Synthetic Lies: Understanding AI-Generated Misinformation and Evaluating Algorithmic and Human Solutions

Large language models have abilities in creating high-volume human-like texts and can be used to generate persuasive misinformation. However, the risks remain under-explored. To address the gap, this work first examined characteristics of AI-generated misinformation (AI-misinfo) compared with human creations, and then evaluated the applicability of existing solutions. We compiled human-created COVID-19 misinformation and ed it into narrative prompts for a language model to output AI-misinfo. We found significant linguistic differences within human-AI pairs, and patterns of AI-misinfo in enhancing details, communicating uncertainties, drawing conclusions, and simulating personal tones. While existing models remained capable of classifying AI-misinfo, a significant performance drop compared to human-misinfo was observed. Results suggested that existing information assessment guidelines had questionable applicability, as AI-misinfo tended to meet criteria in evidence credibility, source transparency, and limitation acknowledgment. We discuss implications for practitioners, researchers, and journalists, as AI can create new challenges to the societal problem of misinformation. © 2023 Owner/Author.

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.

Applying Machine Learning and Model-Driven Approach for the Identification and Diagnosis of Covid-19

Ubiquitous environments are not fixed in time. Entities are constantly evolving;they are dynamic. Ubiquitous applications therefore have a strong need to adapt during their execution and react to the context changes, and developing ubiquitous applications is still complex. The use of the separation of needs and model-driven engineering present the promising solutions adopted in this approach to resolve this complexity. The authors thought that the best way to improve efficiency was to make these models intelligent. That's why they decided to propose an architecture combining machine learning with the domain of modeling. In this article, a novel tool is proposed for the design of ubiquitous applications, associated with a graphical modeling editor with a drag-drop palette, which will allow to instantiate in a graphical way in order to obtain platform independent model, which will be transformed into platform specific model using Acceleo language. The validity of the proposed framework has been demonstrated via a case study of COVID-19. © 2023 IGI Global. All rights reserved.

Visual analysis of mask sales data based on crawler

The outbreak of COVID-19 makes people feel distant from each other, and masks have become one of the indispensable articles in People's Daily life. At present, there are many brands of masks with various types and uneven quality. In order to understand the current market of masks and the sales of different brands, users can choose masks with perfect quality. This paper uses Python web crawler technology, based on the input of the word "mask", crawl JD website sales data, through data visualization technology drawing histogram, pie chart, the word cloud, etc., for goods compared with the relationship between price, average price of all brands, brands, average distribution of analysis and evaluation of user information, In this way, the sales situation, price distribution and quality evaluation of each store of the product can be visually displayed. At the same time, it also provides some reference for other users who need to buy the product. © The Authors. Published under a Creative Commons Attribution CC-BY 3.0 License.

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.

Blending Academic Writing: The Use of Technology to teach amid Covid-19 Pandemic

Due to the Covid-19 pandemic, teachers have to hesitantly switch to the virtual world of teaching, known as enriched virtual blended learning in which technology, surely plays a significant role in material delivery and discussion. Studies related to blended learning in English language classes, especially in academic writing classes, have been done in many countries, but they are still lacking in the Indonesian setting. This study aimed to investigate the effectiveness of technology utilization in enriched blended learning to teach academic writing and the students' perspectives towards the use of technology in blended learning. This study involving forty-five students in a university in Jakarta applied both quantitative and qualitative methods. A pre-test and a post-test were assigned before and after the blended learning period to study the effectiveness of blended learning. In addition, surveys and interviews were conducted to investigate the students' perspectives. The results indicated that the use of technology in blended learning effectively helped students develop their academic writing skills despite being a new experience. Furthermore, despite some limitations, this strategy of using technology in teaching-learning was accepted with optimism as they started to get accustomed to it. Hence, the use of technology is promising for the future of learning. © 2023 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

A Quantitative Analysis of Barriers to International Telemedicine Education: The Case of the Philippines

A multitude of studies have investigated the opportunities and limitations of telemedicine pre- and post-COVID-19 pandemic. However, most of the research has focused on telemedicine's constraints in the context of international, regional, and developed nations, with few studies examining the specific challenges that may affect telemedicine's progress in developing countries where the pandemic may have exacerbated existing technological and geographical difficulties. This study takes the Philippines as a case study due to its archipelagic location, use of English as an official language, and other factors that influence its adaptability to international telemedicine. We assessed the barriers and challenges to the advancement of telemedicine from four viewpoints: policy, organization, individual, and collaboration between organizations. Therefore, the significance of this study is twofold: (a) to concentrate on international telemedicine education by contrasting domestic and international practices, and (b) to newly reveal connections between each component, as prior research highlighted barriers and difficulties but did not clarify relationships among different elements. We surveyed and interviewed 38 physicians, technicians, coordinators, and staff involved in telemedicine education in the Philippines. The study found that (1) public support yields favourable results, (2) a strong correlation exists between domestic and international telemedicine, (3) communication and technical obstacles are interconnected, (4) unity and cooperation in intra-hospital collaboration are critical, and (5) comprehending the "significance of work" has a positive impact. This study underscores the intersectionality of several barriers to telemedicine development. It also recommends providing greater support for telemedicine education in developing nations and promoting collaboration between developing and developed nations. Copyright © 2023 by SCITEPRESS - Science and Technology Publications, Lda. Under CC license (CC BY-NC-ND 4.0)

Design Principles and a Software Reference Architecture for Big Data Question Answering Systems

Companies continuously produce several documents containing valuable information for users. However, querying these documents is challenging, mainly because of the heterogeneity and volume of documents available. In this work, we investigate the challenge of developing a Big Data Question Answering system, i.e., a system that provides a unified, reliable, and accurate way to query documents through naturally asked questions. We define a set of design principles and introduce BigQA, the first software reference architecture to meet these design principles. The architecture consists of high-level layers and is independent of programming language, technology, querying and answering algorithms. BigQA was validated through a pharmaceutical case study managing over 18k documents from Wikipedia articles and FAQ about Coronavirus. The results demonstrated the applicability of BigQA to real-world applications. In addition, we conducted 27 experiments on three open-domain datasets and compared the recall results of the well-established BM25, TF-IDF, and Dense Passage Retriever algorithms to find the most appropriate generic querying algorithm. According to the experiments, BM25 provided the highest overall performance. Copyright © 2023 by SCITEPRESS - Science and Technology Publications, Lda. Under CC license (CC BY-NC-ND 4.0)

An approach for Face Detection and Face Recognition using OpenCV and Face Recognition Libraries in Python

With the extraordinary growth in images and video data sets, there is a mind-boggling want for programmed understanding and evaluation of data with the assistance of smart frameworks, since physically it is a long way off. Individuals, unlike robots, have a limited capacity to distinguish unexpected expressions. As a result, the programmed face proximity frame- work is important in face identification, appearance recognition, head-present evaluation, human-PC cooperation, and other applications. Software that uses facial recognition for face detection and identification is regarded as biometric. This study converts the mathematical aspects of a person's face into a face print, which is then stored in a database to verify an individual's identification. A deep learning system compares a digital image or an image taken quickly to a previously stored image(which is saved in the database). The face has a significant function in interpersonal communication for identifying oneself. Face recognition technology determines the size and placement of a human face in a digital picture. Facial recognition software has a wide range of uses in the consumer market and in the security and surveillance sectors. The COVID pandemic has brought facial recognition into greater focus lately than ever before. Face detection and recognition play a vital part in security systems that people need to interact with without making physical contact. The pattern of online exam proctoring is employing face detection and recognition. Facial recognition is used in the airline sector to enable rapid, accurate identification and verification at every stage of the passenger trip. In this research, we focused on image quality because it is the major drawback in existing algorithms and used OPEN CV, Face Recognition, and designed algorithms using libraries in python. This study discusses a method for facial recognition along with its implementation and applications. © 2023 IEEE.

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.

Stacked Bi-directional Long Short Term Memory model for Multi-Class Arabic Sentiment Analysis on Covid-19

The world has been affected by the Covid-19 epidemic during the last three years. During that period, most people tended to use social networks, where by searching for topics related to Covid-19, information could be provided to manage decisions by organizations or governments about public health. With the importance of the Arabic language, despite the lack of research targeting it, using Arabic language as a source of data and analyzing it due to the large number of users on social networks gives an impetus to understand people's feelings about the Covid-19 pandemic. One of the challenges facing sentiment analysis in Arabic is the use of dialects. The most common and existing methods used have been quite ineffective as they are oblivious to contextual information and cannot handle long-distance word dependencies. The Iraqi Arabic dialect is one of the Arabic dialects that still suffers from a lack of research in sentiment analysis. In this study, the official page of the Iraqi Ministry of Health on Facebook was used to collect and analysis comments. Word2vec model is incorporated to extract words semantic characteristics. To capture contextual features, Stacked Bi-directional Long Short Term Memory model (Stacked Bi-LSTM) utilizes sequential word vectors derived from the Continuous Bag of Words model. When compared to most common and existing approaches, the proposed method performed well. © 2022 IEEE.

Artificial Intelligence-based Speech Signal for COVID-19 Diagnostics

The speech signal has numerous features that represent the characteristics of a specific language and recognize emotions. It also contains information that can be used to identify the mental, psychological, and physical states of the speaker. Recently, the acoustic analysis of speech signals offers a practical, automated, and scalable method for medical diagnosis and monitoring symptoms of many diseases. In this paper, we explore the deep acoustic features from confirmed positive and negative cases of COVID-19 and compare the performance of the acoustic features and COVID-19 symptoms in terms of their ability to diagnose COVID-19. The proposed methodology consists of the pre-trained Visual Geometry Group (VGG-16) model based on Mel spectrogram images to extract deep audio features. In addition to the K-means algorithm that determines effective features, followed by a Genetic Algorithm-Support Vector Machine (GA-SVM) classifier to classify cases. The experimental findings indicate the proposed methodology's capability to classify COVID-19 and NOT COVID-19 from acoustic features compared to COVID-19 symptoms, achieving an accuracy of 97%. The experimental results show that the proposed method remarkably improves the accuracy of COVID-19 detection over the handcrafted features used in previous studies. © 2022 ACM.