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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 COVID-19 pandemic accelerated e-learning adoption, presenting both opportunities and challenges for educational development, particularly in the context of Information Technology for Development (ITD). This study profiles public sentiments on e-learning to identify key factors influencing IT-enabled education. A text analysis of Tweets from the United States and India posted from March 2020 to January 2022 was conducted using a computer-aided text analysis and natural language processing alongside a content analysis using a machine learning technique. Findings show positive sentiments towards e-learning from both countries, with trust, anticipation, and joy as predominant emotion categories. Themes of strongest importance are students, schools, and education as central to e-learning discussions. The study also uncovers challenges such as the need for internet infrastructure and teacher training in digital pedagogy. These findings contribute to the broader understanding of how information technologies can address educational challenges and promote development in diverse global contexts. [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.

To what extent political parties engage in debates about the same issues and how they respond to each other is highly relevant to democratic processes. Existing research on issue engagement has uncovered several interesting patterns and factors, but has neglected one important feature of contemporary democracies: nowadays, political actors have a wide range of communication channels at their disposal with the use of ‘newer’ forms of political communication (e.g., social media) potentially transforming discursive power relations between political actors. However, it remains largely unclear whether the extent and nature of issue engagement varies between more ‘traditional’ and ‘newer’ forms of political communication. To fill this gap, I apply unsupervised topic modelling to press releases and tweets from political parties in Austria, Germany and Switzerland (January 2019–September 2021). The statistical analysis shows substantial differences in issue engagement between political parties in press releases and on Twitter, now X. First, I find a higher likelihood of issue engagement between parties in tweets. Second, Twitter appears to moderate the influence of party‐level factors on issue engagement compared to press releases. The results show that for issue engagement in tweets, the importance of party size is smaller and the role of government parties is larger than in press releases, while the role of ideological distance does not seem to change. These findings add important insights to our understanding of the potential transformative effect of new communication technologies on party competition and political discourse. [ABSTRACT FROM AUTHOR]

Intro: Communication is understood to be a pillar of democracy. Therefore, governments around the world enact laws which make it easier for politicians to communicate with their constituents. However, some governments also restrict this ability during campaign seasons as a way of unclogging the media. In the United States, congressional election blackout dates are periods in which politicians are not allowed to engage in mass unsolicited mailing of political advertising. Because the prohibition only applies to unsolicited media, we theorize that we should see the transition from one type of communication to another. Methods: To test this effect, we created a database containing all tweets by all Members of Congress from the last three Congresses. Results: We find that immediately during the blackout periods, members of Congress substantially increase the number of tweets they post. Discussion: We conclude that Members of Congress are strategic in their ability to exchange communication outlets. [ABSTRACT FROM AUTHOR]

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]

This paper analyses the use of indigenous language elements including code-switching in two contrasting genres, i.e. group chats and Twitter memes along with tweets, in the English communication of South Asian (Bangladeshi, Indian, Pakistani, and Sri Lankan) internet users. The results from group chats show that one of the most common lexical indigenous elements are tags, for example, address forms like yaar, machan, and da which all can be translated to English as 'dude' or 'buddy'. The analysis of Twitter memes along with tweets shows that despite the tweet text being in English, the South Asian users tend to employ memes with indigenous text more often as compared to English memes for political satire. Overall, the study finds that code-switching and indigenous resources are used to create a sense of localness in English communication, whether it is group chats or Twitter memes and tweets. [ABSTRACT FROM AUTHOR]

The majority of educational institutions around the world have switched to online learning due to the COVID-19 pandemic. Since continuing education has become important during the pandemic as well, academics and students have recognized the value of online learning to avoid their challenges. The objective of this study is to categorize peoples' opinions and determine how the community used online learning during the pandemic. A total of 13,155 tweets were collected using the Twitter API. Of these, 4486 were positive about the online learning process, 4490 were negative, and 4179 were advertising for online learning. After pre-processing the tweets, Term Frequency-Inverse Document Frequency (TF-IDF) vectorizer is used to extract the feature vectors. The data was divided into three categories using the Long Short Term Memory (LSTM) and Support Vector Machine (SVM) algorithms. Sentiment analysis is used to determine how society feels about the online learning process by analyzing positive, negative, and advertisement sentiments. According to the results, LSTM beat SVM and achieved an accuracy of 88.58%. It also achieved higher precision, recall, f-measure values, and lowest error rates for 65% of the training dataset. Based on the findings, the significance of online learning as well as the absence of technologies, the internet, and other subpar educational practices were determined. It was determined that more workable solutions were needed in order to improve online education globally. [ABSTRACT FROM AUTHOR]

Copyright of Alteridad: Revista de Educación is the property of Universidad Politecnica Salesiana and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Social media have become a part of our lives, and their use helps us learn about events and comment on them with certain emotions. The purpose of our study was to determine the most frequent tone (positive, negative, neutral) of comments on impactful emergency and crisis news in the Czech Republic and Ukraine on a specific topic (pandemics, war, natural disaster etc.) using the sentiment analysis method. The methods of the study included a theoretical analysis of literature, social media (Twitter, Telegram), a Python program using: large language models GPT-3.5-Turbo and Twitter-XLM-RoBERTa, processing and interpretation of results (psycholinguistic). [ABSTRACT FROM AUTHOR]

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.

Social media platforms, such as Twitter (now X), are a major source of communication. Identifying communicative intentions is useful, as it encapsulates the latent motivations that drive text creation. This intention is also helpful in understanding the message, context, and audience. This study proposes a method for detecting communicative intentions in tweets using Jakobson’s language functions. We constructed a meticulously annotated dataset, drawing from the extensive RepLab2013 corpus. Our dataset underwent rigorous scrutiny by linguistic annotators who analyzed over 12,000 tweets individually. These experts identified the dominant language function within each tweet by employing diverse strategies to ensure precise labeling quality. The outcome demonstrated a noteworthy Kappa agreement score of 0.6, reflecting a strong inter-annotator reliability. Subsequently, these functions were mapped to the corresponding intention categories. We employed logistic regression and support vector machines (SVM) algorithms to classify intention in tweets and explored various pre-processing techniques, incorporating n-grams and bag-of-words representations. Furthermore, we expanded our research using pre-trained large language models, incorporating the latest state-of-the-art techniques in natural language processing. [ABSTRACT FROM AUTHOR]

Nowadays, social media platforms are extensively used by the public to expose their opinions on various sensitive matters. One of the active research challenges in social media analytics is web content mining and context analysis. This helps us to identify events or incidents which are actively discussed among many users who may be from specific geographical areas. Such events or incident identification gives an early warning in many unusual situations [1]. However, the semantic processing of social media is challenging due to its high complexity, ambiguity, and unstructured nature. In this work, we propose a framework for crowd cluster identification and context analysis from clusters using the Online Spherical K-means algorithm and some Natural Language Processing (NLP) techniques. Initially, the tweets are scraped from Twitter and undergo suitable data preprocessing steps. Furthermore, clusters are identified from the cleaned data using the Online Spherical K-means algorithm. Finally, the analysis and visualization of context discussion from each cluster are performed with the aid of various fitting NLP methods. The proposed method is evaluated using tweets scraped with three different hashtags #blacklivesmatter, #Superbowl, and #Texasfreeze. For performance evaluation, we computed the homogeneity score, Completeness score, Calinski-Harabasz Index, and V-Measure. The performance metrics show that the proposed method yields promising results. [ABSTRACT FROM AUTHOR]

"Women are bitchy but men are sarcastic", such comments reveal the relationship between gender and sarcasm. Automatic gender identification can play a crucial role in services that depend on data about a user's background. Although for some social media users the gender of a user is typically unavailable due to privacy and anonymity. Based on the notion that male and female users may express their thoughts and sentiments differently in their posts, social media accounts can be examined using their posts (text) in order to automatically identify the gender of an anonymous user. In the current work, efforts are made in analyzing the effects of emotion and sarcasm intended by the users in their tweets for predicting gender. Sarcasm + emotion aided gender prediction systems are developed using different machine learning and neural network-based architectures. In our developed model, tweet features are extracted by using pre-trained GloVe embeddings. The sarcasm intensity is concatenated with the corresponding tweet representation and at last classification layer is used to predict the gender labels. For the experimentation purpose, the PAN-2018 dataset has been used. We have also shown the effect of utilizing emotion, and sarcasm information over gender prediction using different models. [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. [ABSTRACT FROM AUTHOR]

A large number of users around the globe have preferred to read news and the latest information from the Internet, especially social media, leaving behind the traditional approach of print media. On the one hand, the Internet is a constructive medium to spread the latest news and information briefly. On the other hand, malicious users are very active on the Internet and spread fake news, which becomes viral within a few minutes. The spread of fake news has become a serious threat as many users now rely on Internet news without verification. In this digital world, it is easy to spread any toxic information over the Internet, like hate speech, extremism, propaganda, and political agendas. It is a big challenge in today's digital world to mitigate the spread of fake news; hence, there is a need for an automatic computational tool that can assist in measuring the credibility of news. This study aims to deliver a solution where fake news from Twitter and website-based articles can be detected using the Natural Language Processing (NLP) technique, Bidirectional Encoder Representations from Transformers (BERT), other machine learning classification algorithms, and manual program-based approaches. A dataset with fake and real labels for the textual content is used. Different classification algorithms are evaluated to find a suitable algorithm for delivering a fake news detector. The evaluations are based on machine learning and a program-based approach. The textual content that the user provides, such as an article or tweet, can confirm the legitimacy of fake news. This website offers fake news detection for both website-based news articles and tweets from Twitter in English, Arabic, and Urdu. [ABSTRACT FROM AUTHOR]