiment classification accuracy, demonstrating the method's efficacy. This contribution underscores the importance of relational structures in sentiment analysis and provides a robust framework for extracting actionable insights from user-generated content, leading to improved product quality evaluations. The GQP-PQE method advances sentiment analysis and offers practical implications for businesses seeking to enhance product quality through a better understanding of consumer feedback on social media.

Database of twitter influencers in cryptocurrency (2021-2023) with sentiments.

OBJECTIVES: With the expansion of social networks such as Twitter, many experts share their opinions on various topics. The opinions of experts, who are also known as influencers, can be very influential. Combining these tweets and the historical prices of cryptocurrencies makes it possible to predict their price trends accurately. A Hybrid of RoBERTa deep neural network and BiGRU has been used for Sentiment Analysis (SA). Sentiments of tweets can be of great help to investors to understand the future behavior of the market and manage the stock portfolio. Unlike the tweets that are only extracted using the cryptocurrency name hashtag, the tweets of this dataset have specialized opinions and can determine the market trend. DATA DESCRIPTION: The dataset created in this research concerns the opinions of more than 52 influencers (persons or companies) regarding eight cryptocurrencies. This dataset was collected through the Apify Twitter API for eight months, from February 2021 to June 2023. This dataset contains five Excel files and tweets, compound score, importance coefficient of each tweet, sentiment polarity, and historical prices of four cryptocurrencies: Bitcoin, Ethereum, Binance, and other information. These tweets cover the opinions of 52 influencers on more than 300 cryptocurrencies, although most comments are related to Bitcoin, Ethereum, and Binance. For this reason, three Excel files containing the historical prices of polarity and compound sentiment related to Bitcoin, Ethereum, and Binance cryptocurrencies have been placed separately in the dataset. The polarity of sentiment in these Excel shows the maximum number of polarities by applying the importance coefficient, which determines the dominant polarity of sentiment related to a particular day for the cryptocurrency.

Assessment of beliefs and attitudes towards benzodiazepines using machine learning based on social media posts: an observational study.

BACKGROUND: Benzodiazepines are frequently prescribed drugs; however, their prolonged use can lead to tolerance, dependence, and other adverse effects. Despite these risks, long-term use remains common, presenting a public health concern. This study aims to explore the beliefs and opinions held by the public regarding benzodiazepines, as understanding these perspectives may provide insights into their usage patterns. METHODS: We collected public tweets published in English between January 1, 2019, and October 31, 2020, that mentioned benzodiazepines. The content of each tweet and the characteristics of the users were analyzed using a mixed-method approach, including manual analysis and semi-supervised machine learning. RESULTS: Over half of the Twitter users highlighted the efficacy of benzodiazepines, with minimal discussion of their side effects. The most active participants in these conversations were patients and their families, with health professionals and institutions being notably absent. Additionally, the drugs most frequently mentioned corresponded with those most commonly prescribed by healthcare professionals. CONCLUSIONS: Social media platforms offer valuable insights into users' experiences and opinions regarding medications. Notably, the sentiment towards benzodiazepines is predominantly positive, with users viewing them as effective while rarely mentioning side effects. This analysis underscores the need to educate physicians, patients, and their families about the potential risks associated with benzodiazepine use and to promote clinical guidelines that support the proper management of these medications. CLINICAL TRIAL NUMBER: Not applicable.

Discussion of Heated Tobacco Products on Twitter Following IQOS's Modified-Risk Tobacco Product Authorization and US Import Ban: Content Analysis.

BACKGROUND: Understanding public opinions about emerging tobacco products is important to inform future interventions and regulatory decisions. Heated tobacco products (HTPs) are an emerging tobacco product category promoted by the tobacco industry as a "better alternative" to combustible cigarettes. Philip Morris International's IQOS is leading the global HTP market and recently has been subject to important policy events, including the US Food and Drug Administration's (FDA) modified-risk tobacco product (MRTP) authorization (July 2020) and the US import ban (November 2021). Although limited in their legal implications outside the United States, these policy events have been quoted in global news outlets and Philip Morris International's promotional communications, showing how they may potentially impact global tobacco regulation. Given the impending return of IQOS to the US market, understanding how the policy events were received through social media discourse will provide valuable insights to inform global tobacco control policy. OBJECTIVE: This study aims to examine HTP-related social media discourse around important policy events. METHODS: We analyzed HTP-related posts on Twitter during the time period that included IQOS's MRTP authorization in the United States and the US import ban, examining personal testimonial, news/information, and direct marketing/retail tweets separately. We also examined how the tweets discussed health and policy. A total of 10,454 public English tweets (posted from June 2020 to December 2021) were collected using HTP-related keywords. We randomly sampled 2796 (26.7%) tweets and conducted a content analysis. We used pairwise co-occurrence analyses to evaluate connections across themes. RESULTS: Tweet volumes peaked around IQOS-related policy events. Among all tweets, personal testimonials were the most common (1613/2796, 57.7%), followed by news/information (862/2796, 30.8%) and direct marketing/retail (321/2796, 11%). Among personal testimonials, more tweets were positive (495/1613, 30.7%) than negative (372/1613, 23.1%), often comparing the health risks of HTPs with cigarettes (402/1613, 24.9%) or vaping products (252/1613, 15.6%). Approximately 10% (31/321) of the direct marketing/retail tweets promoted international delivery, suggesting cross-border promotion. More than a quarter of tweets (809/2796, 28.9%) discussed US and global policy, including misinterpretation about IQOS being a "safer" tobacco product after the US FDA's MRTP authorization. Neutral testimonials mentioning the IQOS brand (634/1613, 39.3%) and discussing policy (378/1613, 23.4%) showed the largest pairwise co-occurrence. CONCLUSIONS: Results suggest the need for careful communication about the meaning of MRTP authorizations and relative risks of tobacco products. Many tweets expressed HTP-favorable opinions referring to reduced health risks, even though the US FDA has denied marketing of the HTP with reduced risk claims. The popularity of social media as an information source with global reach poses unique challenges in health communication and health policies. While many countries restrict tobacco marketing via the web, our results suggest that retailers may circumvent such regulations by operating overseas.

Disclosure Patterns of Opioid Use Disorders in Perinatal Care During the Opioid Epidemic on X From 2019 to 2021: Thematic Analysis.

BACKGROUND: In 2021, the United States experienced a 14% rise in fatal drug overdoses totaling 106,699 deaths, driven by harmful opioid use, particularly among individuals in the perinatal period who face increased risks associated with opioid use disorders (OUDs). Increased concerns about the impacts of escalating harmful opioid use among pregnant and postpartum persons are rising. Most of the current limited perinatal OUD studies were conducted using traditional methods, such as interviews and randomized controlled trials to understand OUD treatment, risk factors, and associated adverse effects. However, little is known about how social media data, such as X, formerly known as Twitter, can be leveraged to explore and identify broad perinatal OUD trends, disclosure and communication patterns, and public health surveillance about OUD in the perinatal period. OBJECTIVE: The objective is 3-fold: first, we aim to identify key themes and trends in perinatal OUD discussions on platform X. Second, we explore user engagement patterns, including replying and retweeting behaviors. Third, we investigate computational methods that could potentially streamline and scale the labor-intensive manual annotation effort. METHODS: We extracted 6 million raw perinatal-themed tweets posted by global X users during the opioid epidemic from May 2019 to October 2021. After data cleaning and sampling, we used 500 tweets related to OUD in the perinatal period by US X users for a thematic analysis using NVivo (Lumivero) software. RESULTS: Seven major themes emerged from our thematic analysis: (1) political views related to harmful opioid and other substance use, (2) perceptions of others' substance use, (3) lived experiences of opioid and other substance use, (4) news reports or papers related to opioid and other substance use, (5) health care initiatives, (6) adverse effects on children's health due to parental substance use, and (7) topics related to nonopioid substance use. Among these 7 themes, our user engagement analysis revealed that themes 4 and 5 received the highest average retweet counts, and theme 3 received the highest average tweet reply count. We further found that different computational methods excel in analyzing different themes. CONCLUSIONS: Social media platforms such as X can serve as a valuable tool for analyzing real-time discourse and exploring public perceptions, opinions, and behaviors related to maternal substance use, particularly, harmful opioid use in the perinatal period. More health promotion strategies can be carried out on social media platforms to provide educational support for the OUD perinatal population.

Tree hierarchical deep convolutional neural network optimized with sheep flock optimization algorithm for sentiment classification of Twitter data.

The increasing volume of online reviews and tweets poses significant challenges for sentiment classification because of the difficulty in obtaining annotated training data. This paper aims to enhance sentiment classification of Twitter data by developing a robust model that improves classification accuracy and computational efficiency. The proposed method named Tree Hierarchical Deep Convolutional Neural Network optimized with Sheep Flock Optimization Algorithm for Sentiment Classification of Twitter Data (SCTD-THDCNN-SFOA) utilizes the Stanford Sentiment Treebank dataset. The process begins with pre-processing steps including Tokenization, Stop words Elimination, Filtering, Hashtag Removal, and Multiword Grouping. The Gray Level Co-occurrence Matrix Window Adaptive Algorithm is employed to extract features, such as emoticon counts, punctuation counts, gazetteer word existence, n-grams, and part of speech tags. These features are selected using Entropy-Kurtosis-based Feature Selection approach. Finally, the Tree Hierarchical Deep Convolutional Neural Network enhanced by the Sheep Flock Optimization Algorithm is used to categorize the Twitter data as positive, negative, and neutral sentiments. The proposed SCTD-THDCNN-SFOA method demonstrates superior performance, achieving higher accuracy and lesser computation time than the existing models, respectively. The SCTD-THDCNN-SFOA framework significantly improves the accuracy and efficiency of sentiment classification for Twitter data.

Tracking the Spread of Pollen on Social Media Using Pollen-Related Messages From Twitter: Retrospective Analysis.

BACKGROUND: Allergy disorders caused by biological particles, such as the proteins in some airborne pollen grains, are currently considered one of the most common chronic diseases, and European Academy of Allergy and Clinical Immunology forecasts indicate that within 15 years 50% of Europeans will have some kind of allergy as a consequence of urbanization, industrialization, pollution, and climate change. OBJECTIVE: The aim of this study was to monitor and analyze the dissemination of information about pollen symptoms from December 2006 to January 2022. By conducting a comprehensive evaluation of public comments and trends on Twitter, the research sought to provide valuable insights into the impact of pollen on sensitive individuals, ultimately enhancing our understanding of how pollen-related information spreads and its implications for public health awareness. METHODS: Using a blend of large language models, dimensionality reduction, unsupervised clustering, and term frequency-inverse document frequency, alongside visual representations such as word clouds and semantic interaction graphs, our study analyzed Twitter data to uncover insights on respiratory allergies. This concise methodology enabled the extraction of significant themes and patterns, offering a deep dive into public knowledge and discussions surrounding respiratory allergies on Twitter. RESULTS: The months between March and August had the highest volume of messages. The percentage of patient tweets appeared to increase notably during the later years, and there was also a potential increase in the prevalence of symptoms, mainly in the morning hours, indicating a potential rise in pollen allergies and related discussions on social media. While pollen allergy is a global issue, specific sociocultural, political, and economic contexts mean that patients experience symptomatology at a localized level, needing appropriate localized responses. CONCLUSIONS: The interpretation of tweet information represents a valuable tool to take preventive measures to mitigate the impact of pollen allergy on sensitive patients to achieve equity in living conditions and enhance access to health information and services.

Public Response to Federal Electronic Cigarette Regulations Analyzed Using Social Media Data Through Natural Language Processing: Topic Modeling Study.

BACKGROUND: e-Cigarette (electronic cigarette) use has been a public health issue in the United States. On June 23, 2022, the US Food and Drug Administration (FDA) issued marketing denial orders (MDOs) to Juul Labs Inc for all their products currently marketed in the United States. However, one day later, on June 24, 2022, a federal appeals court granted a temporary reprieve to Juul Labs that allowed it to keep its e-cigarettes on the market. As the conversation around Juul continues to evolve, it is crucial to gain insights into the sentiments and opinions expressed by individuals on social media. OBJECTIVE: This study aims to conduct a comprehensive analysis of tweets before and after the ban on Juul, aiming to shed light on public perceptions and sentiments surrounding this contentious topic and to better understand the life cycle of public health-related policy on social media. METHODS: Natural language processing (NLP) techniques were used, including state-of-the-art BERTopic topic modeling and sentiment analysis. A total of 6023 tweets and 22,288 replies or retweets were collected from Twitter (rebranded as X in 2023) between June 2022 and October 2022. The encoded topics were used in time-trend analysis to depict the boom-and-bust cycle. Content analyses of retweets were also performed to better understand public perceptions and sentiments about this contentious topic. RESULTS: The attention surrounding the FDA's ban on Juul lasted no longer than a week on Twitter. Not only the news (ie, tweets with a YouTube link that directs to the news site) related to the announcement itself, but the surrounding discussions (eg, potential consequences of this ban or block and concerns toward kids or youth health) diminished shortly after June 23, 2022, the date when the ban was officially announced. Although a short rebound was observed on July 4, 2022, which was contributed by the suspension on the following day, discussions dried out in 2 days. Out of the top 50 most retweeted tweets, we observed that, except for neutral (23/45, 51%) sentiment that broadcasted the announcement, posters responded more negatively (19/45, 42%) to the FDA's ban. CONCLUSIONS: We observed a short life cycle for this news announcement, with a preponderance of negative sentiment toward the FDA's ban on Juul. Policy makers could use tactics such as issuing ongoing updates and reminders about the ban, highlighting its impact on public health, and actively engaging with influential social media users who can help maintain the conversation.

The Societal Impact of the Papers Published in Blended Learning in Medicine on Social Network Sites: An Altmetrics Study.

Background: Nowadays, blended learning in medicine (BLM) has gained the attention of most experts as an invaluable approach to improving the quality of medical education. The level of attention to articles in this field on social networks is substantial. This study aimed to study the effectiveness of published articles in blended learning, indexed in Scopus and Web of Science databases between 2013 and 2022, from an altmetrics perspective. Methods: The research is descriptive-analytical, with a scientometrics approach (using the Altmetrics index). The population includes all the articles on blended learning in medicine, indexed in Scopus and Web of Science databases, two well-known citation databases worldwide. Data were extracted using the Altmetrics bookmarklet tool and analyzed with descriptive statistics methods in Excel software. Results: Out of 1327 articles, 136 articles (10.25%) did not have a digital object identifier (DOI) or PMID number. Mendeley, X (previously Twitter), and Dimensions were the most widely used social networks in blended learning. The United States, the United Kingdom, and Australia had the highest number of tweets in blended learning in medicine. Conclusion: The number of articles with altmetrics indicators, categorized by publication year, demonstrates an improvement in the familiarity and use of social media by blended learning researchers in medicine. Blended learning researchers are advised to carefully select reputable journals - preferably with DOI - to increase the visibility and attention to their articles on social media.

Uncovering the Complexity of Perinatal Polysubstance Use Disclosure Patterns on X: Mixed Methods Study.

BACKGROUND: According to the Morbidity and Mortality Weekly Report, polysubstance use among pregnant women is prevalent, with 38.2% of those who consume alcohol also engaging in the use of one or more additional substances. However, the underlying mechanisms, contexts, and experiences of polysubstance use are unclear. Organic information is abundant on social media such as X (formerly Twitter). Traditional quantitative and qualitative methods, as well as natural language processing techniques, can be jointly used to derive insights into public opinions, sentiments, and clinical and public health policy implications. OBJECTIVE: Based on perinatal polysubstance use (PPU) data that we extracted on X from May 1, 2019, to October 31, 2021, we proposed two primary research questions: (1) What is the overall trend and sentiment of PPU discussions on X? (2) Are there any distinct patterns in the discussion trends of PPU-related tweets? If so, what are the implications for perinatal care and associated public health policies? METHODS: We used X's application programming interface to extract >6 million raw tweets worldwide containing ≥2 prenatal health- and substance-related keywords provided by our clinical team. After removing all non-English-language tweets, non-US tweets, and US tweets without disclosed geolocations, we obtained 4848 PPU-related US tweets. We then evaluated them using a mixed methods approach. The quantitative analysis applied frequency, trend analysis, and several natural language processing techniques such as sentiment analysis to derive statistics to preview the corpus. To further understand semantics and clinical insights among these tweets, we conducted an in-depth thematic content analysis with a random sample of 500 PPU-related tweets with a satisfying κ score of 0.7748 for intercoder reliability. RESULTS: Our quantitative analysis indicates the overall trends, bigram and trigram patterns, and negative sentiments were more dominant in PPU tweets (2490/4848, 51.36%) than in the non-PPU sample (1323/4848, 27.29%). Paired polysubstance use (4134/4848, 85.27%) was the most common, with the combination alcohol and drugs identified as the most mentioned. From the qualitative analysis, we identified 3 main themes: nonsubstance, single substance, and polysubstance, and 4 subthemes to contextualize the rationale of underlying PPU behaviors: lifestyle, perceptions of others' drug use, legal implications, and public health. CONCLUSIONS: This study identified underexplored, emerging, and important topics related to perinatal PPU, with significant stigmas and legal ramifications discussed on X. Overall, public sentiments on PPU were mixed, encompassing negative (2490/4848, 51.36%), positive (1884/4848, 38.86%), and neutral (474/4848, 9.78%) sentiments. The leading substances in PPU were alcohol and drugs, and the normalization of PPU discussed on X is becoming more prevalent. Thus, this study provides valuable insights to further understand the complexity of PPU and its implications for public health practitioners and policy makers to provide proper access and support to individuals with PPU.

Japanese Perception of Brain Death and Implications for New Medical Technologies: Quantitative and Qualitative Social Media Analysis.

BACKGROUND: Brain death has been used to decide whether to keep sustained care and treatment. It can facilitate tissue, organ, and body donation for several purposes, such as transplantation and medical education and research. In Japan, brain death has strict diagnostic criteria and family consent is crucial, but it has been a challenging concept for the public since its introduction, including knowledge and communication issues. OBJECTIVE: We analyzed data across YouTube and Twitter in Japan to uncover actors and assess the quality of brain death communication, providing recommendations to communicate new medical technologies. METHODS: Using the keyword "" (brain death), we collected recent data from YouTube and Twitter, classifying the data into 5 dimensions: time, individuality (type of users), place, activity, and relations (hyperlinks). We employed a scale to evaluate brain death information quality. We divided YouTube videos into 3 groups and assessed their differences through statistical analysis. We also provided a text-based analysis of brain death-related narratives. RESULTS: Most videos (20/61, 33%) were uploaded in 2019, while 10,892 tweets peaked between July 3 and 9, 2023, and June 12 and 18, 2023. Videos about brain death were mostly uploaded by citizens (18/61, 27%), followed by media (13/61, 20%) and unknown actors (10/61, 15%). On the other hand, most identified users in a random sample of 100 tweets were citizens (73/100, 73%), and the top 10 retweeted and liked tweets were also mostly authored by citizens (75/100, 75%). No specific information on location was uncovered. Information videos contained guides for accreditation of the National Nursing Exam and religious points of view, while misinformation videos mostly contained promotions by spirituality actors and webtoon artists. Some tweets involved heart transplantation and patient narratives. Most hyperlinks pointed to YouTube and Twitter. CONCLUSIONS: Brain death has become a common topic in everyday life, with some actors disseminating high-quality information, others disseminating no medical information, and others disseminating misinformation. Recommendations include partnering with interested actors, discussing medical information in detail, and teaching people to recognize pseudoscience.

Public Perceptions of the Food and Drug Administration's Regulatory Authority Over Synthetic Nicotine on Twitter: Observational Study.

BACKGROUND: The Omnibus Budget Bill, known as H. R. 2471, passed through Congress on March 10, 2022, and was eventually signed by President Biden on March 15, 2022. This bill amended the Federal Food, Drug, and Cosmetic Act granting the Food and Drug Administration (FDA) regulatory authority over synthetic nicotine. OBJECTIVE: This study aims to examine the public perceptions of the Omnibus Bill that regulates synthetic nicotine products as tobacco products on Twitter (rebranded as X). METHODS: Through the X streaming application programming interface, we collected and identified 964 tweets related to the Omnibus Bill on synthetic nicotine between March 8, 2022, and April 13, 2022. The longitudinal trend was used to examine the discussions related to the bill over time. An inductive method was used for the content analysis of related tweets. By hand-coding 200 randomly selected tweets by 2 human coders respectively with high interrater reliability, the codebook was developed for relevance, major topics, and attitude to the bill, which was used to single-code the rest of the tweets. RESULTS: Between March 8, 2022, and April 13, 2022, we identified 964 tweets related to the Omnibus Bill regulating synthetic nicotine. Our longitudinal trend analysis showed a spike in the number of tweets related to the bill during the immediate period following the bill's introduction, with roughly half of the tweets identified being posted between March 8 and 11, 2022. A majority of the tweets (497/964, 51.56%) had a negative sentiment toward the bill, while a much smaller percentage of tweets (164/964, 17.01%) had a positive sentiment toward the bill. Around 31.43% (303/964) of all tweets were categorized as objective news or questions about the bill. The most popular topic for opposing the bill was users believing that this bill would lead users back to smoking (145/497, 29.18%), followed by negative implications for small vape businesses (122/497, 24.55%) and government or FDA mistrust (94/497, 18.91%). The most popular topic for supporting the bill was that this bill would take a dangerous tobacco product targeted at teens off the market (94/164, 57.32%). CONCLUSIONS: We observed a more negative sentiment toward the bill on X, largely due to users believing it would lead users back to smoking and negatively impact small vape businesses. This study provides insight into public perceptions and discussions of this bill on X and adds valuable information for future regulations on alternative nicotine products.

A novel hybrid deep learning IChOA-CNN-LSTM model for modality-enriched and multilingual emotion recognition in social media.

In the rapidly evolving field of artificial intelligence, the importance of multimodal sentiment analysis has never been more evident, especially amid the ongoing COVID-19 pandemic. Our research addresses the critical need to understand public sentiment across various dimensions of this crisis by integrating data from multiple modalities, such as text, images, audio, and videos sourced from platforms like Twitter. Conventional methods, which primarily focus on text analysis, often fall short in capturing the nuanced intricacies of emotional states, necessitating a more comprehensive approach. To tackle this challenge, our proposed framework introduces a novel hybrid model, IChOA-CNN-LSTM, which leverages Convolutional Neural Networks (CNNs) for precise image feature extraction, Long Short-Term Memory (LSTM) networks for sequential data analysis, and an Improved Chimp Optimization Algorithm for effective feature fusion. Remarkably, our model achieves an impressive accuracy rate of 97.8%, outperforming existing approaches in the field. Additionally, by integrating the GeoCoV19 dataset, we facilitate a comprehensive analysis that spans linguistic and geographical boundaries, enriching our understanding of global pandemic discourse and providing critical insights for informed decision-making in public health crises. Through this holistic approach and innovative techniques, our research significantly advances multimodal sentiment analysis, offering a robust framework for deciphering the complex interplay of emotions during unprecedented global challenges like the COVID-19 pandemic.

Exploring arteriovenous malformations patient sentiments through 1401 social media posts.

INTRODUCTION: Social media has allowed patients with rare diseases to connect and discuss their experiences with others online. This study analyzed various social media platforms to better understand the patient's perception of arteriovenous malformation. METHODS: Twitter, Instagram, and TikTok were searched to find posts about patients' experiences with arteriovenous malformations (AVM). Posts unrelated to the patient's experience were excluded. Posts were coded for the relevant themes related to their experience with the disease, as well as engagement, and gender. RESULTS: The most common theme was raising awareness about the condition (87.0%). Recounting symptoms (50.2%), spreading positivity (17.5%), and survival (8.3%) were other common themes. Other prevalent themes were pain (5.2%) and fear of a rare disease (3.5%). Approximately half of AVM-related Instagram (47.93%) and TikTok (52.94%) posts raised awareness about the condition. Most Instagram (67.75%) and TikTok (89.71%) posts focused on recovery and rehabilitation. Most TikTok posts discussed "survival" or "death" (57.35%), while the majority focused on spreading positivity (79.41%). Most posts were made by women (69.6%). Females were more likely than males to post about the scientific explanation of AVMs (p = 0.003). CONCLUSION: Social media allows patients across the country and the globe to discuss their experiences with uncommon diseases and connect with others. It also allows AVM patients to share their experiences with other patients and the public.

Sharing endodontic research on social media platforms: Is it effective? A 10-year timeline analysis.

The purpose of this study was to analyse the social media activity related to endodontic research over the last 10-years. All research articles published in endodontic journals listed in Scopus (Sc) published in 2012 and 2018 were included in our study. The Altmetric Attention Score (AAS), Twitter, and Facebook mentions were obtained for each article. Citation counts were extracted using two citation metrics: Google Scholar (GS) and Sc. Correlations between the AAS, the number of social media mentions, and citations were analysed using Spearman's rank order correlation coefficient. A multivariable Poisson log-linear regression analysis shows that papers mentioned on social media gain about 35% more citations in GS and 31% more citations in Sc. The academic citations per article on GS and Sc were positively correlated with the AAS. Our data suggest an increasing positive correlation between social media mentions and article citations over the years.

A dataset of the 2023 presidential election in Nigeria.

Nigeria operates under a multi-party system with more than 18 registered political parties. Since the return to democratic rule in 1999, the political scene has been predominantly dominated by two major parties: the People's Democratic Party (PDP) and the All Progressive Congress (APC). Recently, however, emerging parties like The Labour Party (LP) and the New Nigerian People's Party (NNPP) have started gaining traction. Social media has become a pivotal part of modern society. Twitter (now known as X) has emerged as a significant medium for news dissemination, public opinions expression, and emotional responses on various topics. Its ability to allow real-time sharing of views and experiences on current affairs and personal matters has made it a powerful tool in shaping and reflecting public sentiment. The use of Twitter in Nigeria exemplifies its role as a versatile medium for expressing thoughts and feelings, thereby generating a substantial amount of data for sentiment analysis. Deep Learning is a branch of Artificial intelligence that uses multiple layer techniques to extract features from data. It has the capacity to adequately recognize pattern from data to produce insights. There is a dynamic interplay among political developments, social media use, and sentiment analysis using deep learning. This interplay highlights the evolving nature of public discourse and opinion formation in Nigeria. People's opinions about the Nigeria's 2023 Presidential Election were obtained from Twitter using the Twitter API and Python. The dataset contains 364,867 tweets that can be used in predicting the outcome of future elections in Nigeria and for comparing the performances of different models and techniques of sentiment analysis. Sentiment analysis; Deep learning; Python; Twitter.

From Optional to Default　- Enhancing Article Viewership Through X (Formerly Twitter) Posting.

Background: The influence of a change to a default X summary posting strategy on article viewership has not been investigated. Methods and Results: We conducted a retrospective analysis of X-posting rates and journal viewership data for both the Circulation Journal and Circulation Reports from April 2022 to September 2023. Following protocol modifications in March 2023, there was a notable increase in the X-posting rate from 12.4% to 61.7%, along with an uptick in median access counts to article pages within 30 days, from 175 to 231.5. Conclusions: Trend analysis of journal viewership after a default X-posting strategy revealed an increase in viewer access.