Twitter Application Programming Interface Research Articles

From Tweets to Streets: Observational Study on the Association Between Twitter Sentiment and Anti-Asian Hate Crimes in New York City from 2019 to 2022.

Anti-Asian hate crimes escalated during the COVID-19 pandemic; however, limited research has explored the association between social media sentiment and hate crimes toward Asian communities. This study aims to investigate the relationship between Twitter (rebranded as X) sentiment data and the occurrence of anti-Asian hate crimes in New York City from 2019 to 2022, a period encompassing both before and during COVID-19 pandemic conditions. We used a hate crime dataset from the New York City Police Department. This dataset included detailed information on the occurrence of anti-Asian hate crimes at the police precinct level from 2019 to 2022. We used Twitter's application programming interface for Academic Research to collect a random 1% sample of publicly available Twitter data in New York State, including New York City, that included 1 or more of the selected Asian-related keywords and applied support vector machine to classify sentiment. We measured sentiment toward the Asian community using the rates of negative and positive sentiment expressed in tweets at the monthly level (N=48). We used negative binomial models to explore the associations between sentiment levels and the number of anti-Asian hate crimes in the same month. We further adjusted our models for confounders such as the unemployment rate and the emergence of the COVID-19 pandemic. As sensitivity analyses, we used distributed lag models to capture 1- to 2-month lag times. A point increase of 1% in negative sentiment rate toward the Asian community in the same month was associated with a 24% increase (incidence rate ratio [IRR] 1.24; 95% CI 1.07-1.44; P=.005) in the number of anti-Asian hate crimes. The association was slightly attenuated after adjusting for unemployment and COVID-19 emergence (ie, after March 2020; P=.008). The positive sentiment toward Asian tweets with a 0-month lag was associated with a 12% decrease (IRR 0.88; 95% CI 0.79-0.97; P=.002) in expected anti-Asian hate crimes in the same month, but the relationship was no longer significant after adjusting for the unemployment rate and the emergence of COVID-19 pandemic (P=.11). A higher negative sentiment level was associated with more hate crimes specifically targeting the Asian community in the same month. The findings highlight the importance of monitoring public sentiment to predict and potentially mitigate hate crimes against Asian individuals.

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

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. 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. 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. 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. 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.

Negative sentiments toward anterior cruciate ligament injury prevention outweigh positive awareness discussions on social media

ObjectivesThe aim of this study is to understand the current public discussion surrounding anterior cruciate ligament (ACL) injury prevention on social media and determine factors that influence levels of public engagement. MethodsWe performed a qualitative and quantitative cross-sectional analysis of ACL injury prevention techniques discussed on social media via the Twitter application programming interface (API). The Twitter API was queried from inception to May 2023 using keywords related to ACL injury and prevention. We conducted a thematic analysis of the posts and performed a sentiment analysis using natural language processing. A multivariable regression model was used to identify metadata that predicted higher engagement (media, links, tagging, hashtags). ResultsA subset of 1823 unique posts was analyzed from 1701 unique accounts. Most posts were raising awareness about ACL injury prevention (n ​= ​733, 40.2%), followed by opinions on the topic (n ​= ​390, 21.4%), specific prevention techniques (n ​= ​289, 15.9%), personal experiences (n ​= ​272, 14.9%), and research (n ​= ​139, 7.6%). The majority consisted of posts from patients or caregivers (n ​= ​948, 55.7%), whereas healthcare providers accounted for 14.7% of posts. Posts containing media increased Tweet engagement count by an average of 6.1 (95% CI 2.8 to 9.4, p ​= ​0.00033) and posts discussing personal opinions increased engagement by 6.7 (95% CI 3.5 to 9.8, p ​= ​0.00004). On sentiment analysis of all included Tweets, 822 (45.1%) posts were positive, 309 (17.0%) were negative, and 692 (38.0%) were neutral. Sentiments expressed in posts related to ACL prevention were 2.8 times more negative compared to those discussing raising awareness. ConclusionsThere is active discussion about ACL injury prevention on Twitter. The use of visual media increased public engagement. We identified a potential knowledge gap between the available prevention techniques and the perspectives of athletes, highlighting the need for healthcare professionals to enhance their engagement with ACL injury prevention on social media. Level of evidenceIII.

Exploring stroke discourse on Twitter through content and network analysis among Indian users

The study aimed to understand stroke-related Twitter conversations in India, focusing on topics, message sources, reach, and influential users to provide insights to stakeholders regarding community needs for knowledge, support, and interventions. Geo-tagged Twitter posts focusing on stroke originating from India and, spanning from November 7, 2022, to February 28, 2023, were systematically obtained via the Twitter application programming interface, using keywords and hashtags sourced through Symplur Signals. Preprocessing involved the removal of hashtags, stop words, and URLs. The Latent Dirichlet Allocation (LDA) topic model was used to identify recurring stroke-related topics, while influential users were identified through social network analysis. About half of the tweets about stroke in India were about seeking support and post-stroke bereavement sharing and had the highest reachability. Four out of 10 tweets were from the individual twitter users. Tweets on the topic risk factors, awareness and prevention (14.6%) constituted the least proportion, whereas the topic management, research, and promotion had the least retweet ratio. Twitter demonstrates significant potential as a platform for both disseminating and acquiring stroke-related information within the Indian context. The identified topics and understanding of the content of discussion offer valuable resources to public health professionals and organizations to develop targeted educational and engagement strategies for the relevant audience.

Social media for palliative and end-of-life care research: a systematic review

BackgroundSocial media with real-time content and a wide-reaching user network opens up more possibilities for palliative and end-of-life care (PEoLC) researchers who have begun to embrace it as a complementary...

Pediatric Cancer Communication on Twitter: Natural Language Processing and Qualitative Content Analysis.

During the COVID-19 pandemic, Twitter (recently rebranded as "X") was the most widely used social media platform with over 2 million cancer-related tweets. The increasing use of social media among patients and family members, providers, and organizations has allowed for novel methods of studying cancer communication. This study aimed to examine pediatric cancer-related tweets to capture the experiences of patients and survivors of cancer, their caregivers, medical providers, and other stakeholders. We assessed the public sentiment and content of tweets related to pediatric cancer over a time period representative of the COVID-19 pandemic. All English-language tweets related to pediatric cancer posted from December 11, 2019, to May 7, 2022, globally, were obtained using the Twitter application programming interface. Sentiment analyses were computed based on Bing, AFINN, and NRC lexicons. We conducted a supplemental nonlexicon-based sentiment analysis with ChatGPT (version 3.0) to validate our findings with a random subset of 150 tweets. We conducted a qualitative content analysis to manually code the content of a random subset of 800 tweets. A total of 161,135 unique tweets related to pediatric cancer were identified. Sentiment analyses showed that there were more positive words than negative words. Via the Bing lexicon, the most common positive words were support, love, amazing, heaven, and happy, and the most common negative words were grief, risk, hard, abuse, and miss. Via the NRC lexicon, most tweets were categorized under sentiment types of positive, trust, and joy. Overall positive sentiment was consistent across lexicons and confirmed with supplemental ChatGPT (version 3.0) analysis. Percent agreement between raters for qualitative coding was 91%, and the top 10 codes were awareness, personal experiences, research, caregiver experiences, patient experiences, policy and the law, treatment, end of life, pharmaceuticals and drugs, and survivorship. Qualitative content analysis showed that Twitter users commonly used the social media platform to promote public awareness of pediatric cancer and to share personal experiences with pediatric cancer from the perspective of patients or survivors and their caregivers. Twitter was frequently used for health knowledge dissemination of research findings and federal policies that support treatment and affordable medical care. Twitter may serve as an effective means for researchers to examine pediatric cancer communication and public sentiment around the globe. Despite the public mental health crisis during the COVID-19 pandemic, overall sentiments of pediatric cancer-related tweets were positive. Content of pediatric cancer tweets focused on health and treatment information, social support, and raising awareness of pediatric cancer.

Feeling analysis on allergen immunotherapy on Twitter using an unsupervised machine learning model

Analyze feelings about allergen-specific immunotherapy on Twitter using the VADER model VADER (Valence Aware Dictionary and sEntiment Reasoner) model. tweets related to specific allergen immunotherapy were obtained through the Twitter Application Programming Interface (API). The keywords "allergy shot" were used between January 1, 2012, and December 31, 2022. The data was processed by removing URLs, usernames, hashtags, multiple spaces, and duplicate tweets. Subsequently, a sentiment analysis was performed using the VADER model. A total of 34,711 tweets were retrieved, of which 1928 were eliminated. Of the remaining 32,783 tweets, 32.41% expressed a negative sentiment, 31.11% expressed a neutral sentiment, and 36.47% expressed a positive sentiment, with an average polarity of 0.02751 (neutral) over the 11-year period. The average polarity of tweets about allergen-specific immunotherapy is neutral over the 11 years analyzed. There was an annual increase in the average polarity over the years, with 2017, 2018, and 2022 having positive polarity averages. Additionally, the number of tweets decreased over time.

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.

Using Natural Language Processing to Explore Social Media Opinions on Food Security: Sentiment Analysis and Topic Modeling Study.

Social media has the potential to be of great value in understanding patterns in public health using large-scale analysis approaches (eg, data science and natural language processing [NLP]), 2 of which have been used in public health: sentiment analysis and topic modeling; however, their use in the area of food security and public health nutrition is limited. This study aims to explore the potential use of NLP tools to gather insights from real-world social media data on the public health issue of food security. A search strategy for obtaining tweets was developed using food security terms. Tweets were collected using the Twitter application programming interface from January 1, 2019, to December 31, 2021, filtered for Australia-based users only. Sentiment analysis of the tweets was performed using the Valence Aware Dictionary and Sentiment Reasoner. Topic modeling exploring the content of tweets was conducted using latent Dirichlet allocation with BigML (BigML, Inc). Sentiment, topic, and engagement (the sum of likes, retweets, quotations, and replies) were compared across years. In total, 38,070 tweets were collected from 14,880 Twitter users. Overall, the sentiment when discussing food security was positive, although this varied across the 3 years. Positive sentiment remained higher during the COVID-19 lockdown periods in Australia. The topic model contained 10 topics (in order from highest to lowest probability in the data set): "Global production," "Food insecurity and health," "Use of food banks," "Giving to food banks," "Family poverty," "Food relief provision," "Global food insecurity," "Climate change," "Australian food insecurity," and "Human rights." The topic "Giving to food banks," which focused on support and donation, had the highest proportion of positive sentiment, and "Global food insecurity," which covered food insecurity prevalence worldwide, had the highest proportion of negative sentiment. When compared with news, there were some events, such as COVID-19 support payment introduction and bushfires across Australia, that were associated with high periods of positive or negative sentiment. Topics related to food insecurity prevalence, poverty, and food relief in Australia were not consistently more prominent during the COVID-19 pandemic than before the pandemic. Negative tweets received substantially higher engagement across 2019 and 2020. There was no clear relationship between topics that were more likely to be positive or negative and have higher or lower engagement, indicating that the identified topics are discrete issues. In this study, we demonstrated the potential use of sentiment analysis and topic modeling to explore evolution in conversations on food security using social media data. Future use of NLP in food security requires the context of and interpretation by public health experts and the use of broader data sets, with the potential to track dimensions or events related to food security to inform evidence-based decision-making in this area.

Characterizing user demographics in posts related to breast, lung and colon cancer on Japanese twitter (X)

Various cancer-related information is spreading on social media. Our study aimed to examine the account types associated with cancer-related tweets (currently known as posts) on Twitter (currently known as X) in Japan, specifically focusing on breast, lung, and colon cancer. Using the Twitter application programming interface, we collected tweets containing keywords of the three cancers type in August–September 2022. The accounts were categorized into seven types: Survivor, Patient’s family, Healthcare provider, Public organization, Private organization, News, and Other according to account name and texts. We analyzed the sources of the top 50 most liked and retweeted tweets. Out of 7753 identified tweets, breast cancer represented the majority (62.8%), followed by lung cancer (20.8%) and colon cancer (16.3%). Tweets came from 4976 accounts. Account types varied depending on the cancer type, with breast cancer topics more frequently from Survivor (16.0%) and lung cancer from Patient’s family (16.3%). Healthcare provider and Public organization had minimal representation across three cancer types. The trends in the top 50 tweets mirrored the distribution of accounts for each cancer type. Breast cancer-related tweets had the highest frequency. There were few from public organizations. These findings emphasize the need to consider the characteristics of cancer-related information sources when sharing and gathering information on social media.

The Russian invasion of Ukraine and the exchange rate of the Polish zloty: A fallacy of monetary autonomy?

Abstract We use the Twitter application programming interface to construct a novel indicator capturing the varying perceptions of geopolitical risk related to the war in Ukraine. We show that the Twitter variable is a statistically significant determinant of the EUR/PLN exchange rate following the invasion. We estimate that the war in Ukraine was responsible for an increase in the EUR/PLN exchange rate of about 5.0% in the first 2 weeks following its breakout and that the weakening of PLN contributed to an increase of headline inflation in Poland by about 0.33 percentage points. Our findings suggest that monetary policy autonomy can be substantially constrained due to conflicts in nearby countries. Therefore, they provide an argument for Poland in seeking Eurozone membership as a way to contain the impact of geopolitical factors on exchange rate volatility, which is likely to remain at play in the longer run.

Acupuncture Topics on Twitter (Currently X) in English and Japanese: Co-occurrence Network Analysis.

Background With the growth of social media, there has been an increase in health-related studies utilizing data obtained from such sites and applications. Although acupuncture is used as a complementary alternative medicine worldwide, there is little research on acupuncture utilizing social media data. This study investigates the topics related to acupuncture on Twitter, currently known as X, in English and Japanese. Methods We collected tweets containing the English word "acupuncture" and its Japanese equivalent using Twitter's application programming interface from January 1, 2022to December 31, 2022. After extracting the top 50 frequently occurring words from the collected tweet texts separately for each language, we conducted a co-occurrence network analysis for those words, in order to evaluate the patterns of their occurrence. Results A total of 70,435 English tweets from 41,939 users and 188,671 Japanese tweets from 81,093 users were analyzed after excluding retweets and duplicate tweets. The co-occurrence network analysis revealed that topics related to pain, other complementary and alternative medicines, and acupuncture-related experiences were common in both languages. However, explanatory topics such as needle use, Chinese medicine, and body points were specific to English tweets, while those about beauty care were specific to Japanese tweets. Conclusion The number of tweets regarding acupuncture and the number of users who posted them were both higher in Japanese than in English. Some acupuncture topics were common in both languages, while other topics were specific to each language. The findings of this study provide valuable insights into understanding information about acupuncture on social media.

Low credibility URL sharing on Twitter during reporting linking rare blood clots with the Oxford/AstraZeneca COVID-19 vaccine.

The COVID-19 pandemic was accompanied by an "infodemic" of misinformation. Misleading narratives around the virus, its origin, and treatments have had serious implications for public health. In March 2021, concerns were raised about links between the Oxford/AstraZeneca (AZ) COVID-19 vaccine and recipients developing blood clots. This paper aims to identify whether this prompted any reaction in the diffusion of low-credibility COVID-19-relate information on Twitter. Twitter's application programming interface was used to collect data containing COVID-19-related keywords between 4th and 25th March 2021, a period centred on the peak of new coverage linking rare blood clots with the AZ vaccine. We analysed and visualised the data using temporal analysis and social network analysis tools. We subsequently analysed the data to determine the most influential users and domains in the propagation of low-credibility information about COVID-19 and the AZ vaccine. This research presents evidence that the peak of news coverage linking rare blood clots with the AZ vaccine correlated with an increased volume and proportion of low-credibility AZ-related content propagated on Twitter. However, no equivalent changes to the volume, propagation, or network structure for the full dataset of COVID-19-related information or misinformation were observed. The research identified RT.com as the most prolific creator of low-credibility COVID-19-related content. It also highlighted the crucial role of self-promotion in the successful propagation of low-credibility content on Twitter. The findings suggest that the simple approach adopted within the research to identify the most popular and influential sources of low-credibility content presents a valuable opportunity for public health authorities and social media platforms to develop bespoke strategies to counter the propagation of misinformation in the aftermath of a breaking news event.

Analysis of X (Formerly Twitter) Users’ Perceptions towards the Election of Jolidee Matongo as Mayor of Johannesburg

This paper presents a study on the analysis of X (formerly Twitter) users’ perceptions towards the election of Jolidee Matongo as mayor of Johannesburg, South Africa in 2021. This election was significant because it was the first time that a person of Zimbabwean descent had been elected mayor of Johannesburg. Given the vast amount of user-generated content on Twitter, analysing tweets can serve as a valuable proxy for capturing public opinion. However, to date, there appears to be a notable research gap concerning the wide adoption of tweet analysis for understanding public opinion. The study used 4324 tweets that were collected from X (Twitter) via the Twitter application programming interface (API) over a period of one week after the election. Sentiment analysis and topic modelling were used to analyse the results. The results suggested that social media platforms can be used to gain valuable insights into public opinion and the different perspectives. Social media platforms can also be used to spread xenophobia. Cognitive biases, specifically the confirmation bias has been found to influence Twitter users’ perception towards the election of Matongo. A significant number of negative tweets expressed xenophobic views due to Matongo’s Zimbabwean roots. However, the average sentiment score of 0.10 suggested that the overall sentiment of the tweets was slightly positive.

Novel Journal Metrics in Cardiothoracic Surgery: How Different Are Contemporary Metrics From the Impact Factor?

Despite shortcomings, impact factor (IF) remains the "gold standard" metric for journal quality. However, novel metrics including the h-index, g-index, and Altmetric Attention Score (AAS; mentions in mainstream/social media) are gaining traction. We assessed correlations between these metrics among cardiothoracic surgery journals. For all cardiothoracic surgery journals with a 2021 Clarivate IF (N = 20), the 2-year IF (2019 to 2020) and 5-year IF (2016 to 2020), h-index, and g-index were obtained. Two-year journal-level AAS (2019 to 2020) was also calculated. Journal Twitter presence and activity was sourced from Twitter and the Twitter application programming interface. Correlations were assessed using Spearman correlation, and coefficients of determination were calculated. IF demonstrated a moderate-strong positive correlation with the h-index (rs = 0.48 to 0.77) and g-index (rs = 0.49 to 0.79) and a moderate correlation with AAS (rs = 0.53 to 0.58). The 2-year IF accounted for 25% to 49% of variability in the h-index, 27% to 55% of variability in the g-index, and 32% of variability in the AAS. Among journals with a Twitter account (N = 10), IF was strongly correlated with Twitter following (rs = 0.81 to 0.86), which was in turn strongly correlated with journal AAS (rs = 0.79). Article-level AAS was moderately correlated with citation count (rs = 0.47). IF accounted for only between 25% and 55% of variability in the h-index and g-index, indicating that these newer metrics measure unique dimensions of citation-based impact. Thus, the academic community must familiarize itself with these newer journal metrics. Social media attention may be associated with scholarly impact, although further work is needed to understand these relationships.

Enhancing Sentiment Analysis Accuracy by Optimizing Hyperparameters of SVM and Logistic Regression Models

The Analysis of Sentiments expressed on Twitter is a widely practiced application of Natural Language Processing (NLP) and Artificial Intelligence (AI). This process involves examining tweets to determine the emotional tone conveyed within the message. AI-based approaches are employed in Twitter sentiment analysis, typically following these steps: Data Collection, Data Preprocessing, and Sentiment Analysis, where AI techniques like Support Vector Machines (SVM) and Logistic Regression are utilized to categorize tweets into positive, negative, or neutral sentiments. Twitter data is a valuable source of information, serving diverse purposes such as real-time updates, user feedback, brand monitoring, market research, digital marketing, and political analysis. The Twitter API (Application Programming Interface) provides developers with tools and functionalities to access and interact with Twitter data, including tweets, user profiles, and timelines, enabling a wide range of applications and services. However, Twitter sentiment analysis presents challenges such as handling sarcasm, irony, colloquial language, and coping with the sheer volume and rapid flow of Twitter data. Nevertheless, with effective preprocessing techniques and AI methods, Twitter sentiment analysis can yield valuable insights into public opinion on various topics.

Changes in suicide-related tweets before and during the COVID-19 pandemic in France: The importance of social media monitoring in public health prediction

IntroductionThe COVID-19 pandemic impacted mental health, as demonstrated by numerous studies. In recent years, especially during the pandemic, the use of social networks, including Twitter, increased. This suggests that this media could help with mental health monitoring, as attested by previous studies. MethodWe conducted a multidisciplinary study on French tweets that were posted between January 1, 2019, and December 31, 2021. We selected the tweets via the Twitter API (Application Programming Interface) using five keywords relating to suicide: want to die, suicidal ideation, commit suicide, suicidal, and suicide attempt. A word frequency analysis was performed, and the data were compared with the number of emergency visits for suicidal ideation before and during the COVID-19 pandemic as recorded by the French national suicide observatory. ResultsWe observed that 189,005 tweets were related to suicide in 2019, 261,993 in 2020 (+38.62% of that observed in 2019), and 301,177 in 2021 (+59.35% of that observed in 2019). We also observed an increase in the number of tweets containing control words in 2020 (+30.07% of that observed in 2019), but in 2021, the number almost fell back to the level of that in 2019 (+5.96% of that observed in 2019). Furthermore, the difference between both ratios (of suicide-related tweets and of tweets containing control words) was most significant during the third lockdown. The change in the number of suicide-related tweets followed a curve that overlapped with the change in the number of emergency visits following suicidal ideations, as reported by the French national suicide observatory. In conclusion, Twitter can be an adequate and reliable tool for screening for suicidal ideation in the general population.

Detection and Characterization of Web-Based Pediatric COVID-19 Vaccine Discussions and Racial and Ethnic Minority Topics: Retrospective Analysis of Twitter Data.

Despite pediatric populations representing a smaller proportion of COVID-19 cases and having a less severe prognosis, those belonging to racial and ethnic minority groups are at an increased risk of developing more severe COVID-19-related outcomes. Vaccine coverage is crucial to pandemic mitigation efforts, yet since the start of the COVID-19 pandemic, vaccine hesitancy has increased and routine pediatric immunizations have decreased. Limited research exists on how vaccine hesitancy may contribute to low pediatric COVID-19 vaccine uptake among racial and ethnic minority populations. This study aimed to characterize COVID-19 vaccine-related discussion and sentiment among Twitter users, particularly among racial and ethnic minority users. We used the Twitter application programming interface to collect tweets and replies. Tweets were selected by filtering for keywords associated with COVID-19 vaccines and pediatric-related terms. From this corpus of tweets, we used the Biterm Topic Model to output topics and examined the top 200 retweeted tweets that were coded for pediatric COVID-19 vaccine relevance. Relevant tweets were analyzed using an inductive coding approach to characterize pediatric COVID-19 vaccine-related themes. Replies to relevant tweets were collected and coded. User metadata were assessed for self-reporting of race or ethnic group affiliation and verified account status. A total of 863,007 tweets were collected from October 2020 to October 2021. After outputting Biterm Topic Model topics and reviewing the 200 most retweeted tweets, 208,666 tweets and 3905 replies were identified as being pediatric COVID-19 vaccine related. The majority (150,262/208,666, 72.01%) of tweets expressed vaccine-related concerns. Among tweets discussing vaccine confidence, user replies expressing agreement were significantly outweighed by those expressing disagreement (1016/3106, 32.71% vs 2090/3106, 67.29%; P<.001). The main themes identified in the Twitter interactions were conversations regarding vaccine-related concerns including adverse side effects, concerns that the vaccine is experimental or needs more testing and should not be tested on pediatric populations, the perception that the vaccine is unnecessary given the perceived low risk of pediatric infection, and conversations associated with vaccine-related confidence (ie, the vaccine is protective). Among signal tweets and replies, we identified 418 users who self-identified as a racial minority individual and 40 who self-identified as an ethnic minority individual. Among the subcodes identified in this study, the vaccine being protective was the most discussed topic by racial and ethnic minority groups (305/444, 68.7%). Vaccine-related concerns can have negative consequences on vaccine uptake and participation in vaccine-related clinical trials. This can impact the uptake and development of safe and effective vaccines, especially among racial and ethnic minority populations.

A Decade of Tweets: Visualizing Racial Sentiments Towards Minoritized Groups in the United States Between 2011 and 2021.

Research has demonstrated the negative impact of racism on health, yet the measurement of racial sentiment remains challenging. This article provides practical guidance on using social media data for measuring public sentiment. We describe the main steps of such research, including data collection, data cleaning, binary sentiment analysis, and visualization of findings. We randomly sampled 55,844,310 publicly available tweets from 1 January 2011 to 31 December 2021 using Twitter's Application Programming Interface. We restricted analyses to US tweets in English using one or more 90 race-related keywords. We used a Support Vector Machine, a supervised machine learning model, for sentiment analysis. The proportion of tweets referencing racially minoritized groups that were negative increased at the county, state, and national levels, with a 16.5% increase at the national level from 2011 to 2021. Tweets referencing Black and Middle Eastern people consistently had the highest proportion of negative sentiment compared with all other groups. Stratifying temporal trends by racial and ethnic groups revealed unique patterns reflecting historical events specific to each group, such as the killing of George Floyd regarding sentiment of posts referencing Black people, discussions of the border crisis near the 2018 midterm elections and anti-Latinx sentiment, and the emergence of COVID-19 and anti-Asian sentiment. This study demonstrates the utility of social media data as a quantitative means to measure racial sentiment over time and place. This approach can be extended to a range of public health topics to investigate how changes in social and cultural norms impact behaviors and policy.A supplemental digital video is available at http://links.lww.com/EDE/C91.

Studying Public Perception of Covid-19 Vaccination in Pediatric Age Group: A Sentiment Analysis of Tweets

Purpose: The COVID-19 pandemic in 2019 led to the biggest public health crisis in modern times and triggered a global effort to rapidly develop and deploy covid vaccines. This, in turn, led to a sharply divided opinions in the public about the safety of the vaccines, particularly around vaccine mandates and especially more polarized in children. The goal of this study was to use Twitter sentiment analysis to gauge public attitudes as expressed on social media towards the kids’ vaccination for COVID-19. Methods: To collect data for this sentiment analysis project, the Twitter Application Programming Interface (referred to as “API” from here on) was used to scrape relevant tweets containing the keywords “covid vaccine” and “kids” for the time frame December 2020-December 2022, and the number of tweets to ensure a manageable dataset. The data was then converted into a readable format for further analysis. After data cleaning and preprocessing, we performed sentiment analysis on the-leaned and pre-processed tweets using Text Blob. The results were then visualized using Tableau to clearly and concisely represent the sentiment analysis results and identify any trends or patterns. Results: The sentiment analysis showed that overall, 48% of tweets scored a positive sentiment, while only 21% had a negative sentiment, with 31% neutral. Over 50% of the tweets were from the USA, with India having the next highest number of tweets based on location. Verified users accounted for only 17% of the tweets. Conclusion: The positive sentiment towards the COVID-19 vaccine and kids suggests that there is a general acceptance and support for the vaccine among the public. Public perception tends to correlate with the perception of risk and fear, as well as with media efforts aimed at promoting vaccination adoption.