PANACEA: An Automated Misinformation Detection System on COVID-19

In this demo, we introduce a web-based misinformation detection system PANACEA on COVID-19 related claims, which has two modules, fact-checking and rumour detection. Our fact-checking module, which is supported by novel natural language inference methods with a self-attention network, outperforms state-of-the-art approaches. It is also able to give automated veracity assessment and ranked supporting evidence with the stance towards the claim to be checked. In addition, PANACEA adapts the bi-directional graph convolutional networks model, which is able to detect rumours based on comment networks of related tweets, instead of relying on the knowledge base. This rumour detection module assists by warning the users in the early stages when a knowledge base may not be available. © 2023 Association for Computational Linguistics.

Exploring Social Media for Early Detection of Depression in COVID-19 Patients

The COVID-19 pandemic has caused substantial damage to global health. Even though three years have passed, the world continues to struggle with the virus. Concerns are growing about the impact of COVID-19 on the mental health of infected individuals, who are more likely to experience depression, which can have long-lasting consequences for both the affected individuals and the world. Detection and intervention at an early stage can reduce the risk of depression in COVID-19 patients. In this paper, we investigated the relationship between COVID-19 infection and depression through social media analysis. Firstly, we managed a dataset of COVID-19 patients that contains information about their social media activity both before and after infection. Secondly, We conducted an extensive analysis of this dataset to investigate the characteristic of COVID-19 patients with a higher risk of depression. Thirdly, we proposed a deep neural network for early prediction of depression risk. This model considers daily mood swings as a psychiatric signal and incorporates textual and emotional characteristics via knowledge distillation. Experimental results demonstrate that our proposed framework outperforms baselines in detecting depression risk, with an AUROC of 0.9317 and an AUPRC of 0.8116. Our model has the potential to enable public health organizations to initiate prompt intervention with high-risk patients. © 2023 ACM.

Covid-19 Vaccination Stance Detection Using Natural Language Processing and Machine-Learning Algorithms

The coronavirus outbreak has resulted in unprecedented measures, forcing authorities to make decisions related to establishing lockdowns in areas most affected by the pandemic. Social Media have supported people during this difficult time. On November 9, 2020, when the first vaccine with an efficacy rate over 90% was announced, social media reacted and people around the world began to express their feelings about this vaccination. This paper aims to analyze the dynamics of opinion on COVID-19 vaccination, in which the civil society is highly manifested in the vaccination process. We compared classical machine learning algorithms to select the best performing classifier. 4,392 tweets were collected and analyzed. The proposed approach can help governments create and evaluate appropriate communication tools to provide clear and relevant information to the general public, increasing public confidence in vaccination campaigns. © 2022 Copyright for this paper by its authors.

Fake News Detection Using Transformer and Ensemble Learning Models

On social media, misinformation can spread quickly, posing serious problems. Understanding the content and sensitive nature of fake news and misinformation is critical to prevent the damage caused by them. To this end, the characteristics of information must first be discerned. In this paper, we propose a transformer-based hybrid ensemble model to detect misinformation on the Internet. First, false and true news on Covid-19 were analyzed, and various text classification tasks were performed to understand their content. The results were utilized in the proposed hybrid ensemble learning model. Our analysis revealed promising results, establishing the capability of the proposed system to detect misinformation on social media. The final model exhibited an excellent F1 score (0.98) and accuracy (0.97). The AUC (Area Under The Curve) score was also high at 0.98, and the ROC (Receiver Operating Characteristics) curve revealed that the true-positive rate of the data was close to one in this model. Thus, the proposed hybrid model was demonstrated to be successful in recognizing false information online. © 2022 IEEE.

Analyzing COVID-Related Social Discourse on Twitter using Emotion, Sentiment, Political Bias, Stance, Veracity and Conspiracy Theories

Online misinformation has become a major concern in recent years, and it has been further emphasized during the COVID-19 pandemic. Social media platforms, such as Twitter, can be serious vectors of misinformation online. In order to better understand the spread of these fake-news, lies, deceptions, and rumours, we analyze the correlations between the following textual features in tweets: emotion, sentiment, political bias, stance, veracity and conspiracy theories. We train several transformer-based classifiers from multiple datasets to detect these textual features and identify potential correlations using conditional distributions of the labels. Our results show that the online discourse regarding some topics, such as COVID-19 regulations or conspiracy theories, is highly controversial and reflects the actual U.S. political landscape. © 2023 ACM.

Towards a smart pervasive conversational agent for COVID-19 psychological assistance based on NLP

Diverse countries throughout the world were quar-antined due to the novel pandemic known as COVID-19, even after vaccination,. As a result of this grim circumstance, most socioeconomic and political spheres have encountered deep crisis and from there people have experienced stress, anxiety, depression, and even suicide, In this paper, we propose a smart pervasive conversational agent for psychological assistance during and after COVID-19 quarantine, which could converse with a regular citizen to raise awareness of the genuine threat of the outbreak and the importance of vaccination. Our proposed conversational agent could be able to recognize and manage stress and anxiety using natural language understanding (NLU) and international stress and anxiety scales. The messages given by our agent and its mode of communication may help to alleviate anxiety following the world's lockdown. Our agent's comment threads and management styles may be able to soothe people's worry during the world's lockdown. Our proposed approach is a mobile healthcare service with three interdependent units: an input processing (IP) that performs natural language understanding (NL), a Storage that stores every interaction, and a response manager (RM) that controls the responses of our conversational agent. © 2022 IEEE.

Text Mining and Emotion Classification on Monkeypox Twitter Dataset: A Deep Learning-Natural Language Processing (NLP) Approach

Emotion classification has become a valuable tool in analyzing text and emotions people express in response to events or crises, particularly on social media and other online platforms. The recent news about monkeypox highlighted various emotions individuals felt during the outbreak. People’s opinions and concerns have been very different based on their awareness and understanding of the disease. Although there have been studies on monkeypox, emotion classification related to this virus has not been considered. As a result, this study aims to analyze the emotions individual expressed on social media posts related to the monkeypox disease. Our goal is to provide real-time information and identify critical concerns about the disease. To conduct our analysis, first, we extract and preprocess 800,000 datasets and then use NRCLexicon, a Python library, to predict and measure the emotional significance of each text. Secondly, we develop deep learning models based on Convolutional Neural Networks (CNN), Long Short-Term Memory (LSTM), Bidirectional LSTM (BiLSTM), and the combination of Convolutional Neural Networks and Long Short-Term Memory (CLSTM) for emotion classification. We use SMOTE (Synthetic Minority Oversampling Technique) and Random Undersampling techniques to address the class imbalance in our training dataset. The results of our study revealed that the CNN model achieved the highest performance with an accuracy of 96%. Overall, emotion classification on the monkeypox dataset can be a powerful tool for improving our understanding of the disease. The findings of this study will help develop effective interventions and improve public health. Author

Towards Detection of Cyberbullying in Hinglish Code Mixed Data

Covid-19 eruption and lockdown situation have increased the usages of online platforms which have impacted the users. Cyberbullying is one of the negative outcomes of using social media platforms which leads to mental and physical distress. This study proposes a machine learning-based approach for the detection of cyberbullying in Hinglish text. We use the Hinglish Code-Mixed Corpus, which consists of over 6,000 tweets, for our experiments. We use various machine learning algorithms, including Logistic regression (LR), Multinomial Naive Bayes (MNB), Support vector machine (SVM), Random Forest (RF), to train our models. We evaluate the performance of the models using standard evaluation metrics such as precision, recall, and F1-score. Our experiments show that the LR with Term Frequency-Inverse Document Frequency (TFIDF) outperforms the other models, achieving 92% accuracy. Our study demonstrates that machine learning models can be effective for cyberbullying detection in Hinglish text, and the proposed approach can help identify and prevent cyberbullying on social media platforms. © 2023 Bharati Vidyapeeth, New Delhi.

Sentiment Analysis on Twitter Data of Omicron (B.l.l.529) using Natural Language Processing

The COVID pandemic is causing outrageous interference in everyday life and financial activity. Close to two years after the presence of COVID, WHO allotted the variety B.l.l.529 a variety of concern, named 'Omicron'. Online diversion data assessment is created and transformed into a more renowned subject of investigation. In this paper, a sizably voluminous heap of appraisals and assessments are culminated with online redirection information. The evaluations and appearances of Twitter electronic diversion stage clients are summarised and researched by considering sentiment analysis by utilising various natural language processing techniques based on positive, negative, and neutral tweets. All potential outcomes are considered for investigating the feelings of Twitter clients. For the most part, tweets are assessed clearly, and this assessment ensures the headway of this investigation study. Different kinds of analyzers are utilised and measured. The 'TextBlob Sentiment Analyzer' has given the highest polarity score based on positivity, negativity, and neutrality rates in terms of inspiration, pessimism, and impartiality. A total dataset is fully determined and classified with all the analyzers, and a comparative result is also measured to find the ideal analyzer. It is intended to apply boosting machine learning methods to increase the accuracy of the proposed architecture before further implementation. © 2022 IEEE.

Need for Vision: A data-centric approach towards analysing impact of COVID-19

From the beginning of 2020, we saw a rise of a new virus called the Coronavirus and ultimately a pandemic that anyone reading this paper must have been through. With the rise of COVID,many vaccines were found, the global vaccination drive as a result of this naturally fueled a possibility of Pro-Vaxxers and Anti-Vaxxers strongly expressing their support and concerns regarding the vaccines on social media platforms and along with this came up the need of quick identification of people who are experiencing COVID-19 symptoms. So in this paper, an effort has been made to facilitate the understanding of all these complications and help the concerned authorities. With the help of data in the form of Covid-19 tweets, a (machine-learning) classifier has been built which can classify users as per their vaccine related stance and also classify users who have reported their symptoms through tweets. © FIRE 2022: Forum for Information Retrieval Evaluation.

Moral Narratives Around the Vaccination Debate on Facebook

Vaccine hesitancy is a complex issue with psychological, cultural, and even societal factors entangled in the decision-making process. The narrative around this process is captured in our everyday interactions;social media data offer a direct and spontaneous view of peoples' argumentation. Here, we analysed more than 500,000 public posts and comments from Facebook Pages dedicated to the topic of vaccination to study the role of moral values and, in particular, the understudied role of the Liberty moral foundation from the actual user-generated text. We operationalise morality by employing the Moral Foundations Theory, while our proposed framework is based on recurrent neural network classifiers with a short memory and entity linking information. Our findings show that the principal moral narratives around the vaccination debate focus on the values of Liberty, Care, and Authority. Vaccine advocates urge compliance with the authorities as prosocial behaviour to protect society. On the other hand, vaccine sceptics mainly build their narrative around the value of Liberty, advocating for the right to choose freely whether to adhere or not to the vaccination. We contribute to the automatic understanding of vaccine hesitancy drivers emerging from user-generated text, providing concrete insights into the moral framing around vaccination decision-making. Especially in emergencies such as the Covid-19 pandemic, contrary to traditional surveys, these insights can be provided contemporary to the event, helping policymakers craft communication campaigns that adequately address the concerns of the hesitant population. © 2023 ACM.

Analysis of Learners' Sentiments on MOOC Forums using Natural Language Processing Techniques

MOOCs have gained a lot of popularity for past few years. Especially after the outbreak of Coronavirus, everyone is trying to gain some knowledge and skill while being at the comfort of home and making themselves safe. Due to sudden increase in the number of participants on MOOCs there is a need for an automated system to be able to assess the reviews and feedbacks given by the learners and find the sentiments behind their statements. This analysis will help trainers identify their shortcoming and make their courses even better. For sentiments analysis, several approaches may be used. This research aims to provide a system which will perform sentiments analysis on the novel dataset and show the comparison of lexicon-based vs transformer-based sentiment analysis models. For lexicon based, VADER was chosen and for transformer-based, state-of-The-Art BERT was chosen. BERT was found to be exceptionally good with an accuracy of 84% and F1-score of 0.64. © 2022 IEEE.

Sentiment Analysis on COVID-19 Vaccination in Bangladesh

The COVID-19 pandemic has caused a shocking loss of life on a worldwide scale and influenced every sector of Bangladesh very badly. The simplest method for preventing infectious diseases is vaccination. Bangladeshi netizens discuss their opinions, feelings, and experiences associated with the COVID-19 vaccination program on social media platforms. The purpose of this research is to conduct a sentiment analysis of the vaccination campaign, and for this purpose, the reactions of Bangladeshi netizens on social media to the vaccination program were collected. The dataset was manually labelled into two categories: positive and negative. Then process the dataset using Natural Language Processing (NLP). The processed data is then classified using various machine learning algorithms using N-gram as a feature extraction method. The recall, precision, f1-score, and accuracy of various algorithms are all measured. The experiment results show that 61% of the reviews indicate the positive aspects of the vaccination program, while 39% are negative. For unigram, bigram, and trigram, the very best accuracy was achieved by Logistic Regression (LR) at 80.70%, 79.45%, and 78.65%. © 2022 IEEE.

Context Aware Automatic Subjective and Objective Question Generation using Fast Text to Text Transfer Learning

Online learning has gained a tremendous popularity in the last decade due to the facility to learn anytime, anything, anywhere from the ocean of web resources available. Especially the lockdown all over the world due to the Covid-19 pandemic has brought an enormous attention towards the online learning for value addition and skills development not only for the school/college students, but also to the working professionals. This massive growth in online learning has made the task of assessment very tedious and demands training, experience and resources. Automatic Question generation (AQG) techniques have been introduced to resolve this problem by deriving a question bank from the text documents. However, the performance of conventional AQG techniques is subject to the availability of large labelled training dataset. The requirement of deep linguistic knowledge for the generation of heuristic and hand-crafted rules to transform declarative sentence into interrogative sentence makes the problem further complicated. This paper presents a transfer learning-based text to text transformation model to generate the subjective and objective questions automatically from the text document. The proposed AQG model utilizes the Text-to-Text-Transfer-Transformer (T5) which reframes natural language processing tasks into a unified text-to-text-format and augments it with word sense disambiguation (WSD), ConceptNet and domain adaptation framework to improve the meaningfulness of the questions. Fast T5 library with beam-search decoding algorithm has been used here to reduce the model size and increase the speed of the model through quantization of the whole model by Open Neural Network Exchange (ONNX) framework. The keywords extraction in the proposed framework is performed using the Multipartite graphs to enhance the context awareness. The qualitative and quantitative performance of the proposed AQG model is evaluated through a comprehensive experimental analysis over the publicly available Squad dataset. © 2023, International Journal of Advanced Computer Science and Applications. All Rights Reserved.

How Severe is Your COVID-19? Predicting SARS-CoV-2 Infection with Graph Attention Capsule Networks

Recent studies in machine learning have demonstrated the effectiveness of applying graph neural networks (GNNs) to single-cell RNA sequencing (scRNA-seq) data to predict COVID-19 disease states. In this study, we propose a graph attention capsule network (GACapNet) which extracts and fuses Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) transcriptomic patterns to improve node classification performance on cells and genes. Significantly different from the existing GNN approaches, we innovatively incorporate a capsule layer with dynamic routing into our model architecture to combine and fuse gene features effectively and to allow those more prominent gene features present in the output. We evaluate our GACapNet model on two scRNA-seq datasets, and the experimental results show that our GACapNet model significantly outperforms state-of-the-art baseline models. Therefore, our study demonstrates the capability of advanced machine learning models to generate predictive features and evolutionary patterns of the SARS-CoV-2 pathogen, and the applicability of closing knowledge gaps in the pathogenesis and recovery of COVID-19. © 2022 IEEE.

Reexamining the Echo Chamber Effect of COVID-19 Vaccine Videos on YouTube

The COVID-19 pandemic has been going on for more than two years. Vaccination is believed to be one of the most efficient ways to achieve herd immunity and end pandemic. However, the contents about COVID-19 vaccines on social media have impacts on personal attitude towards vaccination. The present study aims to examine the current scenario and the echo chamber effect of COVID-19 vaccine videos on YouTube. A total of 1,646 videos with comments and replies were identified. An approach combining topic modeling, sentiment analysis, and social network analysis was employed to explore users' attitude towards COVID-19 vaccines and whether the echo chamber effect existed. The results indicate that, even if the misleading and anti-vaccination videos were removed by the platform, "anti-vaccination"contents still widely appear in the comments. Moreover, the community of "anti-vaccination"users was more homogeneous compared with that of "pro-vaccination"users. The findings of this study advanced theories of echo chamber effect and the network perspective to examine echo chambers. We propose that should be paid more attention ideology echo chamber, compared with exposure echo chamber. © 2022 IEEE.

A Deep Learning Approach for Sentiment Classification of COVID-19 Vaccination Tweets

Now-a-days, social media platforms enable people to continuously express their opinions and thoughts about different topics. Monitoring and analyzing the sentiments of people is essential for governments and business organizations to better understand people's feelings and thoughts. The Coronavirus disease 2019 (COVID-19) has been one of the most trending topics on social media over the last two years. Consequently, one of the preventative measures to control and prevent the spread of the virus was vaccination. A dataset was formed by collecting tweets from Twitter for over a month from November 13th to December 31st, 2021. After data cleaning, the tweets were assigned a positive, negative, or neutral label using a natural language processing (NLP) sentiment analysis tool. This study aims to analyze people's public opinion towards the vaccination process against COVID-19. To fulfil this goal, an ensemble model based on deep learning (LSTM-2BiGRU) is proposed that combines long short-term memory (LSTM) and bidirectional gated recurrent unit (BiGRU). The performance of the proposed model is compared to five traditional machine learning models, two deep learning models in addition to state-of-the-art models. By comparing the results of the models used in this study, the results reveal that the proposed model outperforms all the machine and deep learning models employed in this work with a 92.46% accuracy score. This study also shows that the number of tweets that involve neutral, positive, and negative sentiments is 517496 (37%) tweets, 484258 (34%) tweets, and 409570 (29%) tweets, respectively. The findings indicate that the number of people carrying neutral sentiments towards COVID-19 immunization through vaccines is the highest among others. © 2023, International Journal of Advanced Computer Science and Applications. All Rights Reserved.

Doc-Handler: Document Scanner, Manipulator, and Translator based on Image and Natural language processing

Physical documents may easily be converted into digital versions in the modern digital era by employing scanning software and the internet. The day when this activity needed printers and scanners is long gone. Nowadays, even our smartphones and cameras may be used to quickly convert paper documents into digital ones. This is especially useful in the wake of the COVID-19 pandemic, where the ability to share and access documents online is more important than ever. This study proposes an application for illiterate people to quickly translate scanned papers or photos into their native language and save them in a digital format. The Application makes use of image processing methods and has capabilities including PDF conversion, image colour adjustment, cropping, and Optical Character Recognition (OCR). A user-friendly application, developed using the Flutter Framework and programmed in Python and Dart, serves as the interface for the system. The proposed application is cross-platform and works with a variety of gadgets. This method intends to increase accessibility and productivity for illiterate people in the digital age by integrating image processing with language translation. © 2023 IEEE.

Sentiment of EmojiSets: How Emoji Sequences Improve Sentiment Cognition

The capability to infer emotional insights from emojis found in social media has projected emoji analysis into the spotlight of current emoji-based research. Previous studies mainly used text-surrounding emojis to estimate sentimentality scores. However, trying to conclude the same score based solely on emojis is challenging. In this paper this challenge was welcomed, and with it we created a new concept. This revolutionary scoring method, named the EmojiSets Sentiment Score Rank, proposes using sets of emojis taken from tweets along with information from previous studies [1] to find a sentiment score. This bottom-up scoring approach gives each emoji a sentiment score. It then calculates the context-level sentiment score of a tweet solely dependent on the emojis found within it. To the best of the authors' knowledge, no such approach has been researched in the Emojis Sentiment Analysis area. We tested our model against over 1.2 million tweets concerning Covid-19 and compared it to the VADER model [7] to validate our assumption. Our model corrected around 72% of the tweets that the other model scored as neutral. To succor these findings, 32 human annotators were given the task of annotating 8040 randomly chosen tweets. When calculating similarity using the Jaccard Index, their results were consistent with our approach in over 70% of cases © 2022 IEEE.

Analyzing the Evolution of Disinformation Content on Facebook - a Pilot Study

Disinformation spread on social media generates a truly massive amount of content on a daily basis, much of it not quite duplicated but repetitive and related. In this paper, we present an approach for clustering social media posts based on topic modeling in order to identify and formalize an underlying structure in all the noise. This would be of great benefit for tracking evolving trends, analyzing large-scale campaigns, and focusing efforts on debunking or community outreach. The steps we took in particular include harvesting through CrowdTangle huge collection of Facebook posts explicitly identified as containing disinformation by debunking experts, following those links back to the people, pages and groups where they were shared then collecting all posts shared on those channels over an extended period of time. This generated a very large textual dataset which was used in the topic modeling experiments attempting to identify the larger trends in the available data. Finally, the results were transformed and collected in a Knowledge Graph for further study and analysis. Our main goal is to investigate different trends and common patterns in disinformation campaigns, and whether there exist some correlations between some of them. For instance, for some of the most recent social media posts related to COVID-19 and political situation in Ukraine. © 2022 Copyright for this paper by its authors.