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This paper investigates the role of sentiment and specific emotion analysis in forecasting donation behaviour within the context of social networking services (SNSs). The study empirically examines the influence of sentiment and specific emotion analysis on donation behaviour for two non-profit organizations (NPOs): The Fred Hollows Foundation (The Foundation) in both Australia and New Zealand, and The University of Auckland (UOA) in New Zealand. We collected and analysed 298,569 tweets from 106,349 users mentioning these NPOs, along with 5,175,359 tweets mentioning the top 20 US brands from 1,623,113 users. We found that NPOs are often associated with brands that induce joy. Furthermore, sadness expressed by marketers and joy expressed by users positively affected donations to The Foundation, while user-expressed anger positively influenced donations to UOA within the same month. A two-month rolling average analysis highlighted the significant effect of lingering negative emotions on monthly donations over time. Specific emotion analysis outperforms sentiment analysis by demonstrating a higher effect size (R 2 ). We advocate for the application of the transformer-transfer learning method for specific emotion analysis when scrutinizing large-scale social media data and devising fundraising strategies. [ABSTRACT FROM AUTHOR]

Background: One of the psychological problems that have become very prevalent in the modern world is depression, where mental health disorders have become very common. Depression, as reported by the WHO, is the second-largest factor in the worldwide burden of illnesses. As these issues grow, social media has become a tremendous platform for people to express themselves. A user's social media behavior may therefore disclose a lot about their emotional state and mental health. This research offers a novel framework for depression detection from Arabic textual data utilizing deep learning (DL), natural language processing (NLP), machine learning (ML), and BERT transformers techniques in light of the disease's high prevalence. To do this, a dataset of tweets was used, which was collected from 3 sources, as we mention later. The dataset was constructed in two variants, one with binary classification and the other with multi-classification. Results: In binary classifications, we used ML techniques such as "support vector machine (SVM), random forest (RF), logistic regression (LR), and Gaussian naive Bayes (GNB)," and used BERT transformers "ARABERT." In comparison ML with BERT transformers, ARABERT has high accuracy in binary classification with a 93.03 percent accuracy rate. In multi-classification, we used DL techniques such as "long short-term memory (LSTM)," and used BERT transformers "Multilingual BERT." In comparison DL with BERT transformers, multilingual has high accuracy in multi-classification with an accuracy of 97.8%. Conclusion: Through user-generated content, we can detect depressed people using artificial intelligence technology in a fast manner and with high accuracy instead of medical technology. [ABSTRACT FROM AUTHOR]

Event-based surveillance is crucial for the early detection and rapid response to potential public health risks. In recent years, social networking services (SNS) have been recognized for their potential role in this domain. Previous studies have demonstrated the capacity of SNS posts for the early detection of health crises and affected individuals, including those related to infectious diseases. However, the reliability of such posts, being subjective and not clinically diagnosed, remains a challenge. In this study, we address this issue by assessing the classification performance of transformer-based pretrained language models to accurately classify Japanese tweets related to heat stroke, a significant health effect of climate change, as true or false. We also evaluated the efficacy of combining SNS and artificial intelligence for event-based public health surveillance by visualizing the data on correctly classified tweets and heat stroke emergency medical evacuees in time–space and animated video, respectively. The transformer-based pretrained language models exhibited good performance in classifying the tweets. Spatiotemporal and animated video visualizations revealed a reasonable correlation. This study demonstrates the potential of using Japanese tweets and deep learning algorithms based on transformer networks for event-based surveillance at high spatiotemporal levels to enable early detection of heat stroke risks. [ABSTRACT FROM AUTHOR]

Predicting the currency exchange rate is crucial for financial agents, risk managers, and policymakers. Traditional approaches use publicly announced news on macroeconomic and financial variables as predictors of currency exchange. However, the rise of social media may have changed the source of information. For instance, tweets can help investors make informed decisions about the foreign exchange (FX) market by reflecting market sentiment and opinion. From another aspect, changes in currency exchange may incite agents to post tweets. Are tweets good predictors of currency exchange? Is the relationship between tweets and currency exchange bidirectional? We investigate the comovement/causality between the number of #dolar ("enflasyon" resp.) tweets and USDTRY currency exchange using wavelet coherence and transfer entropy (TE) to answer these questions. Wavelet coherence allows us to determine the relationship between the number of tweets and the USDTRY rate by considering the time–frequency domain. TE enables us to quantify the net information flow between the number of tweets and USDTRY. Data from October 2020 to March 2022 were used. The obtained results remain robust regardless of the frequency of retained data (daily or hourly) and the methods used (wavelet or TE). Based on our results, USDTRY is correlated with the number of #dolar tweets (#inflation) mainly in the short run and a few times in the medium run. These relationships change through time and frequency (wavelet analysis results). However, the results from TE indicate a bidirectional relationship between the #dolar (#inflation) tweets number and the USDTRY exchange rate. The influence of the exchange rate on the number of tweets is highly pronounced. Financial agents, risk managers, policymakers, and investors should then pay moderate attention to the number of #dolar (#inflation) tweets in trading/forecasting the USD–TRY exchange rate. [ABSTRACT FROM AUTHOR]

The rise of emojis has piqued the interest of many scholars over the past few decades, as is evident by a growing body of emoji literature within various fields. This study adopted a qualitative ethnographic approach to explore the pragmatic functions of emojis in the tweets of 15 female undergraduate and graduate EFL students. The focus was on the roles emojis occupied relative to speech acts, identity representation, and language change. Semi-structured interviews and non-participant observations were used as data collection methods while reflexive thematic analysis followed for the analysis. There were six pragmatic functions of emojis, namely expressing emotions, making meaning, mirroring personal beliefs, signaling familiarity, decorating the tweet, and substituting nonverbal cues. Additionally, emojis mitigated speech acts: They were chiefly used to emphasize expressives, but also to soften directives and accompany representatives and commissives. Such emojis represented the participants' identity, yet the participants' personalities and societal roles were rarely represented by these emojis due to personal preferences. The evolution of emojis from emoticons to graphicons, however, was identifiable. [ABSTRACT FROM AUTHOR]

For decades, the discoverability and visibility of a paper relied on the readership of the academic journal where the publication was issued. As public interest in biomedicine has grown, the news media have taken on an important role in spreading scientific findings. This investigation explores the potential impact of news media stories on the citations and altmetrics of a paper. A total of 2020 open-access biomedical research papers, all published in the same year, 2015, and in journals with an impact factor between 10 and 14, were investigated. The papers were split into two groups based on the sole criterion of receiving or not receiving news media coverage. Papers with news media coverage accounted for 44% of the total. They received, on average, 60% more citations, 104% more blogs, 150% more X posts, 106% more Facebook reports, 40% more Wikipedia references, 85% more videos, and 51% more Mendeley readers than papers without news media coverage. The correlation between news media outlets and increased citations and altmetrics is evident. However, the broader societal impact of news media coverage, in terms of bringing scientific matters or discoveries to the public eye, appears to be more robust when compared to the reactions of the scientific community. [ABSTRACT FROM AUTHOR]

Accurately predicting financial entity performance remains a challenge due to the dynamic nature of financial markets and vast unstructured textual data. Financial knowledge graphs (FKGs) offer a structured representation for tackling this problem by representing complex financial relationships and concepts. However, constructing a comprehensive and accurate financial knowledge graph that captures the temporal dynamics of financial entities is non-trivial. We introduce FintechKG, a comprehensive financial knowledge graph developed through a three-dimensional information extraction process that incorporates commercial entities and temporal dimensions and uses a financial concept taxonomy that ensures financial domain entity and relationship extraction. We propose a temporal and relational graph convolutional network (RGCN)-based representation for FintechKG data across multiple timesteps, which captures temporal dependencies. This representation is then combined with FinBERT embeddings through a projection layer, enabling a richer feature space. To demonstrate the efficacy of FintechKG, we evaluate its performance using the example task of financial performance prediction. A logistic regression model uses these combined features and social media embeddings for performance prediction. We classify whether the revenue will increase or decrease. This approach demonstrates the effectiveness of FintechKG combined with textual information for accurate financial forecasting. Our work contributes a systematic FKG construction method and a framework that utilizes both relational and textual embeddings for improved financial performance prediction. [ABSTRACT FROM AUTHOR]

The perception of nuclear power, while central to energy policy and sustainability endeavors, remains a subject of considerable debate, in which some claim that the expansion of nuclear technology poses threats to global security, while others argue that its access should be shared for development and energy purposes. In this study, a total of 11,256 tweets were gathered over a three-month period using a keyword-based approach through the Twitter Standard Search API, focusing on terms related to nuclear energy. The k-means clustering algorithm was employed to analyze tweets with the aim of determining the underlying sentiments and perspectives within the public domain, while t-SNE was used for visualizing cluster separation. The results show distinct clusters reflecting various viewpoints on nuclear power, with 71.94% of tweets being neutral, 14.64% supportive, and 13.42% negative. This study also identifies a subset of users who appear to be seeking unbiased information, signaling an opportunity for educational outreach. By leveraging the immediacy and pervasiveness of X (formerly known as Twitter), this research provides a timely snapshot of the prevailing attitudes toward nuclear power and offers insights for policymakers, educators, and industry stakeholders. [ABSTRACT FROM AUTHOR]

Abstract Background One of the psychological problems that have become very prevalent in the modern world is depression, where mental health disorders have become very common. Depression, as reported by the WHO, is the second-largest factor in the worldwide burden of illnesses. As these issues grow, social media has become a tremendous platform for people to express themselves. A user’s social media behavior may therefore disclose a lot about their emotional state and mental health. This research offers a novel framework for depression detection from Arabic textual data utilizing deep learning (DL), natural language processing (NLP), machine learning (ML), and BERT transformers techniques in light of the disease’s high prevalence. To do this, a dataset of tweets was used, which was collected from 3 sources, as we mention later. The dataset was constructed in two variants, one with binary classification and the other with multi-classification. Results In binary classifications, we used ML techniques such as “support vector machine (SVM), random forest (RF), logistic regression (LR), and Gaussian naive Bayes (GNB),” and used BERT transformers “ARABERT.” In comparison ML with BERT transformers, ARABERT has high accuracy in binary classification with a 93.03 percent accuracy rate. In multi-classification, we used DL techniques such as “long short-term memory (LSTM),” and used BERT transformers “Multilingual BERT.” In comparison DL with BERT transformers, multilingual has high accuracy in multi-classification with an accuracy of 97.8%. Conclusion Through user-generated content, we can detect depressed people using artificial intelligence technology in a fast manner and with high accuracy instead of medical technology.

Abstract Predicting the currency exchange rate is crucial for financial agents, risk managers, and policymakers. Traditional approaches use publicly announced news on macroeconomic and financial variables as predictors of currency exchange. However, the rise of social media may have changed the source of information. For instance, tweets can help investors make informed decisions about the foreign exchange (FX) market by reflecting market sentiment and opinion. From another aspect, changes in currency exchange may incite agents to post tweets. Are tweets good predictors of currency exchange? Is the relationship between tweets and currency exchange bidirectional? We investigate the comovement/causality between the number of #dolar (“enflasyon” resp.) tweets and USDTRY currency exchange using wavelet coherence and transfer entropy (TE) to answer these questions. Wavelet coherence allows us to determine the relationship between the number of tweets and the USDTRY rate by considering the time–frequency domain. TE enables us to quantify the net information flow between the number of tweets and USDTRY. Data from October 2020 to March 2022 were used. The obtained results remain robust regardless of the frequency of retained data (daily or hourly) and the methods used (wavelet or TE). Based on our results, USDTRY is correlated with the number of #dolar tweets (#inflation) mainly in the short run and a few times in the medium run. These relationships change through time and frequency (wavelet analysis results). However, the results from TE indicate a bidirectional relationship between the #dolar (#inflation) tweets number and the USDTRY exchange rate. The influence of the exchange rate on the number of tweets is highly pronounced. Financial agents, risk managers, policymakers, and investors should then pay moderate attention to the number of #dolar (#inflation) tweets in trading/forecasting the USD–TRY exchange rate.

Accurately predicting financial entity performance remains a challenge due to the dynamic nature of financial markets and vast unstructured textual data. Financial knowledge graphs (FKGs) offer a structured representation for tackling this problem by representing complex financial relationships and concepts. However, constructing a comprehensive and accurate financial knowledge graph that captures the temporal dynamics of financial entities is non-trivial. We introduce FintechKG, a comprehensive financial knowledge graph developed through a three-dimensional information extraction process that incorporates commercial entities and temporal dimensions and uses a financial concept taxonomy that ensures financial domain entity and relationship extraction. We propose a temporal and relational graph convolutional network (RGCN)-based representation for FintechKG data across multiple timesteps, which captures temporal dependencies. This representation is then combined with FinBERT embeddings through a projection layer, enabling a richer feature space. To demonstrate the efficacy of FintechKG, we evaluate its performance using the example task of financial performance prediction. A logistic regression model uses these combined features and social media embeddings for performance prediction. We classify whether the revenue will increase or decrease. This approach demonstrates the effectiveness of FintechKG combined with textual information for accurate financial forecasting. Our work contributes a systematic FKG construction method and a framework that utilizes both relational and textual embeddings for improved financial performance prediction.

Sentiment analysis is an automated approach which is utilized in process of analysing textual data to describe public opinion. The sentiment analysis has major role in creating impact in the day‐to‐day life of individuals. However, a precise interpretation of text still relies as a major concern in classifying sentiment. So, this research introduced Bidirectional Long Short Term Memory with Ranger AdaBelief Optimizer (Bi‐LSTM RAO) to classify sentiment of tweets. Initially, data is obtained from Twitter API, Sentiment 140 and Stanford Sentiment Treebank‐2 (SST‐2). The raw data is pre‐processed and it is subjected to feature extraction which is performed using Bag of Words (BoW) and Term Frequency‐Inverse Document Frequency (TF‐IDF). The feature selection is performed using Gazelle Optimization Algorithm (GOA) which removes the irrelevant or redundant features that maximized model performance and classification is performed using Bi LSTM–RAO. The RAO optimizes the loss function of Bi‐LSTM model that maximized accuracy. The classification accuracy of proposed method for Twitter API, Sentiment 140 and SST 2 dataset is obtained as 909.44%, 99.71% and 99.86%, respectively. These obtained results are comparably higher than ensemble framework, Robustly Optimized BERT and Gated Recurrent Unit (RoBERTa‐GRU), Logistic Regression‐Long Short Term Memory (LR‐LSTM), Convolutional Bi‐LSTM, Sentiment and Context Aware Attention‐based Hybrid Deep Neural Network (SCA‐HDNN) and Stochastic Gradient Descent optimization based Stochastic Gate Neural Network (SGD‐SGNN). [ABSTRACT FROM AUTHOR]

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As social media platforms have gained huge momentum in recent years, the amount of information generated from the social media sites is growing exponentially and gives the information retrieval systems a great challenge to extract the potential named entities. Researchers have utilized the semantic annotation mechanism to retrieve the entities from the unstructured documents, but the mechanism returns with too many ambiguous entities. In this work, the DBpedia knowledge base is adopted for entity extraction and categorization. To achieve the entity extraction task precisely, a two-step process is proposed: (a) train the unstructured datasets with Word2Vec and classify the entities into their respective categories. (b) crawl the web pages, forums, and other web sources to identifying the entities that are not present in the DBpedia. The evaluation shows the results with more precision and promising F1 score. [ABSTRACT FROM AUTHOR]

Este artigo propõe uma reflexão teórica sobre os limites do conceito de participação e sua eventual instrumentalização política em um ambiente democrático. Os pensamentos de Arsntein (1969); Carpentier (2011); Sharp (2017) e Melo et al. (2019) compõem a base da revisão bibliográfica. Além dela, foi elaborado um estudo de caso descritivo, cujo objeto de estudo é a participação das pessoas no Twitter nas ações de denúncia da tentativa de golpe no dia 08 de janeiro em Brasília. Enquanto os resultados da revisão bibliográfica apontam que a variedade de conjunturas históricas, sociais e políticas é um obstáculo para uma compreensão única da participação e sua relação com a mídia; o estudo de caso dá pistas de que a participação dos cidadãos brasileiros pelo Twitter é complexa, apresentando traços de interação, partilha de poder e participação negativa.

Abstract Background Information on social media may affect peoples’ contraceptive decision making. We performed an exploratory analysis of contraceptive content on Twitter (recently renamed X), a popular social media platform. Methods We selected a random subset of 1% of publicly available, English-language tweets related to reversible, prescription contraceptive methods posted between January 2014 and December 2019. We oversampled tweets for the contraceptive patch to ensure at least 200 tweets per method. To create the codebook, we identified common themes specific to tweet content topics, tweet sources, and tweets soliciting information or providing advice. All posts were coded by two team members, and differences were adjudicated by a third reviewer. Descriptive analyses were reported with accompanying qualitative findings. Results During the study period, 457,369 tweets about reversible contraceptive methods were published, with a random sample of 4,434 tweets used for final analysis. Tweets most frequently discussed contraceptive method decision-making (26.7%) and side effects (20.5%), particularly for long-acting reversible contraceptive methods and the depot medroxyprogesterone acetate shot. Tweets about logistics of use or adherence were common for short-acting reversible contraceptives. Tweets were frequently posted by contraceptive consumers (50.6%). A small proportion of tweets explicitly requested information (6.2%) or provided advice (4.2%). Conclusions Clinicians should be aware that individuals are exposed to information through Twitter that may affect contraceptive perceptions and decision making, particularly regarding long-acting reversible contraceptives. Social media is a valuable source for studying contraceptive beliefs missing in traditional health research and may be used by professionals to disseminate accurate contraceptive information.

The continuous developments in information technologies have resulted in a significant rise in security concerns, including cybercrimes, unauthorized access, and cyberattacks. Recently, researchers have increasingly turned to social media platforms like X to investigate cyberattacks. Analyzing and collecting news about cyberattacks from tweets can efficiently provide crucial insights into the attacks themselves, including their impacts, occurrence regions, and potential mitigation strategies. However, there is a shortage of labeled datasets related to cyberattacks. This paper describes CybAttT, a dataset of 36,071 English cyberattack-related tweets. These tweets are manually labeled into three classes: high-risk news, normal news, and not news. Our final overall Inner Annotation agreement was 0.99 (Fleiss kappa), which represents high agreement. To ensure dataset reliability and accuracy, we conducted rigorous experiments using different supervised machine learning algorithms and various fine-tuned language models to assess its quality and suitability for its intended purpose. A high F1-score of 87.6% achieved using the CybAttT dataset not only demonstrates the potential of our approach but also validates the high quality and thoroughness of its annotations. We have made our CybAttT dataset accessible to the public for research purposes. [ABSTRACT FROM AUTHOR]

Social media users are more receptive to products or events and share their thoughts through raw textual data, which is classified as semi-structured data. This data, which is presented using a variety of terminologies, is noisy by nature but yet contains important information and superfluous details, giving analysts a way to identify patterns and knowledge. This hidden information must be extracted from language data in order to make informed decisions and create strategic plans for entering new markets. Among the most prominent fields of study are natural language processing (NLP) and data mining techniques, especially when it comes to sentiment analysis—the process of identifying the feelings and insights concealed in the data. Twitter is one of the significant microblogging platform with millions of users. These users use Twitter to share sentiments using hash tags on different topics and to make status updates known as tweets. Twitter is therefore regarded as a significant real-time source and as one of the most active opinion indicators. The volume of information is produced by Twitter is enormous and manually scanning the entire data set is difficult process. The paper proposed an ensemble classifier to categorize emotion of the tweets on the basis of polarities such as positive and negative.In our study, we ensemble classifiers which is a combination of Random Forest (RF), Support Vector Machine (SVM) and Decision Tree (DT). The data is collected from Twitter API and the Twitter data is analysed autonomously to define public view on particular topic. The features obtained after the process of dimensionality reduction using LDA undergoes the stage of feature selection using Wrapper based technique. The iterative Wrapper based technique predict score for the features, the features with low score are ignored and high score is proceeded for classification. The ensemble classifier used Adaptive Boosting (AdaBoost) technique where the output from the Machine Learning (ML) classifiers are combined to produce a single output. Adaboost combines the poor classifiers and extracts the prediction value to make a better classifier. The experimental results show that the proposed ensemble classifier provides better accuracy of 93.42 % that is comparatively better than existing Convolutional Bidirectional - Long Short-Term Memory (ConvBiLSTM) classifier and Hybrid Lexicon- Naïve Bayes Classifier (HL-NBC) which produce classification accuracy of 91.53 % and 89.61 % respectively.

Background Sentiments and opinions regarding COVID-19 and the COVID-19 vaccination on Indonesian-language Twitter are scarcely reported in one comprehensive study, and thus were aimed at our study. We also analyzed fake news and facts, and Twitter engagement to understand people’s perceptions and beliefs that determine public health literacy. Methods We collected 3,489,367 tweets data from January 2020 to August 2021. We analyzed factual and fake news using the string comparison method. The difflib library was used to measure similarity. The user’s engagement was analyzed by averaging the engagement metrics of tweets, retweets, favorites, replies, and posts shared with sentiments and opinions regarding COVID-19 and COVID-19 vaccination. Result Positive sentiments on COVID-19 and COVID-19 vaccination dominated, however, the negative sentiments increased during the beginning of the implementation of restrictions on community activities (PPKM). The tweets were dominated by the importance of health protocols (washing hands, keeping distance, and wearing masks). Several types of vaccines were on top of the word count in the vaccine subtopic. Acceptance of the vaccination increased during the studied period, and the fake news was overweighed by the facts. The tweets were dynamic and showed that the engaged topics were changed from the nature of COVID-19 to the vaccination and virus mutation which peaked in the early and middle terms of 2021. The public sentiment and engagement were shifted from hesitancy to anxiety towards the safety and effectiveness of the vaccines, whilst changed again into wariness on an uprising of the delta variant. Conclusion Understanding public sentiment and opinion can help policymakers to plan the best strategy to cope with the pandemic. Positive sentiments and fact-based opinions on COVID-19, and COVID-19 vaccination had been shown predominantly. However, sufficient health literacy levels could yet be predicted and sought for further study.

Background Sentiments and opinions regarding COVID-19 and the COVID-19 vaccination on Indonesian-language Twitter are scarcely reported in one comprehensive study, and thus were aimed at our study. We also analyzed fake news and facts, and Twitter engagement to understand people’s perceptions and beliefs that determine public health literacy. Methods We collected 3,489,367 tweets data from January 2020 to August 2021. We analyzed factual and fake news using the string comparison method. The difflib library was used to measure similarity. The user’s engagement was analyzed by averaging the engagement metrics of tweets, retweets, favorites, replies, and posts shared with sentiments and opinions regarding COVID-19 and COVID-19 vaccination. Result Positive sentiments on COVID-19 and COVID-19 vaccination dominated, however, the negative sentiments increased during the beginning of the implementation of restrictions on community activities (PPKM). The tweets were dominated by the importance of health protocols (washing hands, keeping distance, and wearing masks). Several types of vaccines were on top of the word count in the vaccine subtopic. Acceptance of the vaccination increased during the studied period, and the fake news was overweighed by the facts. The tweets were dynamic and showed that the engaged topics were changed from the nature of COVID-19 to the vaccination and virus mutation which peaked in the early and middle terms of 2021. The public sentiment and engagement were shifted from hesitancy to anxiety towards the safety and effectiveness of the vaccines, whilst changed again into wariness on an uprising of the delta variant. Conclusion Understanding public sentiment and opinion can help policymakers to plan the best strategy to cope with the pandemic. Positive sentiments and fact-based opinions on COVID-19, and COVID-19 vaccination had been shown predominantly. However, sufficient health literacy levels could yet be predicted and sought for further study.

For decades, the discoverability and visibility of a paper relied on the readership of the academic journal where the publication was issued. As public interest in biomedicine has grown, the news media have taken on an important role in spreading scientific findings. This investigation explores the potential impact of news media stories on the citations and altmetrics of a paper. A total of 2020 open-access biomedical research papers, all published in the same year, 2015, and in journals with an impact factor between 10 and 14, were investigated. The papers were split into two groups based on the sole criterion of receiving or not receiving news media coverage. Papers with news media coverage accounted for 44% of the total. They received, on average, 60% more citations, 104% more blogs, 150% more X posts, 106% more Facebook reports, 40% more Wikipedia references, 85% more videos, and 51% more Mendeley readers than papers without news media coverage. The correlation between news media outlets and increased citations and altmetrics is evident. However, the broader societal impact of news media coverage, in terms of bringing scientific matters or discoveries to the public eye, appears to be more robust when compared to the reactions of the scientific community.

Background Sentiments and opinions regarding COVID-19 and the COVID-19 vaccination on Indonesian-language Twitter are scarcely reported in one comprehensive study, and thus were aimed at our study. We also analyzed fake news and facts, and Twitter engagement to understand people’s perceptions and beliefs that determine public health literacy. Methods We collected 3,489,367 tweets data from January 2020 to August 2021. We analyzed factual and fake news using the string comparison method. The difflib library was used to measure similarity. The user’s engagement was analyzed by averaging the engagement metrics of tweets, retweets, favorites, replies, and posts shared with sentiments and opinions regarding COVID-19 and COVID-19 vaccination. Result Positive sentiments on COVID-19 and COVID-19 vaccination dominated, however, the negative sentiments increased during the beginning of the implementation of restrictions on community activities (PPKM). The tweets were dominated by the importance of health protocols (washing hands, keeping distance, and wearing masks). Several types of vaccines were on top of the word count in the vaccine subtopic. Acceptance of the vaccination increased during the studied period, and the fake news was overweighed by the facts. The tweets were dynamic and showed that the engaged topics were changed from the nature of COVID-19 to the vaccination and virus mutation which peaked in the early and middle terms of 2021. The public sentiment and engagement were shifted from hesitancy to anxiety towards the safety and effectiveness of the vaccines, whilst changed again into wariness on an uprising of the delta variant. Conclusion Understanding public sentiment and opinion can help policymakers to plan the best strategy to cope with the pandemic. Positive sentiments and fact-based opinions on COVID-19, and COVID-19 vaccination had been shown predominantly. However, sufficient health literacy levels could yet be predicted and sought for further study.

Background Sentiments and opinions regarding COVID-19 and the COVID-19 vaccination on Indonesian-language Twitter are scarcely reported in one comprehensive study, and thus were aimed at our study. We also analyzed fake news and facts, and Twitter engagement to understand people’s perceptions and beliefs that determine public health literacy. Methods We collected 3,489,367 tweets data from January 2020 to August 2021. We analyzed factual and fake news using the string comparison method. The difflib library was used to measure similarity. The user’s engagement was analyzed by averaging the engagement metrics of tweets, retweets, favorites, replies, and posts shared with sentiments and opinions regarding COVID-19 and COVID-19 vaccination. Result Positive sentiments on COVID-19 and COVID-19 vaccination dominated, however, the negative sentiments increased during the beginning of the implementation of restrictions on community activities (PPKM). The tweets were dominated by the importance of health protocols (washing hands, keeping distance, and wearing masks). Several types of vaccines were on top of the word count in the vaccine subtopic. Acceptance of the vaccination increased during the studied period, and the fake news was overweighed by the facts. The tweets were dynamic and showed that the engaged topics were changed from the nature of COVID-19 to the vaccination and virus mutation which peaked in the early and middle terms of 2021. The public sentiment and engagement were shifted from hesitancy to anxiety towards the safety and effectiveness of the vaccines, whilst changed again into wariness on an uprising of the delta variant. Conclusion Understanding public sentiment and opinion can help policymakers to plan the best strategy to cope with the pandemic. Positive sentiments and fact-based opinions on COVID-19, and COVID-19 vaccination had been shown predominantly. However, sufficient health literacy levels could yet be predicted and sought for further study.

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

Background: Online wellness influencers (individuals dispensing unregulated health and wellness advice over social media) may have incentives to oppose traditional medical authorities. Their messaging may decrease the overall effectiveness of public health campaigns during global health crises like the COVID-19 pandemic. Objective: This study aimed to probe how wellness influencers respond to a public health campaign; we examined how a sample of wellness influencers on Twitter (rebranded as X in 2023) identified before the COVID-19 pandemic on Twitter took stances on the COVID-19 vaccine during 2020-2022. We evaluated the prevalence of provaccination messaging among wellness influencers compared with a control group, as well as the rhetorical strategies these influencers used when supporting or opposing vaccination. Methods: Following a longitudinal design, wellness influencer accounts were identified on Twitter from a random sample of tweets posted in 2019. Accounts were identified using a combination of topic modeling and hand-annotation for adherence to influencer criteria. Their tweets from 2020-2022 containing vaccine keywords were collected and labeled as pro- or antivaccination stances using a language model. We compared their stances to a control group of noninfluencer accounts that discussed similar health topics before the pandemic using a generalized linear model with mixed effects and a nearest-neighbors classifier. We also used topic modeling to locate key themes in influencer's pro- and antivaccine messages. Results: Wellness influencers (n=161) had lower rates of provaccination stances in their on-topic tweets (20%, 614/3045) compared with controls (n=242 accounts, with 42% or 3201/7584 provaccination tweets). Using a generalized linear model of tweet stance with mixed effects to model tweets from the same account, the main effect of the group was significant (β1=–2.2668, SE=0. 2940; P <. 001). Covariate analysis suggests an association between antivaccination tweets and accounts representing individuals (β=–0. 9591, SE=0. 2917; P =. 001) but not social network position. A complementary modeling exercise of stance within user accounts showed a significant difference in the proportion of antivaccination users by group (χ 21[N=321]=36. 1, P<. 001). While nearly half of the influencer accounts were labeled by a K-nearest neighbor classifier as predominantly antivaccination (48%, 58/120), only 16% of control accounts were labeled this way (33/201). Topic modeling of influencer tweets showed that the most prevalent antivaccination themes were protecting children, guarding against government overreach, and the corruption of the pharmaceutical industry. Provaccination messaging tended to encourage followers to take action or emphasize the efficacy of the vaccine. Conclusions: Wellness influencers showed higher rates of vaccine opposition compared with other accounts that participated in health discourse before the pandemic. This pattern supports the theory that unregulated wellness influencers have incentives to resist messaging from establishment authorities such as public health agencies. [ABSTRACT FROM AUTHOR]

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

Background Sentiments and opinions regarding COVID-19 and the COVID-19 vaccination on Indonesian-language Twitter are scarcely reported in one comprehensive study, and thus were aimed at our study. We also analyzed fake news and facts, and Twitter engagement to understand people’s perceptions and beliefs that determine public health literacy. Methods We collected 3,489,367 tweets data from January 2020 to August 2021. We analyzed factual and fake news using the string comparison method. The difflib library was used to measure similarity. The user’s engagement was analyzed by averaging the engagement metrics of tweets, retweets, favorites, replies, and posts shared with sentiments and opinions regarding COVID-19 and COVID-19 vaccination. Result Positive sentiments on COVID-19 and COVID-19 vaccination dominated, however, the negative sentiments increased during the beginning of the implementation of restrictions on community activities (PPKM). The tweets were dominated by the importance of health protocols (washing hands, keeping distance, and wearing masks). Several types of vaccines were on top of the word count in the vaccine subtopic. Acceptance of the vaccination increased during the studied period, and the fake news was overweighed by the facts. The tweets were dynamic and showed that the engaged topics were changed from the nature of COVID-19 to the vaccination and virus mutation which peaked in the early and middle terms of 2021. The public sentiment and engagement were shifted from hesitancy to anxiety towards the safety and effectiveness of the vaccines, whilst changed again into wariness on an uprising of the delta variant. Conclusion Understanding public sentiment and opinion can help policymakers to plan the best strategy to cope with the pandemic. Positive sentiments and fact-based opinions on COVID-19, and COVID-19 vaccination had been shown predominantly. However, sufficient health literacy levels could yet be predicted and sought for further study.

Twitter is regarded today as an essential communication platform of U.S. diplomacy. Of all diplomatic tweets, those published by U.S. presidents carry the greatest weight and hold great potential to influence perceptions of the country. In this study, we conduct cross-presidential comparative analyses on an original dataset of over 2,000 tweets published by the first two presidents of the Twitter era. In particular, we test the commonly held notion that the substance and tone of Barack Obama's communication reflected positively on America's image abroad, with the potential to expand soft power—a vital foreign policy asset—while Donald Trump's communication reflected negatively on America's image, potentially eroding the nation's image and its soft power. Findings demonstrate that what and how presidents communicate on Twitter may produce profound and disparate impacts on America's image abroad and on U.S. soft power. [ABSTRACT FROM AUTHOR]

In the last fifteen years, an immense expansion has been witnessed in mobile app usage and production. The intense competition in the tech sector and also the rapidly and constantly evolving user requirements have led to increased burden on mobile app creators. Nowadays, fulfilling users' expectations cannot be readily achieved and new and unconventional approaches are needed to permit an interested crowd of users to contribute in the introduction of creative mobile apps. Indeed, users and developers of mobile apps are the most influential candidates to engage in any of the requirements engineering activities. The place where both can best be found is on Twitter, one of the most widely used social media platforms. More interestingly, Twitter is considered as a fertile ground for textual content generated by the crowd that can assist in building robust predictive classification models using machine learning (ML) and natural language processing (NLP) techniques. Therefore, in this study, we have built two classification models that can identify mobile apps users and developers using tweets. A thorough empirical comparison of different feature extraction techniques and machine learning classification algorithms were experimented with to find the best-performing mobile app user and developer classifiers. The results revealed that for mobile app user classification, the highest accuracy achieved was ≈0.86, produced via logistic regression (LR) using Term Frequency Inverse Document Frequency (TF-IDF) with N-gram (unigram, bigram and trigram), and the highest precision was ≈0.86, produced via LR using Bag-of-Words (BOW) with N-gram (unigram and bigram). On the other hand, for mobile app developer classification, the highest accuracy achieved was ≈0.87, produced by random forest (RF) using BOW with N-gram (unigram and bigram), and the highest precision was ≈0.88, produced by multi-layer perception neural network (MLP NN) using BERTweet for feature extraction. According to the results, we believe that the developed classification models are efficient and can assist in identifying mobile app users and developers from tweets. Moreover, we envision that our models can be harnessed as a crowd selection approach for crowdsourcing requirements engineering activities to enhance and design inventive and satisfying mobile apps. [ABSTRACT FROM AUTHOR]

Background: Sentiments and opinions regarding COVID-19 and the COVID-19 vaccination on Indonesian-language Twitter are scarcely reported in one comprehensive study, and thus were aimed at our study. We also analyzed fake news and facts, and Twitter engagement to understand people’s perceptions and beliefs that determine public health literacy. Methods: We collected 3,489,367 tweets data from January 2020 to August 2021. We analyzed factual and fake news using the string comparison method. The difflib library was used to measure similarity. The user’s engagement was analyzed by averaging the engagement metrics of tweets, retweets, favorites, replies, and posts shared with sentiments and opinions regarding COVID-19 and COVID-19 vaccination. Result: Positive sentiments on COVID-19 and COVID-19 vaccination dominated, however, the negative sentiments increased during the beginning of the implementation of restrictions on community activities (PPKM). The tweets were dominated by the importance of health protocols (washing hands, keeping distance, and wearing masks). Several types of vaccines were on top of the word count in the vaccine subtopic. Acceptance of the vaccination increased during the studied period, and the fake news was overweighed by the facts. The tweets were dynamic and showed that the engaged topics were changed from the nature of COVID-19 to the vaccination and virus mutation which peaked in the early and middle terms of 2021. The public sentiment and engagement were shifted from hesitancy to anxiety towards the safety and effectiveness of the vaccines, whilst changed again into wariness on an uprising of the delta variant. Conclusion: Understanding public sentiment and opinion can help policymakers to plan the best strategy to cope with the pandemic. Positive sentiments and fact-based opinions on COVID-19, and COVID-19 vaccination had been shown predominantly. However, sufficient health literacy levels could yet be predicted and sought for further study.

The unprecedented rise in the number of COVID-19 cases has drawn global attention, as it has caused an adverse impact on the lives of people all over the world. As of December 31, 2021, more than 2, 86, 901, 222 people have been infected with COVID-19. The rise in the number of COVID-19 cases and deaths across the world has caused fear, anxiety and depression among individuals. Social media is the most dominant tool that disturbed human life during this pandemic. Among the social media platforms, Twitter is one of the most prominent and trusted social media platforms. To control and monitor the COVID-19 infection, it is necessary to analyze the sentiments of people expressed on their social media platforms. In this study, we proposed a deep learning approach known as a long short-term memory (LSTM) model for the analysis of tweets related to COVID-19 as positive or negative sentiments. In addition, the proposed approach makes use of the firefly algorithm to enhance the overall performance of the model. Further, the performance of the proposed model, along with other state-of-the-art ensemble and machine learning models, has been evaluated by using performance metrics such as accuracy, precision, recall, the AUC-ROC and the F1-score. The experimental results reveal that the proposed LSTM + Firefly approach obtained a better accuracy of 99.59% when compared with the other state-of-the-art models.

There are millions of People Living with HIV/AIDS (PLWHA) globally and over the years, addressing their concerns has been topical for many stakeholders. It is a well-known and established fact that PLWHA are at increased risk of victimization and stigmatization. Unfortunately, the world experienced an outbreak of the COVID-19 pandemic that has led to strict social measures in many states. Thus, it is the goal of this research to study the impact that the outbreak and its mitigation measures have had on the PLWHA. Specifically, we sought to highlight their concerns from sentiments expressed on social media based on posted tweets. By combining machine learning (ML) techniques such as textual mining and thematic analysis, we determined 14 major themes as factors that are worth exploring. In this work, we originally extracted 2,839,091 tweets related to HIV/AIDS posted from March 2020 to April 2022. After initially doing data cleaning and preprocessing, we performed topic modeling using the Latent Dirichlet Allocation (LDA) topic model to extract 25 topics that are made up of 30 keywords each. The topics were then narrowed into 14 themes. The paper details the negative, positive, and neutral sentiment polarities which we highlight as concerning. These sentiments were determined using the Valence Aware Dictionary and sEntiment Reasoner (VADER) Sentiment Analysis Library with a 90% F1-score compared to TextBlob which showed a 53% F1-score. The research findings highlight issues affecting PLWHA during and post-pandemic such as high cost of medical care, late diagnosis of HIV, limited access to medications, stigmatization and victimization, absence of testing kits in hospitals, and lack of urgency in the development of vaccines or cure for HIV.

Online social media such as Twitter has been used as an important source for predicting, detecting, or analyzing critical social phenomena such as elections, disease outbreaks, and cyberattacks. In this study, we propose a cyberattack detection model on social media. First, we conduct community detection of users related to cyberattacks on Twitter to identify the most relevant group to the cyberattacks. Second, to effectively identify the tweets related to cyberattacks, we conduct a textual similarity analysis between the tweet and the cyberattack relevant keywords, which overcomes the limitation of lexical analysis of tweets such as keyword-based filtering and frequency of keywords. Finally, we propose a novel cyberattack detection model by integrating both text- and graph-based models. Our methodology has a distinguishing feature from the existing studies in that we incorporate the semantics in Tweets to evaluate the relevance with cyberattacks and employ community detection to identify the most relevant group to the cyberattacks. Through extensive experiments, we show the effectiveness of the proposed model. First, we show that the text analysis in the proposed model outperforms detection accuracy of the keyword frequency-based analysis by up to 29.46%. Second, the community detection improves the detection accuracy by 28.89∼35.56% compared to the baseline criteria to select relevant users to the cyberattacks. Through two experiments to measure the relevancy of detected communities to the cyberattack, the results consistently show that the highest relevant community by our community detection shows the highest relevancy.

The mining of Tweets to develop datasets on recent issues, global challenges, pandemics, virus outbreaks, emerging technologies, and trending matters has been of significant interest to the scientific community in the recent past, as such datasets serve as a rich data resource for the investigation of different research questions. Furthermore, the virus outbreaks of the past, such as COVID-19, Ebola, Zika virus, and flu, just to name a few, were associated with various works related to the analysis of the multimodal components of Tweets to infer the different characteristics of conversations on Twitter related to these respective outbreaks. The ongoing outbreak of the monkeypox virus, declared a Global Public Health Emergency (GPHE) by the World Health Organization (WHO), has resulted in a surge of conversations about this outbreak on Twitter, which is resulting in the generation of tremendous amounts of Big Data. There has been no prior work in this field thus far that has focused on mining such conversations to develop a Twitter dataset. Furthermore, no prior work has focused on performing a comprehensive analysis of Tweets about this ongoing outbreak. To address these challenges, this work makes three scientific contributions to this field. First, it presents an open-access dataset of 556,427 Tweets about monkeypox that have been posted on Twitter since the first detected case of this outbreak. A comparative study is also presented that compares this dataset with 36 prior works in this field that focused on the development of Twitter datasets to further uphold the novelty, relevance, and usefulness of this dataset. Second, the paper reports the results of a comprehensive analysis of the Tweets of this dataset. This analysis presents several novel findings; for instance, out of all the 34 languages supported by Twitter, English has been the most used language to post Tweets about monkeypox, about 40,000 Tweets related to monkeypox were posted on the day WHO declared monkeypox as a GPHE, a total of 5470 distinct hashtags have been used on Twitter about this outbreak out of which #monkeypox is the most used hashtag, and Twitter for iPhone has been the leading source of Tweets about the outbreak. The sentiment analysis of the Tweets was also performed, and the results show that despite a lot of discussions, debate, opinions, information, and misinformation, on Twitter on various topics in this regard, such as monkeypox and the LGBTQI+ community, monkeypox and COVID-19, vaccines for monkeypox, etc., “neutral” sentiment was present in most of the Tweets. It was followed by “negative” and “positive” sentiments, respectively. Finally, to support research and development in this field, the paper presents a list of 50 open research questions related to the outbreak in the areas of Big Data, Data Mining, Natural Language Processing, and Machine Learning that may be investigated based on this dataset.

In recent years, computer science has advanced exponentially, helping significantly to identify and classify text extracted from social networks, specifically Twitter. This work identifies, classifies, and analyzes tweets related to real natural disasters through tweets with the hashtag #Nat-uralDisasters, using Machine learning (ML) algorithms, such as Bernoulli Naive Bayes (BNB), Multinomial Naive Bayes (MNB), Logistic Regression (LR), K-Nearest Neighbors (KNN), Decision Tree (DT), Random Forest (RF). First, tweets related to natural disasters were identified, creating a dataset of 122k geo-located tweets for training. Secondly, the data-cleaning process was carried out by applying stemming and lemmatization techniques. Third, exploratory data analysis (EDA) was performed to gain an initial understanding of the data. Fourth, the training and testing process of the BNB, MNB, L, KNN, DT, and RF models was initiated, using tools and libraries for this type of task. The results of the trained models demonstrated optimal performance: BNB, MNB, and LR models achieved a performance rate of 87% accuracy; and KNN, DT, and RF models achieved performances of 82%, 75%, and 86%, respectively. However, the BNB, MNB, and LR models performed better with respect to performance on their respective metrics, such as processing time, test accuracy, precision, and F1 score. Demonstrating, for this context and with the trained dataset that they are the best in terms of text classifiers. [ABSTRACT FROM AUTHOR]

COVID-19 is a novel virus that presents challenges due to a lack of consistent and in-depth research. The news of the COVID-19 spreads across the globe, resulting in a flood of posts on social media sites. Apart from health, social, and economic disturbances brought by the COVID-19 pandemic, another important consequence involves public mental health crises which is of greater concern. Data related to COVID-19 is a valuable asset for researchers in understanding people's feelings related to the pandemic. It is thus important to extract the early information evolving public sentiments on social platforms during the outbreak of COVID-19. The objective of this study is to look at people's perceptions of the COVID-19 pandemic who interact with each other and share tweets on the Twitter platform. COVIDSenti, a large-scale benchmark dataset comprising 90,000 COVID-19 tweets collected from February to March 2020, during the initial phases of the outbreak served as the foundation for our experiments. A pre-trained bidirectional encoder representations from transformers (BERT) model is fine-tuned and embeddings generated are combined with two long short-term memory networks to propose the residual encoder transformation network model. The proposed model is used for multiclass text classification on a large dataset labeled as positive, negative, and neutral. The experimental outcomes validate that: (1) the proposed model is the best performing model, with 98% accuracy and 96% F1-score; (2) It also outperforms conventional machine learning algorithms and different variants of BERT, and (3) the approach achieves better results as compared to state-of-the-art on different benchmark datasets. [ABSTRACT FROM AUTHOR]

Today social media became a communication line among people to share their happiness, sadness, and anger with their end-users. It is necessary to know people's emotions are very important to identify depressed people from their messages. Early depression detection helps to save people's lives and other dangerous mental diseases. There are many intelligent algorithms for predicting depression with high accuracy, but they lack the definition of such cases. Several machine learning methods help to identify depressed people. But the accuracy of existing methods was not satisfactory. To overcome this issue, the deep learning method is used in the proposed method for depression detection. In this paper, a novel Deep Learning Multi-Aspect Depression Detection with Hierarchical Attention Network (MDHAN) is used for classifying the depression data. Initially, the Twitter data was preprocessed by tokenization, punctuation mark removal, stop word removal, stemming, and lemmatization. The Adaptive Particle and grey Wolf optimization methods are used for feature selection. The MDHAN classifies the Twitter data and predicts the depressed and non-depressed users. Finally, the proposed method is compared with existing methods such as Convolutional Neural Network (CNN), Support Vector Machine (SVM), Minimum Description Length (MDL), and MDHAN. The suggested MDH-PWO architecture gains 99.86% accuracy, more significant than frequency-based deep learning models, with a lower false-positive rate. The experimental result shows that the proposed method achieves better accuracy, precision, recall, and F1-measure. It also minimizes the execution time. [ABSTRACT FROM AUTHOR]

The continuous developments in information technologies have resulted in a significant rise in security concerns, including cybercrimes, unauthorized access, and cyberattacks. Recently, researchers have increasingly turned to social media platforms like X to investigate cyberattacks. Analyzing and collecting news about cyberattacks from tweets can efficiently provide crucial insights into the attacks themselves, including their impacts, occurrence regions, and potential mitigation strategies. However, there is a shortage of labeled datasets related to cyberattacks. This paper describes CybAttT, a dataset of 36,071 English cyberattack-related tweets. These tweets are manually labeled into three classes: high-risk news, normal news, and not news. Our final overall Inner Annotation agreement was 0.99 (Fleiss kappa), which represents high agreement. To ensure dataset reliability and accuracy, we conducted rigorous experiments using different supervised machine learning algorithms and various fine-tuned language models to assess its quality and suitability for its intended purpose. A high F1-score of 87.6% achieved using the CybAttT dataset not only demonstrates the potential of our approach but also validates the high quality and thoroughness of its annotations. We have made our CybAttT dataset accessible to the public for research purposes.

Social media has become a valuable tool for disseminating cancer prevention information. However, the design of messages for achieving wide dissemination remains poorly understood. We conducted a multi-method study to identify the effects of sender type (individuals or organizations) and content type (personal experiences or factual information) on promoting the spread of cervical cancer prevention messages over social media. First, we used observational Twitter data to examine correlations between sender type and content type with retweet activity. Then, to confirm the causal impact of message properties, we constructed 900 experimental tweets according to a 2 (sender type) by 2 (content type) factorial design and tested their probabilities of being shared in an online platform. A total of 782 female participants were randomly assigned to 87 independent 9-person online groups and each received a unique message feed of 100 tweets drawn from the 4 experimental cells over 5 days. We conducted both tweet-level and group-level analyses to examine the causal effects of tweet properties on influencing sharing behaviors. Personal experience tweets and organizational senders were associated with more retweets. However, the experimental study revealed that informational tweets were shared significantly more (19%, 95% CI: 11 to 27) than personal experience tweets; and organizational senders were shared significantly more (10%, 95% CI: 3 to 18) than individual senders. While rare personal experience messages can achieve large success, they are generally unsuccessful; however, there is a reproducible causal effect of messages that use organizational senders and factual information for achieving greater peer-to-peer dissemination.

Purpose. Microblogging sites are being used by people across the globe to share their opinions and to express sentiments for everything in real time. Through social listening, companies analyse the sentiments to assess the way forward, and the researchers use it to analyse the trend or an event and give forward-looking recommendations. The objective of the paper is to analyse the sentiments of people relating to Paytm IPO which can be used to predict the way forward. Design/methodology/approach. The study attempts sentiment analysis. For this purpose, QSR NVIVO 12, the qualitative analysis tool was used to retrieve the tweets from the Twitter website. NCapture was installed for this purpose. Post data cleaning, stemming, query augmentation and classification, the Twitter data was analysed. Findings. The sentiments around the IPO of Paytm have been negative and sarcastic. The extremely negative tweets were near twice the number of extremely positive tweets. Practical implication. The study can help an investor in evaluating the investment that they might be planning in the given company. For the company, whose IPO is being considered, an analysis of the sentiments around the IPO can help in taking corrective measures, if the sentiment is negative, towards reputation building. Originality/value. The study is an original contribution to the extant literature in the field of sentiment analysis.

During the COVID-19 pandemic, the value of palliative care has become more evident than ever. The current study quantitatively investigated the perceptions of palliative care emerging from the pandemic experience by analyzing a total of 26,494 English Tweets collected between 1 January 2020 and 1 January 2022. Such an investigation was considered invaluable in the era of more people sharing and seeking healthcare information on social media, as well as the emerging roles of palliative care. Using a web scraping method, we reviewed 6000 randomly selected Tweets and identified four themes in the extracted Tweets: (1) Negative Impact of the Pandemic on Palliative Care; (2) Positive Impact of the Pandemic on Palliative Care; (3) Recognized Benefits of Palliative Care; (4) Myth of Palliative Care. Although a large volume of Tweets focused on the negative impact of COVID-19 on palliative care as expected, we found almost the same volume of Tweets that were focused on the positive impact of COVID-19 on palliative care. We also found a smaller volume of Tweets associated with myths about palliative care. Using these manually classified Tweets, we trained machine learning (ML) algorithms to automatically classify the remaining tweets. The automatic classification of Tweets was found to be effective in classifying the negative impact of the COVID-19. [ABSTRACT FROM AUTHOR]

The aim of this paper is to develop a model to classify the stance expressed in social media texts. More specifically, the work presented focuses on tweets. In stance detection (SD) tasks, the objective is to identify the stance of a person towards a target of interest. In this paper, a model for SD is established and its variations are evaluated using different classifiers. The single models differ based on the pre-processing and the combination of features. To reduce the dimensionality of the feature space, analysis of variance (ANOVA) test is used. Then, two classifiers are employed as base learners including Random Forests (RF) and Support Vector Machines (SVM). Experimental analyses are conducted on SemEval dataset that is used as a benchmark for SD. Finally, the base learners that resulted from different design alternatives, are combined into three ensemble models. Experimental results show the significance of the used features and the effectiveness of a manually built dictionary that is used in the pre-processing stage. Moreover, the proposed ensembles outperform the state-of-the-art models in the overall test score, which suggests that ensemble learning is the best tool for effective SD in tweets. [ABSTRACT FROM AUTHOR]

This study aimed to analyse public sentiments of UK-originated tweets related to COVID-19 vaccines, and it applied six chronological time periods, between January and December 2021. The dates were related to six BBC news reports about the most significant developments in the three main vaccines that were being administered in the UK at the time: Pfizer-BioNTech, Moderna, and Oxford-AstraZeneca. Each time period spanned seven days, starting from the day of the news report. The study employed the bidirectional encoder representations from transformers (BERT) model to analyse the sentiments in 4172 extracted tweets. The BERT model adopts the transformer architecture and uses masked language and next sentence prediction models. The results showed that the overall sentiments for all three vaccines were negative across all six periods, with Moderna having the least negative tweets and the highest percentage of positive tweets overall while AstraZeneca attracted the most negative tweets. However, for all the considered time periods, Period 3 (23–29 May 2021) received the least negative and the most positive tweets, following the related BBC report—'COVID: Pfizer and AstraZeneca jabs work against Indian variant'—despite reports of blood clots associated with AstraZeneca during the same time period. Time periods 5 and 6 had no breaking news related to COVID vaccines, and they reflected no significant changes. We, therefore, concluded that the BBC news reports on COVID vaccines significantly impacted public sentiments regarding the COVID-19 vaccines. [ABSTRACT FROM AUTHOR]

Nowadays, Climate change is an important environmental factor that affects every living thing on the earth. It is very essential to study the public perceptions regarding the disaster events frequently happening due to climate change. In today's digital era individuals are using social network platforms namely Twitter, Facebook, and Weibo now and then to express their views about any events. In this paper, the climate change Twitter data set was considered for analyzing the topics and the opinions discussed by the public regarding climate change. The Latent Dirichlet Allocation(LDA) method was used to list out the various topics present in the data set and the Bidirectional Encoder Representation from Transformers(BERT uncased) is an efficient deep learning technique used to classify the sentiments present in the data set. Here the sentiments were labelled as pro news, support, neutral and anti. The performance of the proposed topic modelling and sentiment classification model was compared using the precision, recall, and accuracy measures. The BERT uncased model with has shown the best results such as precision of 91.35%, recall of 89.65%, and accuracy of 93.50% compared to other methods. [ABSTRACT FROM AUTHOR]

Monkeypox is a rare disease caused by the monkeypox virus. This disease was considered eradicated in 1980 and was believed to affect rodents and not humans. However, recent years have seen a massive outbreak of monkeypox in humans, setting off worldwide alerts from health agencies. As of September 2022, the number of confirmed cases in Peru had reached 1964. Although most monkeypox patients have been discharged, we cannot neglect the monitoring of the population with respect to the monkeypox virus. Lately, the population has started to express their feelings and opinions through social media, specifically Twitter, as it is the most used social medium and is an ideal space to gather what people think about the monkeypox virus. The information imparted through this medium can be in different formats, such as text, videos, images, audio, etc. The objective of this work is to analyze the positive, negative, and neutral feelings of people who publish their opinions on Twitter with the hashtag #Monkeypox. To find out what people think about this disease, a hybrid-based model architecture built on CNN and LSTM was used to determine the prediction accuracy. The prediction result obtained from the total monkeypox data was 83% accurate. Other performance metrics were also used to evaluate the model, such as specificity, recall level, and F1 score, representing 99%, 85%, and 88%, respectively. The results also showed the polarity of feelings through the CNN-LSTM confusion matrix, where 45.42% of people expressed neither positive nor negative opinions, while 19.45% expressed negative and fearful feelings about this infectious disease. The results of this work contribute to raising public awareness about the monkeypox virus. [ABSTRACT FROM AUTHOR]

Social media is a platform where people communicate, share content, and build relationships. Due to the current pandemic, many people are turning to social networks such as Facebook, WhatsApp, Twitter, etc., to express their feelings. In this paper, we analyse the sentiments of Indian citizens about the COVID-19 pandemic and vaccination drive using text messages posted on the Twitter platform. The sentiments were classified using deep learning and lexicon-based techniques. A lexicon-based approach was used to classify the polarity of the tweets using the tools VADER and NRCLex. A recurrent neural network was trained using Bi-LSTM and GRU techniques, achieving 92.70% and 91.24% accuracy on the COVID-19 dataset. Accuracy values of 92.48% and 93.03% were obtained for the vaccination tweets classification with Bi-LSTM and GRU, respectively. The developed models can assist healthcare workers and policymakers to make the right decisions in the upcoming pandemic outbreaks. [ABSTRACT FROM AUTHOR]

This paper combines political/poststructuralist discourse theory with actor–network theory to explore dystopian visions in the context of a discourse around the hashtag #NotDying4Wallstreet. The call for protest against former US president Donald Trump's demand to reopen the economy during lockdown dominates the discourse. The tweets were analyzed with quantitative discourse analysis and network analysis to identify key terms and meaning clusters leading to two main conclusions. The first (A) is an imaginary dystopic future with an accelerated neoliberal order. Human lives, especially elderly people, are sacrificed for a well-functioning economy in this threat scenario. The second (B) includes the motive of protest and the potential of the people's demands to unite and rally against this threat. Due to the revelation of populist features, this (online) social movement seems to be populist without a leader figure. The empirical study is used to propose a research approach toward a mixed-methods design based on a methodological discussion and the enhancement of PDT with ANT. Thus, the article has a double aim: an update of contemporary approaches to social media analysis in discourse studies and its empirical demonstration with a study. [ABSTRACT FROM AUTHOR]