Twitter Publications Research Articles

Controversy about the use and financing of SYSADOA for osteoarthritis in Spain: An analysis of the scientific-social debate in the media

Background and objectiveThe use of SYmptomatic Slow-Acting Drugs for Osteoarthritis (SYSADOA) in the treatment of osteoarthritis (OA) has been a topic of debate in the scientific community and public entities regarding their public financing in Spain. The objective of this study was to describe and analyse the main positions of media outlets, public entities, regarding the use and financing of SYSADOA in Spain. MethodsA qualitative and quantitative analysis of the content regarding the use and financing of SYSADOA was conducted in general media outlets (El País, El Mundo, La Vanguardia, ABC, and 20minutos), public statements, and Twitter publications. ResultsA total of 15 articles in general media outlets, 872 tweets, and 7 public entity statements were identified. Mostly, media outlets (91%) and social media platforms (78%) exhibited a favorable trend towards funding. Discussion and conclusionsThe use of SYSADOA in OA patients continues to be controversial in the scientific community. However, there is consensus among patient associations in favour of public funding and use as a treatment for OA patients.

Unraveling Microblog Sentiment Dynamics: A Twitter Public Attitudes Analysis towards COVID-19 Cases and Deaths

The identification and analysis of sentiment polarity in microblog data has drawn increased attention. Researchers and practitioners attempt to extract knowledge by evaluating public sentiment in response to global events. This study aimed to evaluate public attitudes towards the spread of COVID-19 by performing sentiment analysis on over 2.1 million tweets in English. The implications included the generation of insights for timely disease outbreak prediction and assertions regarding worldwide events, which can help policymakers take suitable actions. We investigated whether there was a correlation between public sentiment and the number of cases and deaths attributed to COVID-19. The research design integrated text preprocessing (regular expression operations, (de)tokenization, stopwords), sentiment polarization analysis via TextBlob, hypothesis formulation (null hypothesis testing), and statistical analysis (Pearson coefficient and p-value) to produce the results. The key findings highlight a correlation between sentiment polarity and deaths, starting at 41 days before and expanding up to 3 days after counting. Twitter users reacted to increased numbers of COVID-19-related deaths after four days by posting tweets with fading sentiment polarization. We also detected a strong correlation between COVID-19 Twitter conversation polarity and reported cases and a weak correlation between polarity and reported deaths.

From “Bangtan Boys” to “International Relations Professor”: Mapping Self‐Identifications in the UN’s Twitter Public

Digitalization and social media established world-encompassing publics that engage with international organizations. While scholarship has analyzed how international organizations communicate with such digital publics, this article determines who participates in these publics. We created a novel dataset to map the UN’s digital public on Twitter and analyzed the bios of 243,168 accounts that have interacted with the UN. Members of this public provide self-identifications (such as researcher, consultant, or scientist) that indicate a professional interest in the UN. We analyzed clusters of users that self-identify with similar words. We find high heterogeneity in the UN’s digital public: Clusters of professional, academic, and organizational users suggest that the technocratic history of international organizations reflects in the members of its digital public. At the same time, the digital public of the UN extends to very different groups (human rights activists and K-Pop fans feature in the UN’s public on Twitter). We demonstrate for future research how multiple correspondence analysis can reveal clusters in unstructured biographical data. The article contributes the first analysis of self-identifications in digital publics of global politics.

Neurofeedback on twitter: Evaluation of the scientific credibility and communication about the technique

Neurofeedback is a popular technique to induce neuroplasticity with a controversial reputation. The public discourse on neurofeedback, as a therapeutic and neuroenhancement technique, encompasses scientific communication, therapeutic expectations and outcomes, as well as complementary and alternative practices. We investigated twitter publications from 2010 to 2022 on the keyword “neurofeedback”. A total of over 138 k tweets were obtained, which originated from over 42 k different users. The communication flow in the neurofeedback community is mainly unidirectional and non-interactive. Analysis of hashtags revealed application fields, therapy provider and neuroenhancement to be the most popular contents in neurofeedback communication. A group of 1221 productive users was identified, in which clinicians, entrepreneurs, broadcasters, and scientists contribute. We identified reactions to critical publications in the twitter traffic and an increase in the number of tweets by academic users which suggest an increase in the interest on the scientific credibility of neurofeedback. More intense scientific communication on neurofeedback in twitter may contribute to promote a more realistic view on challenges and advances regarding good scientific practice of neurofeedback.

Разработка и исследование алгоритмов машинного обучения для решения задачи классификации в публикациях Twitter

Посты в социальных сетях способны как предсказывать движение финансового рынка, так и в некоторых случаях даже определять его направление. Анализ постов в Twitter способствует прогнозированию цен на криптовалюту. Специфика рассматриваемого сообщества заключается в особенной лексике. Так, в постах используются сленговые выражения, аббревиатуры и сокращения, наличие которых затрудняет векторизацию текстовых данных, в следствие чего рассматриваются методы предобработки такие, как лемматизация Stanza и применение регулярных выражений. В этой статье описываются простейшие модели машинного обучения, которые могут работать, несмотря на такие проблемы, как нехватка данных и короткие сроки прогнозирования. Решается задача бинарной текстовой классификации, в условиях которой слово рассматривается как элемент бинарного вектора единицы данных. Базисные слова определяются на основе частотного анализа упоминаний того или иного слова. Разметка составляется на основе свечей Binance с варьируемыми параметрами для более точного описания тренда изменения цены. В работе вводятся метрики, отражающие распределение слов в зависимости от их принадлежности к положительному или отрицательному классам. Для решения задачи классификации использовались dense-модель с подобранными при помощи Keras Tuner параметрами, логистическая регрессия, классификатор случайного леса, наивный байесовский классификатор, способный работать с малочисленной выборкой, что весьма актуально для нашей задачи, и метод k-ближайших соседей. Было проведено сравнение построенных моделей на основе метрики точности предсказанных меток. В ходе исследования было выяснено, что наилучшим подходом является использование моделей, которые предсказывают ценовые движения одной монеты. Наши модели имеют дело с постами, содержащими упоминания проекта LUNA, которого на данный момент уже не существует. Данный подход к решению бинарной классификации текстовых данных широко применяется для предсказания цены актива, тренда ее движения, что часто используется в автоматизированной торговле.

Разработка и исследование жесткого алгоритма анализа публикаций в Twitter и их влияния на движение рынка криптовалют

Посты в социальных сетях являются важным индикатором, отображающим положение активов на финансовом рынке. В статье описывается жесткое решение задачи классификации для определения влияния активности в социальных сетях на движение финансового рынка. Отбираются аккаунты авторитетных в сообществе крипто-трейдеров-инфлюенсеров. В качестве данных используются специальные пакеты сообщений, которые состоят из текстовых постов, взятых из Twitter. Приведены способы предобработки текста, заключающиеся в лемматизации Stanza и применении регулярных выражений, для очищения зашумленных текстов, особенностью которых является многочисленное употребление сленговых слов и сокращений. Решается задача бинарной классификации, где слово рассматривается как элемент вектора единицы данных. Для более точного описания криптовалютной активности ищутся наилучшие параметры разметки для обработки свечей Binance. Методы выявления признаков, необходимых для точного описания текстовых данных и последующего процесса установления зависимости, представлены в виде машинного обучения и статистического анализа. В качестве первого используется отбор признаков на основе критерия информативности, который применяется при разбиении решающего дерева на поддеревья. Такой подход реализован в модели случайного леса и актуален для задачи выбора значимых для «стрижки деревьев» признаков. Второй же основан на жестком составлении бинарного вектора в ходе грубой проверки наличия либо отсутствия слова в пакете и подсчете суммы элементов этого вектора. Затем принимается решение в зависимости от преодоления этой суммой порогового значения, базирующегося на уровне, предварительно подобранном с помощью анализа частотного распределения упоминаний слова. Алгоритм, используемый для решения проблемы, был назван бенчмарком и проанализирован в качестве инструмента. Подобные алгоритмы часто используются в автоматизированных торговых стратегиях. В процессе исследования также описаны наблюдения влияния часто встречающихся в тексте слов, которые используются в качестве базиса размерностью 2 и 3 при векторизации.

Разработка и исследование алгоритма выделения признаков в публикациях Twitter для задачи классификации с известной разметкой

Посты социальных сетей играют важную роль в отражении ситуации на финансовом рынке, а их анализ является мощным инструментом ведения торговли. В статье описан результат исследования влияния деятельности социальных медиа на движение финансового рынка. Сначала отбирается топ инфлюенсеров, активность которых считается авторитетной в криптовалютном сообществе. Сообщения в Twitter используются в качестве данных. Подобные тексты обычно сильно зашумлены, так как включают сленг и сокращения, поэтому представлены методы подготовки первичных текстовых данных, включающих в себя обработку Stanza, регулярными выражениями. Рассмотрено два подхода представления момента времени в формате текстовых данных. Так исследуется влияние либо одного твита, либо целого пакета, состоящего из твитов, собранных за определенный период времени. Также рассмотрен статистический подход в виде частотного анализа, введены метрики, способные отразить значимость того или иного слова при выявлении зависимости между изменением цены и постами в Twitter. Частотный анализ подразумевает исследование распределений встречаемости различных слов и биграмм в тексте для положительного, отрицательного либо общего трендов. Для построения разметки изменения на рынке перерабатываются в бинарный вектор с помощью различных параметров, задавая таким образом задачу бинарной классификации. Параметры для свечей Binance подбираются для лучшего описания движения рынка криптовалюты, их вариативность также исследуется в данной статье. Оценка эмоционального окраса текстовых данных изучается с помощью Stanford Core NLP. Результат статистического анализа представляет непосредственно практический интерес, так как предполагает выбор признаков для дальнейшей бинарной или мультиклассовой задач классификации. Представленные методы анализа текста способствуют повышению точности моделей, решающих задачи обработки естественного языка, с помощью отбора слов, улучшения качества векторизации. Такие алгоритмы зачастую используются в автоматизированных торговых стратегиях для предсказания цены актива, тренда ее движения.

Twitter Public Sentiment on Fintech by Using Drone Emprit

Public trust is the main key to the success of a company's product. Businesses can find out how much their products are trusted by the community through reviews as well as various public opinions that are poured on social media. This research aims to map the sentiments of people in the world towards Fintech recorded on social media platforms Twitter. The results of this study are that people show negative sentiment towards fintech. as much as 9,324 mentions negative twitter users and as many as 2,708 mentions positive towards fintech. The fintech industry especially in Indonesia, in this case, needs to respond well to things that are weaknesses so that public trust can be earned.

Sentiment Analysis of Hate Speech on Twitter Public Figures with AdaBoost and XGBoost Methods

Public figures are often scrutinized by social media users, either because of what they say or even because of their role in a television series. Generally, public figures upload something on their social media accounts to help shape their image. But not everyone who sees it is happy. Some even dislike the upload. This study aims to determine public sentiment towards public figure Anya Geraldine conveyed on Twitter in Indonesian. The classification process in this study uses the Adaptive Boosting (AdaBoost) and Extreme Gradient Boosting (XGBoost) classification methods with text preprocessing using cleaning, case folding, tokenization, and filtering. The data used are tweets in Indonesian with the keyword ”@anyaselalubenar”, with a total dataset of 7,475 tweets divided into 6,887 positive and 588 negative tweets. From the label results using oversampling to avoid excessive overfitting problems. The feature used is TF-IDF weighting. Four experimental scenarios were carried out to validate the effectiveness of the model used: first model performance without oversampling, second model performance with oversampling, third model performance with undersampling, and fourth model performance with Hyperparameter tune. The experimental results show that XGBoost+SMOTE+Hyperparameter achieved 95% compared to AdaBoost+SMOTE+Hyperparameter of 87%. The application of SMOTE and Hyperparameter tune is proven to overcome the problem of data imbalance and get better classification results.

Can Fake News Detection Models Maintain the Performance through Time? A Longitudinal Evaluation of Twitter Publications

The negative impact of false information on social networks is rapidly growing. Current research on the topic focused on the detection of fake news in a particular context or event (such as elections) or using data from a short period of time. Therefore, an evaluation of the current proposals in a long-term scenario where the topics discussed may change is lacking. In this work, we deviate from current approaches to the problem and instead focus on a longitudinal evaluation using social network publications spanning an 18-month period. We evaluate different combinations of features and supervised models in a long-term scenario where the training and testing data are ordered chronologically, and thus the robustness and stability of the models can be evaluated through time. We experimented with 3 different scenarios where the models are trained with 15-, 30-, and 60-day data periods. The results show that detection models trained with word-embedding features are the ones that perform better and are less likely to be affected by the change of topics (for example, the rise of COVID-19 conspiracy theories). Furthermore, the additional days of training data also increase the performance of the best feature/model combinations, although not very significantly (around 2%). The results presented in this paper build the foundations towards a more pragmatic approach to the evaluation of fake news detection models in social networks.

Pandemics, Protests, and Publics

As an integral component of public discourse, Twitter is among the main data sources for scholarship in this area. However, there is much that scholars do not know about the basic mechanisms of public discourse on Twitter, including the prevalence of various modes of communication, the types of posts users make, the engagement those posts receive, or how these things vary with user demographics and across different topical events. This paper broadens our understanding of these aspects of public discourse. We focus on the first nine months of 2020, studying that period as a whole and giving particular attention to two monumentally important topics of that time: the Black Lives Matter movement and the COVID-19 pandemic. Leveraging a panel of 1.6 million Twitter accounts matched to U.S. voting records, we examine the demographics, activity, and engagement of 800,000 American adults who collectively posted nearly 300 million tweets during this time span. We find notable variation in user activity and engagement, in terms of modality (e.g., retweets vs. replies), demographic subgroup, and topical context. We further find that while Twitter can best be understood as a collection of interconnected publics, neither topical nor demographic variation perfectly encapsulates the "Twitter public." Rather, Twitter publics are fluid, contextual communities which form around salient topics and are informed by demographic identities. Together, this paper presents a disaggregated, multifaceted description of the demographics, activity, and engagement of American Twitter users in 2020.

Framing Twitter Public Sentiment on Nigerian Government COVID-19 Palliatives Distribution Using Machine Learning

Sustainable development plays a vital role in information and communication technology. In times of pandemics such as COVID-19, vulnerable people need help to survive. This help includes the distribution of relief packages and materials by the government with the primary objective of lessening the economic and psychological effects on the citizens affected by disasters such as the COVID-19 pandemic. However, there has not been an efficient way to monitor public funds’ accountability and transparency, especially in developing countries such as Nigeria. The understanding of public emotions by the government on distributed palliatives is important as it would indicate the reach and impact of the distribution exercise. Although several studies on English emotion classification have been conducted, these studies are not portable to a wider inclusive Nigerian case. This is because Informal Nigerian English (Pidgin), which Nigerians widely speak, has quite a different vocabulary from Standard English, thus limiting the applicability of the emotion classification of Standard English machine learning models. An Informal Nigerian English (Pidgin English) emotions dataset is constructed, pre-processed, and annotated. The dataset is then used to classify five emotion classes (anger, sadness, joy, fear, and disgust) on the COVID-19 palliatives and relief aid distribution in Nigeria using standard machine learning (ML) algorithms. Six ML algorithms are used in this study, and a comparative analysis of their performance is conducted. The algorithms are Multinomial Naïve Bayes (MNB), Support Vector Machine (SVM), Random Forest (RF), Logistics Regression (LR), K-Nearest Neighbor (KNN), and Decision Tree (DT). The conducted experiments reveal that Support Vector Machine outperforms the remaining classifiers with the highest accuracy of 88%. The “disgust” emotion class surpassed other emotion classes, i.e., sadness, joy, fear, and anger, with the highest number of counts from the classification conducted on the constructed dataset. Additionally, the conducted correlation analysis shows a significant relationship between the emotion classes of “Joy” and “Fear”, which implies that the public is excited about the palliatives’ distribution but afraid of inequality and transparency in the distribution process due to reasons such as corruption. Conclusively, the results from this experiment clearly show that the public emotions on COVID-19 support and relief aid packages’ distribution in Nigeria were not satisfactory, considering that the negative emotions from the public outnumbered the public happiness.

КОМУНІКАТИВНІ ТА ЛІНГВІСТИЧНІ ЗАСОБИ, ВИКОРИСТАНІ В ПЕРСОНАЛЬНОМУ БРЕНДИНГУ ІЛОНА МАСКА (НА МАТЕРІАЛІ ПУБЛІКАЦІЙ У TWITTER ТА ОНЛАЙН-ПУБЛІКАЦІЙ)

This research is dedicated to the study of Elon Musk’s personal brand. Communicative and linguistic devices used by Elon Musk to build, maintain and boost his personal brand, as well as to counter threats to its status, were analyzed on the basis of Twitter and online publications within the framework of the present research. The study traced the conflict between Elon Musk and short sellers with the participation of the US Securities and Exchange Commission and proved that regardless of the financial losses and legal challenges the businessman faced, the reaction of Elon Musk’s followers was mostly positive due to the effective brand-building techniques employed by Elon Musk. The conducted Twitter analysis dealt with both Elon Musk’s personal publications and the responses of his followers, and found that the main linguistic and communicative devices of Elon Musk’s personal branding were provocative tweets, popular culture references, connotatively charged notions, irony, allusions, acronyms and wordplay. As part of the present research, we studied how the linguistic phenomenon shaped reality, as Elon Musk took a literary technique and used it as a real-life enhancement of his personal brand. According to the results of the research, these devices were aimed at reinstating the credibility and authority of Elon Musk’s personal brand, attracting the attention of the followers, winning their support and counterattacking the opponents in order to re-establish a powerful personal brand. The research proves that these devices found positive response among Elon’s followers and had a positive effect on his brand attracting attention and boosting popularity and recognizability of Elon Musk’s personal brand.

Shaping Facebook and Twitter Public Spheres: The Case of Swiss Franc Mortgage Holders in Poland

The article focuses on the online communication on Polish Facebook and Twitter around issues related to mortgage loans in Swiss francs. Based on 20k posts retrieved from both Facebook and Twitter, we found the most active actors and the most common topics appearing on those micro-public spheres. It was revealed that actors with institutional affiliation and those who are not affiliated discuss different problems and share dissimilar content related to Swiss franc debts. It was also found that certain categories of actors may influence online discussions on both social networking sites by promoting specific content in order to pursue their institutional interests. Finally, the diversity of topics and problems discussed by the two categories of actors identified on Facebook and Twitter suggest that Swiss franc mortgage holders are a good example of “connective action” with no clear identity or community traits that led to favourable judgment of the European Court of Justice.

Educational Influencers – What Can We Learn from Them?

Social networks have positioned themselves in today’s society as the means through which teachers interact, communicate and share knowledge. The aim of the present study was to explore the format and contents present in Twitter publications that Spanish “educational influencers” use in their interaction with their followers. This investigation presents a qualitative design with a sample of 10 Spanish influencers of the educational scope, who were selected for having over 15,000 followers and over 5,000 tweets published. A qualitative analysis based on an inductive system of categories revealed that the topics that are most frequently addressed by these influencers are the complaints, concerns and reflections of teachers in online teaching, teaching strategies, activities and learning resources.

Research Synthesis and Thematic Analysis of Twitter Through Bibliometric Analysis

In literature, there is a shortage of comprehensive documents that can provide proper details about Twitter in research community. This study conducted a first descriptive bibliometric analysis to examine the most influential journals, institutions, and countries on Twitter. Similarly, bibliometric mapping analysis is carried out to explore different research themes in Twitter publications. VOSviewer was employed to process the 11,006 Twitter publications retrieved from the Web of Science (WoS) from 2009 to 2018. Obtained results suggest that USA and China received the highest number of publications on Twitter research, while the University of Illinois was the most productive institute. Furthermore, the five major themes have emerged in Twitter publications, and its remarkable role has been found in event detection, sentiment analysis, education, health, politics, and crisis as well as risk management. The authors believe that this study will open new doors for researchers to use online Twitter social networking communities in beauty salons, consulting companies, banks, and airlines.

The Use of Social Digital Networks by NGDO from a Social Marketing Perspective

The social marketing paradigm has been changing due to the use of digital social networks. This causes Non-Governmental Development Organizations’ efforts to focus on achieving a greater reaction from the public in these communication channels. We propose that the way forward is to analyze aspects of messages that give rise to a greater response from the audience. In this regard, we have analyzed 3608 Facebook and Twitter publications with the combination of content analysis and correlation analysis. We have considered three aspects: purpose, theme, and quality of the message. We have also listed a breakdown of quality and purpose parameters in order to become more fully acquainted with these aspects. The objectives of this research are firstly to carry out the communication profiles of the NGDOs studied from the points of view of the organizations and the public. Secondly, to analyze the reaction from the public (interactions) measured by the sum of likes plus the number of shares for each post, on Facebook and Twitter, according the parameters considered. The results showed that the most published messages from the organizations do not usually coincide with those that have the most impact on the public. Another proven aspect is that Twitter posts about behavior have more effectiveness than informative messages. Likewise, quality aspects, such as hashtags, mentions, or links, are not succeeding in generating public reaction.

The usage of Twitter from the Spanish media during the elections

Traditional media sources have adopted Twitter as a canal to broadcast information across digital audiences. This study has the objective of analyzing the characteristics of the tweets published by Spanish media sources during the twelve months in which presidential electoral processes took place. This was done to test the differences in the use of this platform, according to the content of the coverage, and the phase in which it was published. Our research uses a quantitative methodology, where we used computerized content analysis for Twitter publications that different media made during 2015 and 2016. Our results revealed a promotional use of tools such as the hashtag, or mentions in media with less followers. At the same time, political information was characterized by containing a higher use of mentions and statements by the candidates, especially during the election campaigning, favoring the creation of echo chambers in the political Twittersphere.

We Don't Know What We Don't Know: When and How the Use of Twitter's Public APIs Biases Scientific Inference

Though Twitter research has proliferated, no standards for data collection have crystallized. When using keyword queries, the most common data sources—the Search and Streaming APIs—rarely return the full population of tweets, and scholars do not know whether their data constitute a representative sample. This paper seeks to provide the most comprehensive look to-date at the potential biases that may result. Employing data derived from four identical keyword queries to the Firehose (which provides the full population of tweets but is cost-prohibitive), Streaming, and Search APIs, we use Kendall’s-tau and logit regression analyses to understand the differences in the datasets, including what user and content characteristics make a tweet more or less likely to appear in sampled results. We find that there are indeed systematic differences that are likely to bias scholars’ findings in almost all datasets we examine, and we recommend significant caution in future Twitter research.

Statistical and Semantic Approaches for Tweet Contextualization

Abstract Microblogging sites, like Twitter, have emerged as a popular platform for expressing opinions. Bound to 140 characters, Twitter's publications (tweets) are very short and not always written maintaining formal grammar and proper spelling. The spelling variations increase the likelihood of vocabulary mismatch and make the tweets difficult to understand without some kind of context. The task of tweet contextualization was organized around these issues. It aims to provide an automatic readable context explaining a given tweet, in order to help the reader understand this latter. In this paper, we describe statistical and semantic approaches for the tweet contextualization task. While the statistical one is based on association rules mining, the semantic one uses Wikipedia as an external knowledge source. The effectiveness of our approaches is proved through an experimental study conducted on the INEX 2013 collection.