Automated fact-checking has received widespread attention in recent years. In this paper, we present Factify, a transformer-based fact-checking approach that retrieves textual evidence from a trusted source for a given claim and subsequently uses the retrieved evidence to judge the validity of the claim. Our retrieval model is based on extractive question-answering and to train the model, we fine-tune various pre-trained BERT models using the FEVER fact-checking dataset. Our claim verification model is also BERT-based and is again trained using the FEVER dataset. Our evaluation using the test data of the FEVER dataset demonstrates the effectiveness of our approach in retrieving relevant textual evidences for given claims from trusted sources, and for determining whether claims are true or false based on the retrieved evidences, compared to various state-of-the-art automated fact-checking approaches.

The substantial volume of unstructured social media data generated during disasters often conceals critical information. Developing efficient methods to extract actionable insights from this data can significantly enhance emergency response and resource allocation. However, existing methods, primarily reliant on supervised learning, encounter challenges such as dependence on labeled data, limited adaptability, and scalability. To overcome these limitations, we present KeyMinES, an unsupervised model that extracts minimal keyphrases—bigrams and tokens—from social media data to identify and classify critical sub-events. Our approach integrates semantic and grammar-based reconstruction to ensure that the extracted keyphrases are both grammatically correct and contextually meaningful. Through clustering, we group these reconstructed sub-events, enabling the identification of patterns and offering actionable insights for decision-makers. Our experimental results, attained through quantitative and qualitative evaluations, demonstrate that KeyMinES outperforms baseline methods, achieving higher F1 scores and providing a scalable and cost-effective solution. Our ablation study reveals that combining bigram+token enhances sub-event detection compared to using only bigram or token, capturing both contextual relationships and granular details, thereby leading to more accurate identification of critical sub-events. This model holds significant potential for various stakeholders, including emergency responders and humanitarian organizations, by improving the extraction of actionable insights during disasters.

During emergencies where time is of the essence, efficient management of disasters depends on swiftly recognizing relevant and urgent information from online platforms like X (Twitter), which is imperative for augmenting established response frameworks, such as the 911 emergency system. This paper introduces CURD, a Context-aware Relevance and Urgency Determination system designed to enhance the efficiency of disaster response. The system addresses two critical challenges: filtering out irrelevant data and assessing the urgency of relevant information. Our approach includes a multi-level annotation process for event type, relevancy, and an urgency annotation algorithm that significantly improves information extraction accuracy and efficiency. CURDdl, our classifier, uses a deep learning pipeline architecture with a combination of transformer models, a convolution layer, and custom attention mechanisms to classify disaster-related tweets into multiclass-event type, binary-relevance-and-urgency categories, and rank urgent ones based on significance. Experimental results show that our best baseline classifiers for all three tasks achieved ≥ 88% F1 and accuracy, and ≥ 94%. AUC. Our models also outperformed models from related works in all metrics, validating the effectiveness of CURD in prioritizing response messages that will facilitate decision-making and resource allocation in disaster scenarios. CURD annotated dataset and code are available on GitHub 1.

Sports have been extensively studied for their impact on people’s emotional well-being, with research revealing that they have the ability to reduce anxiety and unhappiness while boosting positive emotions1. Among all sports, soccer stands out as the most popular and controversial2, eliciting a wide range of emotional reactions from fans, players, officials, and spectators, particularly on social media. While sentiment classifications such as positive, negative, and neutral have been extensively studied, low-level emotions, which refer to more specific and granular emotional states beyond the three basic categories, have yet to be given much attention. This study scraped over 300,000 tweets during the 2022 FIFA World Cup in Qatar to gain a more in-depth understanding of human emotions. We use the dataset to finetune transformer models such as pre-trained BERT and RoBERTa to detect high and low-level emotions in the collected tweets. Pre-trained $\mathrm{RoBERTa}_{\mathrm{Sent3}}$ was initially used to predict three high-level classes. Three models were then trained using one model with 28 and two models with 27 GoEmotions labels from Reddit comments to break the high-level sentiment into low-level emotions. Impressively, these models outperformed GoEmotions paper classification research in terms of precision and F1-score for BER$\mathrm{T}_{\mathrm{Emot}28}$, and they predicted less neutral labels compared to $\mathrm{RoBERTa}_{\mathrm{Sent3}}$. To further improve the classification accuracy, BER$\mathrm{T}_{\mathrm{Emot}27}$ and RoBERT$\mathrm{a}_{\mathrm{Emot}27}$ were used to reclassify BER$\mathrm{T}_{\mathrm{Emot}28}$, and these models achieved better performance in terms of precision, recall, and F1-score compared to other models. BER$\mathrm{T}_{\mathrm{Emot}27}$ was particularly effective in eliminating false neutral classifications, reclassifying the predicted neutral label class from BER$\mathrm{T}_{\mathrm{Emot}28}$ into other classes and achieving retrieval counts of 50%, 51%, 53%, and 53% for weeks 1, 2, 3, and 4 respectively. This study stresses the importance of using a nuanced approach to emotion classification and provides valuable insights into the emotional landscape of a major sporting event. It demonstrates that tweets initially perceived as neutral contained other emotional content, highlighting the need for further research.1Mental Health Benefits of Sports2The Most Popular Sports In The World