Data and models for stance and premise detection in COVID-19 tweets: Insights from the Social Media Mining for Health (SMM4H) 2022 shared task.

The COVID-19 pandemic has sparked numerous discussions on social media platforms, with users sharing their views on topics such as mask-wearing and vaccination. To facilitate the evaluation of neural models for stance detection and premise classification, we organized the Social Media Mining for Health (SMM4H) 2022 Shared Task 2. This competition utilized manually annotated posts on three COVID-19-related topics: school closures, stay-at-home orders, and wearing masks. In this paper, we extend the previous work and present newly collected data on vaccination from Twitter to assess the performance of models on a different topic. To enhance the accuracy and effectiveness of our evaluation, we employed various strategies to aggregate tweet texts with claims, including models with feature-level (early) fusion and dual-view architectures from the SMM4H 2022 Task 2 leaderboard. Our primary objective was to create a valuable dataset and perform an extensive experimental evaluation to support future research in argument mining in the health domain.

Digital Epidemiology of Prescription Drug References on X (Formerly Twitter): Neural Network Topic Modeling and Sentiment Analysis.

BACKGROUND: Data from the social media platform X (formerly Twitter) can provide insights into the types of language that are used when discussing drug use. In past research using latent Dirichlet allocation (LDA), we found that tweets containing "street names" of prescription drugs were difficult to classify due to the similarity to other colloquialisms and lack of clarity over how the terms were used. Conversely, "brand name" references were more amenable to machine-driven categorization. OBJECTIVE: This study sought to use next-generation techniques (beyond LDA) from natural language processing to reprocess X data and automatically cluster groups of tweets into topics to differentiate between street- and brand-name data sets. We also aimed to analyze the differences in emotional valence between the 2 data sets to study the relationship between engagement on social media and sentiment. METHODS: We used the Twitter application programming interface to collect tweets that contained the street and brand name of a prescription drug within the tweet. Using BERTopic in combination with Uniform Manifold Approximation and Projection and k-means, we generated topics for the street-name corpus (n=170,618) and brand-name corpus (n=245,145). Valence Aware Dictionary and Sentiment Reasoner (VADER) scores were used to classify whether tweets within the topics had positive, negative, or neutral sentiments. Two different logistic regression classifiers were used to predict the sentiment label within each corpus. The first model used a tweet's engagement metrics and topic ID to predict the label, while the second model used those features in addition to the top 5000 tweets with the largest term-frequency-inverse document frequency score. RESULTS: Using BERTopic, we identified 40 topics for the street-name data set and 5 topics for the brand-name data set, which we generalized into 8 and 5 topics of discussion, respectively. Four of the general themes of discussion in the brand-name corpus referenced drug use, while 2 themes of discussion in the street-name corpus referenced drug use. From the VADER scores, we found that both corpora were inclined toward positive sentiment. Adding the vectorized tweet text increased the accuracy of our models by around 40% compared with the models that did not incorporate the tweet text in both corpora. CONCLUSIONS: BERTopic was able to classify tweets well. As with LDA, the discussion using brand names was more similar between tweets than the discussion using street names. VADER scores could only be logically applied to the brand-name corpus because of the high prevalence of non-drug-related topics in the street-name data. Brand-name tweets either discussed drugs positively or negatively, with few posts having a neutral emotionality. From our machine learning models, engagement alone was not enough to predict the sentiment label; the added context from the tweets was needed to understand the emotionality of a tweet.

A Typology of Social Media Use by Human Service Nonprofits: Mixed Methods Study.

BACKGROUND: Nonprofit organizations are increasingly using social media to improve their communication strategies with the broader population. However, within the domain of human service nonprofits, there is hesitancy to fully use social media tools, and there is limited scope among organizational personnel in applying their potential beyond self-promotion and service advertisement. There is a pressing need for greater conceptual clarity to support education and training on the varied reasons for using social media to increase organizational outcomes. OBJECTIVE: This study leverages the potential of Twitter (subsequently rebranded as X [X Corp]) to examine the online communication content within a sample (n=133) of nonprofit sexual assault (SA) centers in Canada. To achieve this, we developed a typology using a qualitative and supervised machine learning model for the automatic classification of tweets posted by these centers. METHODS: Using a mixed methods approach that combines machine learning and qualitative analysis, we manually coded 10,809 tweets from 133 SA centers in Canada, spanning the period from March 2009 to March 2023. These manually labeled tweets were used as the training data set for the supervised machine learning process, which allowed us to classify 286,551 organizational tweets. The classification model based on supervised machine learning yielded satisfactory results, prompting the use of unsupervised machine learning to classify the topics within each thematic category and identify latent topics. The qualitative thematic analysis, in combination with topic modeling, provided a contextual understanding of each theme. Sentiment analysis was conducted to reveal the emotions conveyed in the tweets. We conducted validation of the model with 2 independent data sets. RESULTS: Manual annotation of 10,809 tweets identified seven thematic categories: (1) community engagement, (2) organization administration, (3) public awareness, (4) political advocacy, (5) support for others, (6) partnerships, and (7) appreciation. Organization administration was the most frequent segment, and political advocacy and partnerships were the smallest segments. The supervised machine learning model achieved an accuracy of 63.4% in classifying tweets. The sentiment analysis revealed a prevalence of neutral sentiment across all categories. The emotion analysis indicated that fear was predominant, whereas joy was associated with the partnership and appreciation tweets. Topic modeling identified distinct themes within each category, providing valuable insights into the prevalent discussions surrounding SA and related issues. CONCLUSIONS: This research contributes an original theoretical model that sheds light on how human service nonprofits use social media to achieve their online organizational communication objectives across 7 thematic categories. The study advances our comprehension of social media use by nonprofits, presenting a comprehensive typology that captures the diverse communication objectives and contents of these organizations, which provide content to expand training and education for nonprofit leaders to connect and engage with the public, policy experts, other organizations, and potential service users.

Characterizing Twitter Content About HIV Pre-exposure Prophylaxis (PrEP) for Women: Qualitative Content Analysis.

BACKGROUND: HIV remains a persistent health problem in the United States, especially among women. Approved in 2012, HIV pre-exposure prophylaxis (PrEP) is a daily pill or bimonthly injection that can be taken by individuals at increased risk of contracting HIV to reduce their risk of new infection. Women who are at risk of HIV face numerous barriers to HIV services and information, underscoring the critical need for strategies to increase awareness of evidence-based HIV prevention methods, such as HIV PrEP, among women. OBJECTIVE: We aimed to identify historical trends in the use of Twitter hashtags specific to women and HIV PrEP and explore content about women and PrEP shared through Twitter. METHODS: This was a qualitative descriptive study using a purposive sample of tweets containing hashtags related to women and HIV PrEP from 2009 to 2022. Tweets were collected via Twitter's API. Each Twitter user profile, tweet, and related links were coded using content analysis, guided by the framework of the Health Belief Model (HBM) to generate results. We used a factor analysis to identify salient clusters of tweets. RESULTS: A total of 1256 tweets from 396 unique users were relevant to our study focus of content about PrEP specifically for women (1256/2908, 43.2% of eligible tweets). We found that this sample of tweets was posted mostly by organizations. The 2 largest groups of individual users were activists and advocates (61/396, 15.4%) and personal users (54/396, 13.6%). Among individual users, most were female (100/166, 60%) and American (256/396, 64.6%). The earliest relevant tweet in our sample was posted in mid-2014 and the number of tweets significantly decreased after 2018. We found that 61% (496/820) of relevant tweets contained links to informational websites intended to provide guidance and resources or promote access to PrEP. Most tweets specifically targeted people of color, including through the use of imagery and symbolism. In addition to inclusive imagery, our factor analysis indicated that more than a third of tweets were intended to share information and promote PrEP to people of color. Less than half of tweets contained any HBM concepts, and only a few contained cues to action. Lastly, while our sample included only tweets relevant to women, we found that the tweets directed to lesbian, gay, bisexual, transgender, queer (LGBTQ) audiences received the highest levels of audience engagement. CONCLUSIONS: These findings point to several areas for improvement in future social media campaigns directed at women about PrEP. First, future posts would benefit from including more theoretical constructs, such as self-efficacy and cues to action. Second, organizations posting on Twitter should continue to broaden their audience and followers to reach more people. Lastly, tweets should leverage the momentum and strategies used by the LGBTQ community to reach broader audiences and destigmatize PrEP use across all communities.

Investigating the Role of Nutrition in Enhancing Immunity During the COVID-19 Pandemic: Twitter Text-Mining Analysis.

BACKGROUND: The COVID-19 pandemic has brought to the spotlight the critical role played by a balanced and healthy diet in bolstering the human immune system. There is burgeoning interest in nutrition-related information on social media platforms like Twitter. There is a critical need to assess and understand public opinion, attitudes, and sentiments toward nutrition-related information shared on Twitter. OBJECTIVE: This study uses text mining to analyze nutrition-related messages on Twitter to identify and analyze how the general public perceives various food groups and diets for improving immunity to the SARS-CoV-2 virus. METHODS: We gathered 71,178 nutrition-related tweets that were posted between January 01, 2020, and September 30, 2020. The Correlated Explanation text mining algorithm was used to identify frequently discussed topics that users mentioned as contributing to immunity building against SARS-CoV-2. We assessed the relative importance of these topics and performed a sentiment analysis. We also qualitatively examined the tweets to gain a closer understanding of nutrition-related topics and food groups. RESULTS: Text-mining yielded 10 topics that users discussed frequently on Twitter, viz proteins, whole grains, fruits, vegetables, dairy-related, spices and herbs, fluids, supplements, avoidable foods, and specialty diets. Supplements were the most frequently discussed topic (23,913/71,178, 33.6%) with a higher proportion (20,935/23,913, 87.75%) exhibiting a positive sentiment with a score of 0.41. Consuming fluids (17,685/71,178, 24.85%) and fruits (14,807/71,178, 20.80%) were the second and third most frequent topics with favorable, positive sentiments. Spices and herbs (8719/71,178, 12.25%) and avoidable foods (8619/71,178, 12.11%) were also frequently discussed. Negative sentiments were observed for a higher proportion of avoidable foods (7627/8619, 84.31%) with a sentiment score of -0.39. CONCLUSIONS: This study identified 10 important food groups and associated sentiments that users discussed as a means to improve immunity. Our findings can help dieticians and nutritionists to frame appropriate interventions and diet programs.

Cervical Myelopathy and Social Media: Mixed Methods Analysis.

BACKGROUND: Degenerative cervical myelopathy (DCM) is a progressive neurologic condition caused by age-related degeneration of the cervical spine. Social media has become a crucial part of many patients' lives; however, little is known about social media use pertaining to DCM. OBJECTIVE: This manuscript describes the landscape of social media use and DCM in patients, caretakers, clinicians, and researchers. METHODS: A comprehensive search of the entire Twitter application programing interface database from inception to March 2022 was performed to identify all tweets about cervical myelopathy. Data on Twitter users included geographic location, number of followers, and number of tweets. The number of tweet likes, retweets, quotes, and total engagement were collected. Tweets were also categorized based on their underlying themes. Mentions pertaining to past or upcoming surgical procedures were recorded. A natural language processing algorithm was used to assign a polarity score, subjectivity score, and analysis label to each tweet for sentiment analysis. RESULTS: Overall, 1859 unique tweets from 1769 accounts met the inclusion criteria. The highest frequency of tweets was seen in 2018 and 2019, and tweets decreased significantly in 2020 and 2021. Most (888/1769, 50.2%) of the tweets' authors were from the United States, United Kingdom, or Canada. Account categorization showed that 668 of 1769 (37.8%) users discussing DCM on Twitter were medical doctors or researchers, 415 of 1769 (23.5%) were patients or caregivers, and 201 of 1769 (11.4%) were news media outlets. The 1859 tweets most often discussed research (n=761, 40.9%), followed by spreading awareness or informing the public on DCM (n=559, 30.1%). Tweets describing personal patient perspectives on living with DCM were seen in 296 (15.9%) posts, with 65 (24%) of these discussing upcoming or past surgical experiences. Few tweets were related to advertising (n=31, 1.7%) or fundraising (n=7, 0.4%). A total of 930 (50%) tweets included a link, 260 (14%) included media (ie, photos or videos), and 595 (32%) included a hashtag. Overall, 847 of the 1859 tweets (45.6%) were classified as neutral, 717 (38.6%) as positive, and 295 (15.9%) as negative. CONCLUSIONS: When categorized thematically, most tweets were related to research, followed by spreading awareness or informing the public on DCM. Almost 25% (65/296) of tweets describing patients' personal experiences with DCM discussed past or upcoming surgical interventions. Few posts pertained to advertising or fundraising. These data can help identify areas for improvement of public awareness online, particularly regarding education, support, and fundraising.

Results and Methodological Implications of the Digital Epidemiology of Prescription Drug References Among Twitter Users: Latent Dirichlet Allocation (LDA) Analyses.

BACKGROUND: Social media is an important information source for a growing subset of the population and can likely be leveraged to provide insight into the evolving drug overdose epidemic. Twitter can provide valuable insight into trends, colloquial information available to potential users, and how networks and interactivity might influence what people are exposed to and how they engage in communication around drug use. OBJECTIVE: This exploratory study was designed to investigate the ways in which unsupervised machine learning analyses using natural language processing could identify coherent themes for tweets containing substance names. METHODS: This study involved harnessing data from Twitter, including large-scale collection of brand name (N=262,607) and street name (N=204,068) prescription drug-related tweets and use of unsupervised machine learning analyses (ie, natural language processing) of collected data with data visualization to identify pertinent tweet themes. Latent Dirichlet allocation (LDA) with coherence score calculations was performed to compare brand (eg, OxyContin) and street (eg, oxys) name tweets. RESULTS: We found people discussed drug use differently depending on whether a brand name or street name was used. Brand name categories often contained political talking points (eg, border, crime, and political handling of ongoing drug mitigation strategies). In contrast, categories containing street names occasionally referenced drug misuse, though multiple social uses for a term (eg, Sonata) muddled topic clarity. CONCLUSIONS: Content in the brand name corpus reflected discussion about the drug itself and less often reflected personal use. However, content in the street name corpus was notably more diverse and resisted simple LDA categorization. We speculate this may reflect effective use of slang terminology to clandestinely discuss drug-related activity. If so, straightforward analyses of digital drug-related communication may be more difficult than previously assumed. This work has the potential to be used for surveillance and detection of harmful drug use information. It also might be used for appropriate education and dissemination of information to persons engaged in drug use content on Twitter.

Evolution of social mood in Spain throughout the COVID-19 vaccination process: a machine learning approach to tweets analysis.

OBJECTIVES: This paper presents a new approach based on the combination of machine learning techniques, in particular, sentiment analysis using lexicons, and multivariate statistical methods to assess the evolution of social mood through the COVID-19 vaccination process in Spain. METHODS: Analysing 41,669 Spanish tweets posted between 27 February 2020 and 31 December 2021, different sentiments were assessed using a list of Spanish words and their associations with eight basic emotions (anger, fear, anticipation, trust, surprise, sadness, joy and disgust) and three valences (neutral, negative and positive). How the different subjective emotions were distributed across the tweets was determined using several descriptive statistics; a trajectory plot representing the emotional valence vs narrative time was also included. RESULTS: The results achieved are highly illustrative of the social mood of citizens, registering the different emerging opinion clusters, gauging public states of mind via the collective valence, and detecting the prevalence of different emotions in the successive phases of the vaccination process. CONCLUSIONS: The present combination in formal models of objective and subjective information would therefore provide a more accurate vision of social reality, in this case regarding the COVID-19 vaccination process in Spain, which will enable a more effective resolution of problems.

A BERT Framework to Sentiment Analysis of Tweets.

Sentiment analysis has been widely used in microblogging sites such as Twitter in recent decades, where millions of users express their opinions and thoughts because of its short and simple manner of expression. Several studies reveal the state of sentiment which does not express sentiment based on the user context because of different lengths and ambiguous emotional information. Hence, this study proposes text classification with the use of bidirectional encoder representations from transformers (BERT) for natural language processing with other variants. The experimental findings demonstrate that the combination of BERT with CNN, BERT with RNN, and BERT with BiLSTM performs well in terms of accuracy rate, precision rate, recall rate, and F1-score compared to when it was used with Word2vec and when it was used with no variant.

A global portrait of expressed mental health signals towards COVID-19 in social media space.

Globally, the COVID-19 pandemic has induced a mental health crisis. Social media data offer a unique opportunity to track the mental health signals of a given population and quantify their negativity towards COVID-19. To date, however, we know little about how negative sentiments differ across countries and how these relate to the shifting policy landscape experienced through the pandemic. Using 2.1 billion individual-level geotagged tweets posted between 1 February 2020 and 31 March 2021, we track, monitor and map the shifts in negativity across 217 countries and unpack its relationship with COVID-19 policies. Findings reveal that there are important geographic, demographic, and socioeconomic disparities of negativity across continents, different levels of a nation's income, population density, and the level of COVID-19 infection. Countries with more stringent policies were associated with lower levels of negativity, a relationship that weakened in later phases of the pandemic. This study provides the first global and multilingual evaluation of the public's real-time mental health signals to COVID-19 at a large spatial and temporal scale. We offer an empirical framework to monitor mental health signals globally, helping international authorizations, including the United Nations and World Health Organization, to design smart country-specific mental health initiatives in response to the ongoing pandemic and future public emergencies.

Popular and Scientific Discourse on Autism: Representational Cross-Cultural Analysis of Epistemic Communities to Inform Policy and Practice.

BACKGROUND: Social media provide a window onto the circulation of ideas in everyday folk psychiatry, revealing the themes and issues discussed both by the public and by various scientific communities. OBJECTIVE: This study explores the trends in health information about autism spectrum disorder within popular and scientific communities through the systematic semantic exploration of big data gathered from Twitter and PubMed. METHODS: First, we performed a natural language processing by text-mining analysis and with unsupervised (machine learning) topic modeling on a sample of the last 10,000 tweets in English posted with the term #autism (January 2021). We built a network of words to visualize the main dimensions representing these data. Second, we performed precisely the same analysis with all the articles using the term "autism" in PubMed without time restriction. Lastly, we compared the results of the 2 databases. RESULTS: We retrieved 121,556 terms related to autism in 10,000 tweets and 5.7x109 terms in 57,121 biomedical scientific articles. The 4 main dimensions extracted from Twitter were as follows: integration and social support, understanding and mental health, child welfare, and daily challenges and difficulties. The 4 main dimensions extracted from PubMed were as follows: diagnostic and skills, research challenges, clinical and therapeutical challenges, and neuropsychology and behavior. CONCLUSIONS: This study provides the first systematic and rigorous comparison between 2 corpora of interests, in terms of lay representations and scientific research, regarding the significant increase in information available on autism spectrum disorder and of the difficulty to connect fragments of knowledge from the general population. The results suggest a clear distinction between the focus of topics used in the social media and that of scientific communities. This distinction highlights the importance of knowledge mobilization and exchange to better align research priorities with personal concerns and to address dimensions of well-being, adaptation, and resilience. Health care professionals and researchers can use these dimensions as a framework in their consultations to engage in discussions on issues that matter to beneficiaries and develop clinical approaches and research policies in line with these interests. Finally, our study can inform policy makers on the health and social needs and concerns of individuals with autism and their caregivers, especially to define health indicators based on important issues for beneficiaries.

Use of the Hashtag #DataSavesLives on Twitter: Exploratory and Thematic Analysis.

BACKGROUND: "Data Saves Lives" is a public engagement campaign that highlights the benefits of big data research and aims to establish public trust for this emerging research area. OBJECTIVE: This study explores how the hashtag #DataSavesLives is used on Twitter. We focused on the period when the UK government and its agencies adopted #DataSavesLives in an attempt to support their plans to set up a new database holding National Health Service (NHS) users' medical data. METHODS: Public tweets published between April 19 and July 15, 2021, using the hashtag #DataSavesLives were saved using NCapture for NVivo 12. All tweets were coded twice. First, each tweet was assigned a positive, neutral, or negative attitude toward the campaign. Second, inductive thematic analysis was conducted. The results of the thematic analysis were mapped under 3 models of public engagement: deficit, dialogue, and participatory. RESULTS: Of 1026 unique tweets available for qualitative analysis, discussion around #DataSavesLives was largely positive (n=716, 69.8%) or neutral (n=276, 26.9%) toward the campaign with limited negative attitudes (n=34, 3.3%). Themes derived from the #DataSavesLives debate included ethical sharing, proactively engaging the public, coproducing knowledge with the public, harnessing potential, and gaining an understanding of big data research. The Twitter discourse was largely positive toward the campaign. The hashtag is predominantly used by similar-minded Twitter users to share information about big data projects and to spread positive messages about big data research when there are public controversies. The hashtag is generally used by organizations and people supportive of big data research. Tweet authors recognize that the public should be proactively engaged and involved in big data projects. The campaign remains UK centric. The results indicate that the communication around big data research is driven by the professional community and remains 1-way as members of the public rarely use the hashtag. CONCLUSIONS: The results demonstrate the potential of social media but draws attention to hashtag usage being generally confined to "Twitter bubbles": groups of similar-minded Twitter users.

Sentiment and emotion trends in nurses' tweets about the COVID-19 pandemic.

PURPOSE: Twitter is being increasingly used by nursing professionals to share ideas, information, and opinions about the global pandemic, yet there continues to be a lack of research on how nurse sentiment is associated with major events happening on the frontline. The purpose of the study was to quantitatively identify sentiments, emotions, and trends in nurses' tweets and to explore the variations in sentiments and emotions over a period in 2020 with respect to the number of cases and deaths of COVID-19 worldwide. DESIGN: A cross-sectional data mining study was held from March 3, 2020 through December 3, 2020. The tweets related to COVID-19 were downloaded using the tweet IDs available from a public website. Data were processed and filtered by searching for keywords related to nursing in the profile description field using the R software and JMP Pro Version 16 and the sentiment analysis of each tweet was done using AFINN, Bing, and NRC lexicon. FINDINGS: A total of 13,868 tweets from the Twitter accounts of self-identified nurses were included in the final analysis. The sentiment scores of nurses' tweets fluctuated over time and some clear patterns emerged related to the number of COVID-19 cases and deaths. Joy decreased and sadness increased over time as the pandemic impacts increased. CONCLUSIONS: Our study shows that Twitter data can be leveraged to study the emotions and sentiments of nurses, and the findings suggest that the emotional realm of nurses was affected during the COVID-19 pandemic according to the emotional trends observed in tweets. CLINICAL RELEVANCE: The study provides insight into what nurses are feeling, and findings from this study highlight the importance of developing and implementing interventions targeted at nurses at the workplace to prevent mental health consequences.

Depression Detection Based on Hybrid Deep Learning SSCL Framework Using Self-Attention Mechanism: An Application to Social Networking Data.

In today's world, mental health diseases have become highly prevalent, and depression is one of the mental health problems that has become widespread. According to WHO reports, depression is the second-leading cause of the global burden of diseases. In the proliferation of such issues, social media has proven to be a great platform for people to express themselves. Thus, a user's social media can speak a great deal about his/her emotional state and mental health. Considering the high pervasiveness of the disease, this paper presents a novel framework for depression detection from textual data, employing Natural Language Processing and deep learning techniques. For this purpose, a dataset consisting of tweets was created, which were then manually annotated by the domain experts to capture the implicit and explicit depression context. Two variations of the dataset were created, on having binary and one ternary labels, respectively. Ultimately, a deep-learning-based hybrid Sequence, Semantic, Context Learning (SSCL) classification framework with a self-attention mechanism is proposed that utilizes GloVe (pre-trained word embeddings) for feature extraction; LSTM and CNN were used to capture the sequence and semantics of tweets; finally, the GRUs and self-attention mechanism were used, which focus on contextual and implicit information in the tweets. The framework outperformed the existing techniques in detecting the explicit and implicit context, with an accuracy of 97.4 for binary labeled data and 82.9 for ternary labeled data. We further tested our proposed SSCL framework on unseen data (random tweets), for which an F1-score of 94.4 was achieved. Furthermore, in order to showcase the strengths of the proposed framework, we validated it on the "News Headline Data set" for sarcasm detection, considering a dataset from a different domain. It also outmatched the performance of existing techniques in cross-domain validation.

Stance Analysis of Distance Education in the Kingdom of Saudi Arabia during the COVID-19 Pandemic Using Arabic Twitter Data.

The coronavirus has caused significant disruption to people's everyday lives, altering how people live, work, and study. The Kingdom of Saudi Arabia (KSA) reacted very quickly to suppress the spread of the virus even before the first case of COVID-19 was confirmed in the country. In the education sector, all face-to-face activities at public and private schools and universities were suspended, as they switched from traditional to distance learning for the entire 2020 academic year. This study collected 1,846,285 tweets to analyze the public's dynamic opinions towards distance education in the KSA during the 2020 academic year. Several classical machine-learning models and deep-learning models, including ensemble random forest (RF), support vector machine (SVM), adaptive boosting (AdaBoost), multinomial naïve Bayes (MNB), convolutional neural network (CNN), and long short-term memory (LSTM), were tested on this data, and the best-performing models were selected to analyze the public stance towards distance education. Additionally, I correlated my analysis with the major events that were announced by the Ministry of Education (MOE). I observed that people in the KSA took some time to react and express their stances at the start of the academic year. Regarding the news, I observed that any exam-related topic attracted high engagement. In-favor stances increased when news headlines covered the topic of exams compared to other topics. The results show that the primary Saudi public stance favored distance education during the 2020 academic year.

A note on tweeting and equity markets before and during the Covid-19 pandemic.

We investigate the differential effects of a new index of Twitter-based market uncertainty (TMU) and variables for the US equity market before and during the Covid-19 pandemic. We find that markets are significantly more sensitive to the uncertainty contained in tweets during the pandemic, the TMU is a leading indicator of returns only during the pandemic, and the effect of the TMU on the volatility and liquidity of equity markets is greater during the pandemic compared to the pre-pandemic period. Our results show that the information contained tweets are having a much larger effect on equity markets during the pandemic.

Suicide-related Twitter Content in Response to a National Mental Health Awareness Campaign and the Association between the Campaign and Suicide Rates in Ontario.

OBJECTIVE: Mental health awareness (MHA) campaigns have been shown to be successful in improving mental health literacy, decreasing stigma, and generating public discussion. However, there is a dearth of evidence regarding the effects of these campaigns on behavioral outcomes such as suicides. Therefore, the objective of this article is to characterize the association between the event and suicide in Canada's most populous province and the content of suicide-related tweets referencing a Canadian MHA campaign (Bell Let's Talk Day [BLTD]). METHODS: Suicide counts during the week of BTLD were compared to a control window (2011 to 2016) to test for associations between the BLTD event and suicide. Suicide tweets geolocated to Ontario, posted in 2016 with the BLTD hashtag were coded for specific putatively harmful and protective content. RESULTS: There was no associated change in suicide counts. Tweets (n = 3,763) mainly included content related to general comments about suicide death (68%) and suicide being a problem (42.8%) with little putatively helpful content such as stories of resilience (0.6%) and messages of hope (2.2%). CONCLUSIONS: In Ontario, this national mental health media campaign was associated with a high volume of suicide-related tweets but not necessarily including content expected to diminish suicide rates. Campaigns like BLTD should strongly consider greater attention to suicide-related messaging that promotes help-seeking and resilience. This may help to further decrease stigmatization, and potentially, reduce suicide rates.

"Care is not care if it isn't person-centred": A content analysis of how Person-Centred Care is expressed on Twitter.

BACKGROUND: Person-Centred Care (PCC) has been the subject of growing interest in recent decades. Even though there is no conceptual consensus regarding PCC, many health-care institutions have implemented elements into their care. OBJECTIVE: This study aimed to investigate the PCC topics presented by different stakeholder groups on Twitter and to explore the perceptions of PCC within the content of the tweets. METHOD: Tweets with mentions of PCC in various translations were collected through a Twitter Application Programming Interface in October 2019. The tweets were analysed using quantitative and qualitative content analysis. RESULTS: Five stakeholder groups and ten topics were identified within 1540 tweets. The results showed that the PCC content focused on providing information and opinions rather than expressing experiences of PCC in practice. Qualitative content analysis of 428 selected tweets revealed content on a vision that all care should be person-centred but that the realization of that vision was more complicated. CONCLUSIONS: Twitter has shown to be a quick and non-intrusive data collection tool for uncovering stakeholders' expressions concerning PCC. The PCC content revealed that stakeholders feel a need to 'educate' others about their perception of PCC when experiences and real-life applications are missing. More action should be taken for the implementation of PCC rather than circulating PCC vision without operationalization in care. PUBLIC CONTRIBUTION: The public provided the data through their posts on Twitter, and it is their perception of PCC that is studied here.

Examining Twitter Discourse on Electronic Cigarette and Tobacco Consumption During National Cancer Prevention Month in 2018: Topic Modeling and Geospatial Analysis.

BACKGROUND: Examining public perception of tobacco products is critical for effective tobacco policy making and public education outreach. While the link between traditional tobacco products and lung cancer is well established, it is not known how the public perceives the association between electronic cigarettes (e-cigarettes) and lung cancer. In addition, it is unclear how members of the public interact with official messages during cancer campaigns on tobacco consumption and lung cancer. OBJECTIVE: In this study, we aimed to analyze e-cigarette and smoking tweets in the context of lung cancer during National Cancer Prevention Month in 2018 and examine how e-cigarette and traditional tobacco product discussions relate to implementation of tobacco control policies across different states in the United States. METHODS: We mined tweets that contained the term "lung cancer" on Twitter from February to March 2018. The data set contained 13,946 publicly available tweets that occurred during National Cancer Prevention Month (February 2018), and 10,153 tweets that occurred during March 2018. E-cigarette-related and smoking-related tweets were retrieved, using topic modeling and geospatial analysis. RESULTS: Debates on harmfulness (454/915, 49.7%), personal experiences (316/915, 34.5%), and e-cigarette risks (145/915, 15.8%) were the major themes of e-cigarette tweets related to lung cancer. Policy discussions (2251/3870, 58.1%), smoking risks (843/3870, 21.8%), and personal experiences (776/3870, 20.1%) were the major themes of smoking tweets related to lung cancer. Geospatial analysis showed that discussion on e-cigarette risks was positively correlated with the number of state-level smoke-free policies enacted for e-cigarettes. In particular, the number of indoor and on campus smoke-free policies was related to the number of tweets on e-cigarette risks (smoke-free indoor, r49=0.33, P=.02; smoke-free campus, r49=0.32, P=.02). The total number of e-cigarette policies was also positively related to the number of tweets on e-cigarette risks (r49=0.32, P=.02). In contrast, the number of smoking policies was not significantly associated with any of the smoking themes in the lung cancer discourse (P>.13). CONCLUSIONS: Though people recognized the importance of traditional tobacco control policies in reducing lung cancer incidences, their views on e-cigarette risks were divided, and discussions on the importance of e-cigarette policy control were missing from public discourse. Findings suggest the need for health organizations to continuously engage the public in discussions on the potential health risks of e-cigarettes and raise awareness of the insidious lobbying efforts from the tobacco industry.

An Emotion Care Model using Multimodal Textual Analysis on COVID-19.

At the dawn of the year 2020, the world was hit by a significant pandemic COVID-19, that traumatized the entire planet. The infectious spread grew in leaps and bounds and forced the policymakers and governments to move towards lockdown. The lockdown further compelled people to stay under house arrest, which further resulted in an outbreak of emotions on social media platforms. Perceiving people's emotional state during these times becomes critically and strategically important for the government and the policymakers. In this regard, a novel emotion care scheme has been proposed in this paper to analyze multimodal textual data contained in real-time tweets related to COVID-19. Moreover, this paper studies 8-scale emotions (Anger, Anticipation, Disgust, Fear, Joy, Sadness, Surprise, and Trust) over multiple categories such as nature, lockdown, health, education, market, and politics. This is the first of its kind linguistic analysis on multiple modes pertaining to the pandemic to the best of our understanding. Taking India as a case study, we inferred from this textual analysis that 'joy' has been lesser towards everything (~9-15%) but nature (~17%) due to the apparent fact of lessened pollution. The education system entailed more trust (~29%) due to teachers' fraternity's consistent efforts. The health sector witnessed sadness (~16%) and fear (~18%) as the dominant emotions among the masses as human lives were at stake. Additionally, the state-wise and emotion-wise depiction is also provided. An interactive internet application has also been developed for the same.