[Feeling analysis on allergen immunotherapy on Twitter using an unsupervised machine learning model]. / Análisis de sentimientos acerca de la inmunoterapia con alérgenos en Twitter mediante un modelo de aprendizaje automático no supervisado.

OBJECTIVE: Analyze feelings about allergen-specific immunotherapy on Twitter using the VADER model VADER (Valence Aware Dictionary and sEntiment Reasoner) model. METHODS: 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. RESULTS: 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. CONCLUSIONS: 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.

OBJETIVO: Analizar los sentimientos acerca de la inmunoterapia alérgeno-específica en Twitter mediante el modelo VADER (Valence Aware Dictionary and sEntiment Reasoner). MÉTODOS: Se utilizaron tweets relacionados con la inmunoterapia alérgeno-específica obtenidos a través del API (Application Programming Interface) de Twitter. Se incorporaron las palabras clave "allergy shot" en el período comprendido entre el 1 de enero de 2012 y el 31 de diciembre de 2022. Los datos obtenidos fueron procesados, eliminando las URL, nombres de usuarios, hashtags, espacios múltiples y tweets duplicados. Posteriormente, se realizó un análisis de sentimientos utilizando el modelo VADER. RESULTADOS: Se recolectaron 34,711 tweets, de los que se eliminaron 1928. De los 32,783 tweets restantes, se encontró que el 32.41% de los usuarios expresó un sentimiento negativo, el 31.11% un sentimiento neutral y el 36.47% un sentimiento positivo, con una media de polaridad de 0.02751 (neutral) a lo largo de los 11 años. CONCLUSIONES: La polaridad media de los tweets acerca de la inmunoterapia alérgeno-específica es neutral a lo largo de los 11 años analizados. Existe un aumento anual en la polaridad media positiva a lo largo de los años, sobre todo entre 2017, 2018 y 2022. La cantidad de tweets disminuyó con el tiempo.

Twitter Analysis of Health Care Workers' Sentiment and Discourse Regarding Post-COVID-19 Condition in Children and Young People: Mixed Methods Study.

BACKGROUND: The COVID-19 pandemic has had a significant global impact, with millions of cases and deaths. Research highlights the persistence of symptoms over time (post-COVID-19 condition), a situation of particular concern in children and young people with symptoms. Social media such as Twitter (subsequently rebranded as X) could provide valuable information on the impact of the post-COVID-19 condition on this demographic. OBJECTIVE: With a social media analysis of the discourse surrounding the prevalence of post-COVID-19 condition in children and young people, we aimed to explore the perceptions of health care workers (HCWs) concerning post-COVID-19 condition in children and young people in the United Kingdom between January 2021 and January 2022. This will allow us to contribute to the emerging knowledge on post-COVID-19 condition and identify critical areas and future directions for researchers and policy makers. METHODS: From a pragmatic paradigm, we used a mixed methods approach. Through discourse, keyword, sentiment, and image analyses, using Pulsar and InfraNodus, we analyzed the discourse about the experience of post-COVID-19 condition in children and young people in the United Kingdom shared on Twitter between January 1, 2021, and January 31, 2022, from a sample of HCWs with Twitter accounts whose biography identifies them as HCWs. RESULTS: We obtained 300,000 tweets, out of which (after filtering for relevant tweets) we performed an in-depth qualitative sample analysis of 2588 tweets. The HCWs were responsive to announcements issued by the authorities regarding the management of the COVID-19 pandemic in the United Kingdom. The most frequent sentiment expressed was negative. The main themes were uncertainty about the future, policies and regulations, managing and addressing the COVID-19 pandemic and post-COVID-19 condition in children and young people, vaccination, using Twitter to share scientific literature and management strategies, and clinical and personal experiences. CONCLUSIONS: The perceptions described on Twitter by HCWs concerning the presence of the post-COVID-19 condition in children and young people appear to be a relevant and timely issue and responsive to the declarations and guidelines issued by health authorities over time. We recommend further support and training strategies for health workers and school staff regarding the manifestations and treatment of children and young people with post-COVID-19 condition.

Sentiment Analysis toward the COVID-19 Vaccine in the Main Latin American Media on Twitter: The Cases of Argentina, Chile, Colombia, Mexico, and Peru.

This article analyzes the media coverage of the COVID-19 vaccine by major media outlets in five Latin American countries: Argentina, Colombia, Chile, Mexico, and Peru. For this purpose, the XLM-roBERTa model was applied and the sentiments of all tweets published between January 2020 and June 2023 (n = 24,243) by the five outlets with the greatest online reach in each country were analyzed. The results show that the sentiment in the overall media and in each nation studied was mostly negative, and only at the beginning of the pandemic was there some positivity. In recent months, negative sentiment has increased twelvefold over positive sentiment, and has also garnered many more interactions than positive sentiment. The differences by platform and country are minimal, but there are markedly negative media, some more inclined to neutrality, and only one where positive sentiment predominates. This paper questions the role of journalism in Latin America during a health crisis as serious as that of the coronavirus, in which, instead of the expected neutrality, or even a certain message of hope, the media seem to have been dragged along by the negativity promoted by certain discourses far removed from scientific evidence.

Political quarrel overshadows vaccination advocacy: How the vaccine debate on Brazilian Twitter was framed by anti-vaxxers during Bolsonaro administration.

Despite Brazil's tradition of successful mass immunization programs, the country has been experiencing alarming declines in vaccination coverage, especially among children. That is aggravated by the growth of anti-vaccine movements and the spread of health misinformation in social media in the last decade, which have worsened during the COVID-19 outbreak. Several reports link populism and far-right politicians to anti-vaccination support worldwide, which was also the case in Brazil during president Jair Bolsonaro's administration. This project aimed to identify the circulating pro and anti-vaccine narratives in Portuguese on Twitter, during a crucial decision-making period regarding childhood vaccination in Brazil, from December 9, 2021, until February 9, 2022. From the over one million tweets and four million retweets collected, we identified two well-defined groups, one in favor and another against vaccination. Within the sample, we selected 1500 influencer tweets with the highest impact (>500 retweets) and conducted content analysis. Although the pro-vaccine influencers were more retweeted than anti-vaxxer ones, we observed that anti-vaccine movements were more succesful in framing discussions on Twitter. The subject of COVID-19 was the target of political polarization embedded in populist, anti-science and anti-traditional media discourses promoted by anti-vaxxers. As a counterpart, the pro-vaccine influencers reacted inarticulately, focusing on criticizing the anti-vaccination actors, attitudes, and policies instead of promoting vaccines. Based on reults, we claim that a well-coordinated network of health communicators from science centers and health institutions, in partnership with properly briefed social media influencers and fact-checking sources, would more efectively pre-tempt the public about vaccine misinformation.

The overturn of Roe v. Wade beyond abortion: a snapshot of methotrexate accessibility for people with rheumatic and musculoskeletal diseases-a mixed methods study using Twitter data.

In June 2022, the Supreme Court of the United States (US) overturned the right established in Roe v. Wade to terminate a pregnancy. Subsequently, some states passed abortion ban laws prohibiting the use of abortive methods, including methotrexate (MTX), which has been a cornerstone drug in rheumatology. We aimed to explore and analyze Twitter data to comprehend the short-term implications of the overturn on rheumatology care. We conducted a mixed methods study using social media (SoMe) data. Tweets publicly posted using "#Methotrexate or Methotrexate" were tracked. A combination of SoMe performance data with qualitative hashtag co-occurrence analysis and content analysis was conducted. A total of 5180 posts were generated and reached approximately 40 million users. Seventy-three percent of all publications came from the US. Females posted more than males. Additionally, the three pairs of hashtags with higher co-occurrence were: #roevswade, #abortionishealthcare, and #rheumatoidarthritis. From the content analysis, three main themes were generated: (i) violence against women, (ii) health policy without public health intelligence, and (iii) call for strategic alliances in favor of public health. The combination of biological sex and state of residence could condition the use of MTX. Men will be able to continue their treatment; however, women could lose continuity of it. Inequity in access to treatment is a political decision, just as it is to reproduce inequities and vulnerabilities through the actions taken. Not having access to MTX for rheumatic and musculoskeletal diseases (RMDs) is a critical issue that endangers the physical and mental health of people with RMDs.

Engagement of the e-commerce industry in the US, according to Twitter in the period of the COVID-19 pandemic.

E-commerce and the use of social media, particularly Twitter, both grew rapidly during the COVID-19 period. Companies may significantly benefit from social media management, which highlights the significance of responsible consumerism highlighted in SDG 12. This study analyzed the relationship of the level of engagement of leading US e-commerce companies according to their position in the financial market through the use of Twitter. The methodology was a quantitative and longitudinal approach, analyzing statistically (through statistical analysis to descriptive statistics, multiple and simple regressions). The 22,400 tweets during 2020, to estimate their engagement. The results showed that the level of engagement on Twitter is not directly related to the financial ranking, neither to its sales nor to the share price. The main contribution lies in the contribution to the literature, to guide academics, managers and CEOs of companies in efficient decision-making in their business strategies in the areas of marketing with the use of Twitter, where companies can boost loyalty, engagement and sales of their users.

Analysis of Fluoride-Free Content on Twitter: Topic Modeling Study.

BACKGROUND: Although social media has the potential to spread misinformation, it can also be a valuable tool for elucidating the social factors that contribute to the onset of negative beliefs. As a result, data mining has become a widely used technique in infodemiology and infoveillance research to combat misinformation effects. On the other hand, there is a lack of studies that specifically aim to investigate misinformation about fluoride on Twitter. Web-based individual concerns on the side effects of fluoridated oral care products and tap water stimulate the emergence and propagation of convictions that boost antifluoridation activism. In this sense, a previous content analysis-driven study demonstrated that the term fluoride-free was frequently associated with antifluoridation interests. OBJECTIVE: This study aimed to analyze "fluoride-free" tweets regarding their topics and frequency of publication over time. METHODS: A total of 21,169 tweets published in English between May 2016 and May 2022 that included the keyword "fluoride-free" were retrieved by the Twitter application programming interface. Latent Dirichlet allocation (LDA) topic modeling was applied to identify the salient terms and topics. The similarity between topics was calculated through an intertopic distance map. Moreover, an investigator manually assessed a sample of tweets depicting each of the most representative word groups that determined specific issues. Lastly, additional data visualization was performed regarding the total count of each topic of fluoride-free record and its relevance over time, using Elastic Stack software. RESULTS: We identified 3 issues by applying the LDA topic modeling: "healthy lifestyle" (topic 1), "consumption of natural/organic oral care products" (topic 2), and "recommendations for using fluoride-free products/measures" (topic 3). Topic 1 was related to users' concerns about leading a healthier lifestyle and the potential impacts of fluoride consumption, including its hypothetical toxicity. Complementarily, topic 2 was associated with users' personal interests and perceptions of consuming natural and organic fluoride-free oral care products, whereas topic 3 was linked to users' recommendations for using fluoride-free products (eg, switching from fluoridated toothpaste to fluoride-free alternatives) and measures (eg, consuming unfluoridated bottled water instead of fluoridated tap water), comprising the propaganda of dental products. Additionally, the count of tweets on fluoride-free content decreased between 2016 and 2019 but increased again from 2020 onward. CONCLUSIONS: Public concerns toward a healthy lifestyle, including the adoption of natural and organic cosmetics, seem to be the main motivation of the recent increase of "fluoride-free" tweets, which can be boosted by the propagation of fluoride falsehoods on the web. Therefore, public health authorities, health professionals, and legislators should be aware of the spread of fluoride-free content on social media to create and implement strategies against their potential health damage for the population.

Regionalized models for Spanish language variations based on Twitter.

Spanish is one of the most spoken languages in the world. Its proliferation comes with variations in written and spoken communication among different regions. Understanding language variations can help improve model performances on regional tasks, such as those involving figurative language and local context information. This manuscript presents and describes a set of regionalized resources for the Spanish language built on 4-year Twitter public messages geotagged in 26 Spanish-speaking countries. We introduce word embeddings based on FastText, language models based on BERT, and per-region sample corpora. We also provide a broad comparison among regions covering lexical and semantical similarities and examples of using regional resources on message classification tasks.

Beyond fear and anger: A global analysis of emotional response to Covid-19 news on Twitter using deep learning.

The media has been used to disseminate public information amid the Covid-19 pandemic. However, the Covid-19 news has triggered emotional responses in people that have impacted their mental well-being and led to news avoidance. To understand the emotional response to the Covid-19 news, we study user comments on the news published on Twitter by 37 media outlets in 11 countries from January 2020 to December 2022. We employ a deep-learning-based model to identify one of the 6 Ekman's basic emotions, or the absence of emotional expression, in comments to the Covid-19 news, and an implementation of Latent Dirichlet Allocation (LDA) to identify 12 different topics in the news messages. Our analysis finds that while nearly half of the user comments show no significant emotions, negative emotions are more common. Anger is the most common emotion, particularly in the media and comments about political responses and governmental actions in the United States. Joy, on the other hand, is mainly linked to media outlets from the Philippines and news on vaccination. Over time, anger is consistently the most prevalent emotion, with fear being most prevalent at the start of the pandemic but decreasing and occasionally spiking with news of Covid-19 variants, cases, and deaths. Emotions also vary across media outlets, with Fox News having the highest level of disgust, the second-highest level of anger, and the lowest level of fear. Sadness is highest at Citizen TV, SABC, and Nation Africa, all three African media outlets. Also, fear is most evident in the comments to the news from The Times of India.

Deep Learning Model for COVID-19 Sentiment Analysis on Twitter.

The COVID-19 pandemic impacted the mood of the people, and this was evident on social networks. These common user publications are a source of information to measure the population's opinion on social phenomena. In particular, the Twitter network represents a resource of great value due to the amount of information, the geographical distribution of the publications and the openness to dispose of them. This work presents a study on the feelings of the population in Mexico during one of the waves that produced the most contagion and deaths in this country. A mixed, semi-supervised approach was used, with a lexical-based data labeling technique to later bring these data to a pre-trained model of Transformers completely in Spanish. Two Spanish-language models were trained by adding to the Transformers neural network the adjustment for the sentiment analysis task specifically on COVID-19. In addition, ten other multilanguage Transformer models including the Spanish language were trained with the same data set and parameters to compare their performance. In addition, other classifiers with the same data set were used for training and testing, such as Support Vector Machines, Naive Bayes, Logistic Regression, and Decision Trees. These performances were compared with the exclusive model in Spanish based on Transformers, which had higher precision. Finally, this model was used, developed exclusively based on the Spanish language, with new data, to measure the sentiment about COVID-19 of the Twitter community in Mexico.

Detection of Depression-Related Tweets in Mexico Using Crosslingual Schemes and Knowledge Distillation.

Mental health problems are one of the various ills that afflict the world's population. Early diagnosis and medical care are public health problems addressed from various perspectives. Among the mental illnesses that most afflict the population is depression; its early diagnosis is vitally important, as it can trigger more severe illnesses, such as suicidal ideation. Due to the lack of homogeneity in current diagnostic tools, the community has focused on using AI tools for opportune diagnosis. Unfortunately, there is a lack of data that allows the use of IA tools for the Spanish language. Our work has a cross-lingual scheme to address this issue, allowing us to identify Spanish and English texts. The experiments demonstrated the methodology's effectiveness with an F1-score of 0.95. With this methodology, we propose a method to solve a classification problem for depression tweets (or short texts) by reusing English language databases with insufficient data to generate a classification model, such as in the Spanish language. We also validated the information obtained with public data to analyze the behavior of depression in Mexico during the COVID-19 pandemic. Our results show that the use of these methodologies can serve as support, not only in the diagnosis of depression, but also in the construction of different language databases that allow the creation of more efficient diagnostic tools.

Sentiment analysis in tweets: an assessment study from classical to modern word representation models.

With the exponential growth of social media networks, such as Twitter, plenty of user-generated data emerge daily. The short texts published on Twitter - the tweets - have earned significant attention as a rich source of information to guide many decision-making processes. However, their inherent characteristics, such as the informal, and noisy linguistic style, remain challenging to many natural language processing (NLP) tasks, including sentiment analysis. Sentiment classification is tackled mainly by machine learning-based classifiers. The literature has adopted different types of word representation models to transform tweets to vector-based inputs to feed sentiment classifiers. The representations come from simple count-based methods, such as bag-of-words, to more sophisticated ones, such as BERTweet, built upon the trendy BERT architecture. Nevertheless, most studies mainly focus on evaluating those models using only a small number of datasets. Despite the progress made in recent years in language modeling, there is still a gap regarding a robust evaluation of induced embeddings applied to sentiment analysis on tweets. Furthermore, while fine-tuning the model from downstream tasks is prominent nowadays, less attention has been given to adjustments based on the specific linguistic style of the data. In this context, this study fulfills an assessment of existing neural language models in distinguishing the sentiment expressed in tweets, by using a rich collection of 22 datasets from distinct domains and five classification algorithms. The evaluation includes static and contextualized representations. Contexts are assembled from Transformer-based autoencoder models that are also adapted based on the masked language model task, using a plethora of strategies.

Contesting views on mobility restrictions in urban green spaces amid COVID-19-Insights from Twitter in Latin America and Spain.

Positive sentiments towards urban green spaces (UGS) unequivocally increased worldwide amid COVID-19. In contrast, this paper documents that views on mobility restrictions applicable to UGS are of a contested nature. That is, while residents unambiguously report positive sentiments towards UGS, they do not share views on how to administer access to UGS-which is a matter of public policy. These contesting views reflect opposite demands that managers of UGS had to balance during the pandemic as they faced the challenge of reducing risk of spread while providing services that support physical and mental health of residents. The empirical analysis in this paper relies on views inferred through a text classification algorithm implemented on Twitter messages posted from January to October 2020, by urban residents in three Latin American countries-Argentina, Colombia, and Mexico-and Spain. The focus on Latin America is motivated by the documented lack of compliance with mobility restrictions; Spain works as a comparison point to learn differences with respect to other regions. Understanding and following in real-time the evolution of contesting views amid a pandemic is useful for managers and city planners to inform adaptation measures-e.g. communication strategies can be tailored to residents with specific views.

Análisis de sentimientos y tópicos sobre las vacunas cubanas Soberana 02 y Abdala en Twitter

El análisis de sentimientos o minería de opiniones es una rama de la computación que permite analizar opiniones, sentimientos y emociones en ciertas áreas de interés social como productos, servicios, organizaciones, compañías, eventos y temas de interés actual. En tal sentido se propuso identificar los sentimientos y tópicos presentes en los tweets que hicieron mención a las vacunas cubanas Soberana 02 y Abdala en la red social Twitter. Se optó por los lenguajes de programación Python y R con sus librerías específicas para la ciencia de datos. La primera parte del estudio, que abarcó desde el web scraping hasta la cuantificación de las palabras más usadas, se realizó con Python y las siguientes librerías: tweepy, pandas, re, nltk y matplotlib. Mientras que la segunda, que fue la del análisis de sentimientos y detección de tópicos, se implementó con R y se utilizó: tokenizers, tm, syuzhet, topic modeling, tidyverse, barplot y wordcloud. Se obtuvo que entre los términos con que más se dialoga en Twitter están dosis, vacunas, eficacia, cubanos, candidatos, millones, país, personas, recibido y población. En los tweets las emociones predominantes fueron el miedo y, ligeramente por encima, la confianza; en la polaridad predominó la positiva, como expresión del contexto vivido en el cual se desarrolló la campaña de vacunación. A partir de los tópicos identificados y los términos que se relacionaron con las emociones predominantes, así como por la polaridad, se aprecia consenso en torno a las vacunas Soberana 02 y Abdala.

Sentiment analysis or opinion mining is a branch of computing that allows analyzing opinions, feelings and emotions in certain areas of social interest such as products, services, organizations, companies, events and topics of current interest. In this sense, the objective of this paper was to identify the feelings and topics present in the tweets mentioning the Cuban vaccines Soberana 02 and Abdala on Twitter social network. The programming languages Python and R with their specific libraries for data science were chosen. The first part of the study, which ranged from web scraping to the quantification of the most used words, was carried out with Python and the libraries tweepy, pandas, re, nltk and matplotlib. While the second, which was the sentiment analysis and topic detection, was implemented with R and used tokenizers, tm, syuzhet, topic modeling, tidyverse, barplot, and wordcloud. It was obtained that among the terms with which there is more dialogue on Twitter are doses, vaccines, efficacy, Cubans, candidates, millions, country, people, received and population. In the tweets, the predominant emotions were fear and confidence, slightly above it; in the polarity, the positive one predominated, as an expression of the lived context in which the vaccination campaign was developed. A consensus can be perceived around the vaccines Soberana 02 and Abdala, from the identified topics and the terms that were related to the predominant emotions, as well as the polarity.

The COVID-19 Response in North America.

In our Information Technology (IT) based societies, social media plays an important role in communications and social networks for COVID-19. This study explores social responses for COVID-19 in North America, which is the most severe continent affected by the COVID-19 pandemic. This study employs social network analysis for Twitter among the US, Canada, and Mexico. This study finds that the 3 countries show different characteristics of social networks for COVID-19. For example, the Prime Minister plays the second most important role in the Canadian networks, whereas the Presidents play the most significant role in them, in the US, and Mexico. WHO shows a pivotal effect on social networks of COVID-19 in Canada and the US, whereas it does not affect them in Mexico. Canadians are interested in COVID-19 apps, the American people criticize the president and administration as incompetent in terms of COVID-19, and the Mexican people search for COVID-19 cases and the pandemic in Mexico. This study shows that governments and disease experts should understand social networks and communications of social network services, to develop effective COVID-19 policies according to the characteristics of their country.

The COVID-19 Infodemic on Twitter: A Space and Time Topic Analysis of the Brazilian Immunization Program and Public Trust.

The context of the COVID-19 pandemic has brought to light the infodemic phenomenon and the problem of misinformation. Agencies involved in managing COVID-19 immunization programs are also looking for ways to combat this problem, demanding analytical tools specialized in identifying patterns of misinformation and understanding how they have evolved in time and space to demonstrate their effects on public trust. The aim of this article is to present the results of a study applying topic analysis in space and time with respect to public opinion on the Brazilian COVID-19 immunization program. The analytical process involves applying topic discovery to tweets with geoinformation extracted from the COVID-19 vaccination theme. After extracting the topics, they were submitted to manual annotation, whereby the polarity labels pro, anti, and neutral were applied based on the support and trust in the COVID-19 vaccination. A space and time analysis was carried out using the topic and polarity distributions, making it possible to understand moments during which the most significant quantities of posts occurred and the cities that generated the most tweets. The analytical process describes a framework capable of meeting the needs of agencies for tools, providing indications of how misinformation has evolved and where its dissemination focuses, in addition to defining the granularity of this information according to what managers define as adequate. The following research outcomes can be highlighted. (1) We identified a specific date containing a peak that stands out among the other dates, indicating an event that mobilized public opinion about COVID-19 vaccination. (2) We extracted 23 topics, enabling the manual polarity annotation of each topic and an understanding of which polarities were associated with tweets. (3) Based on the association between polarities, topics, and tweets, it was possible to identify the Brazilian cities that produced the majority of tweets for each polarity and the amount distribution of tweets relative to cities populations.

Análisis de sentimiento de tweets sobre la vacuna contra el COVID-19 en países iberoamericanos hispanohablantes / Sentiment analysis of tweets about the COVID-19 vaccine in Spanish-speaking Ibero-American countries

Resumen Introducción: En este estudio se evalúa la emocionalidad asociada a la vacunación contra el COVID-19 a partir de la técnica de análisis de sentimientos de los tweets en países iberoamericanos hispanohablantes. Método: En enero de 2021 se realizó un estudio mixto observacional transversal de 41023 tweets procedentes de nueve países iberoamericanos hispanohablantes (Chile, El Salvador, Venezuela, Ecuador, Argentina, México, Panamá, Perú y España) con una fase cuantitativa y técnicas de análisis de sentimientos mediante algoritmos de inteligencia artificial y una fase cualitativa donde se realizó un análisis del discurso de los tweets cuya emocionalidad era en extremo positiva y negativa. Resultados: A partir del análisis de sentimiento de los tweets, se observó que los países presentan una emocionalidad negativa asociada a la vacunación contra el COVID-19, que se podría atribuir a la desconfianza hacia las autoridades y a la eficacia o seguridad de las vacunas, según el análisis del discurso en los tweets de emocionalidad en extremo negativa. Conclusiones: Las técnicas de análisis de sentimientos en combinación con el análisis del discurso de la emocionalidad extrema posibilitaron la monitorización de las opiniones negativas y sus posibles factores asociados en la vacunación contra el COVID-19 en los países iberoamericanos estudiados.

Abstract Introduction: This study evaluates the emotionality associated with vaccination against COVID-19 using the sentiment analysis technique of tweets in Spanish-speaking Ibero-American countries. Method: In January 2021 a mixed cross-sectional observational study of 41023 tweets from nine Spanish-speaking Ibero-American countries (Chile, El Salvador, Venezuela, Ecuador, Argentina, Mexico, Panama, Peru and Spain) was carried out with a quantitative phase and analysis techniques of feelings based on artificial intelligence algorithms and a qualitative phase where an analysis of the discourse of the tweets whose emotionality was extremely positive and negative was carried out. Results: From the sentiment analysis of the tweets, it was observed that the countries present a negative emotionality associated with the vaccination against COVID-19, which could be attributed to mistrust towards the authorities and the efficacy or safety of the vaccines, according to the analysis of the discourse in the extremely negative emotionality tweets. Conclusions: Sentiment analysis techniques in combination with extreme emotionality discourse analysis made it possible to monitor negative opinions and their possible associated factors in vaccination against COVID-19 in the Ibero-American countries studied.

Analyzing the far-right political action on Twitter: the Chilean constituent process.

The concept of "politics of the end" assumes the catastrophe of living in a world that produces new forms of accumulation and allows symbolic and semiotic capital to create value. Currently, various far-right movements worldwide seem to appropriate this concept, employing radical communication strategies as a repertoire to contest the public agenda. These strategies include the massive creation of bots on social networks to spread hate speech and coordinate ideological manifestations. This article seeks to verify the use of these strategies by the Chilean far-right on Twitter. For the above, a social network analysis approach is proposed during the current socio-political crisis in Chile, which began with the massive protests of October 2019 and led to an unprecedented constituent process. For nine months, we studied five opinion leaders on Twitter from the Chilean far-right, who together have more than 600 thousand followers and almost 130 thousand followings. Through descriptive, quantitative, and qualitative techniques, an explicit political action "from the resistance" is revealed in the activity of the network, which includes hundreds of new users and coordinated bots to disseminate identifiable discourses with strongly ideological ideas. This coordination also presents identifiable differences in how opinion leaders interact and communicate with their network environment.

Mining Public Opinions on COVID-19 Vaccination: A Temporal Analysis to Support Combating Misinformation.

This article presents a study that applied opinion analysis about COVID-19 immunization in Brazil. An initial set of 143,615 tweets was collected containing 49,477 pro- and 44,643 anti-vaccination and 49,495 neutral posts. Supervised classifiers (multinomial naïve Bayes, logistic regression, linear support vector machines, random forests, adaptative boosting, and multilayer perceptron) were tested, and multinomial naïve Bayes, which had the best trade-off between overfitting and correctness, was selected to classify a second set containing 221,884 unclassified tweets. A timeline with the classified tweets was constructed, helping to identify dates with peaks in each polarity and search for events that may have caused the peaks, providing methodological assistance in combating sources of misinformation linked to the spread of anti-vaccination opinion.

Political polarization on Twitter during the COVID-19 pandemic: a case study in Brazil.

The debate over the COVID-19 pandemic is constantly trending at online conversations since its beginning in 2019. The discussions in many social media platforms is related not only to health aspects of the disease, but also public policies and non-pharmacological measures to mitigate the spreading of the virus and propose alternative treatments. Divergent opinions regarding these measures are leading to heated discussions and polarization. Particularly in highly politically polarized countries, users tend to be divided in those in-favor or against government policies. In this work we present a computational method to analyze Twitter data and: (i) identify users with a high probability of being bots using only COVID-19 related messages; (ii) quantify the political polarization of the Brazilian general public in the context of the COVID-19 pandemic; (iii) analyze how bots tweet and affect political polarization. We collected over 100 million tweets from 26 April 2020 to 3 January 2021, and observed in general a highly polarized population (with polarization index varying from 0.57 to 0.86), which focuses on very different topics of discussions over the most polarized weeks-but all related to government and health-related events.