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.

Forecasting COVID-19 Vaccination Rates using Social Media Data

The COVID-19 pandemic has had a profound impact on the global community, and vaccination has been recognized as a crucial intervention. To gain insight into public perceptions of COVID-19 vaccines, survey studies and the analysis of social media platforms have been conducted. However, existing methods lack consideration of individual vaccination intentions or status and the relationship between public perceptions and actual vaccine uptake. To address these limitations, this study proposes a text classification approach to identify tweets indicating a user's intent or status on vaccination. A comparative analysis between the proportions of tweets from different categories and real-world vaccination data reveals notable alignment, suggesting that tweets may serve as a precursor to actual vaccination status. Further, regression analysis and time series forecasting were performed to explore the potential of tweet data, demonstrating the significance of incorporating tweet data in predicting future vaccination status. Finally, clustering was applied to the tweet sets with positive and negative labels to gain insights into underlying focuses of each stance. © 2023 ACM.

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.

COVID-19 vaccine stance classification from tweets

Since the discovery and betterment of vaccines for human diseases, Anti-Vaccine rhetoric and resistance have been prevalent in social circles. These sentiments adversely affect the effectiveness of preventing the contraction of deadly contagious diseases, such as COVID-19. With the advent of social media platforms, the expression of anti-vaccine stances has a far greater reach in society. In this paper, we tackle the task of COVID-19 vaccine stance detection to gauge people's receptiveness towards vaccines and subsequently understand the effectiveness of the vaccination drives. © 2021 Copyright for this paper by its authors.

A Machine Learning Approach for COVID-19 Tweet Classification

Vaccine-related information is awash on social media platforms like Twitter and Facebook. One party supports vaccination, while the other opposes vaccination and promotes misconceptions and misleading information about the risks of vaccination. The analysis of social media posts can give significant information into public opinion on vaccines, which can help government authorities in decision-making.This paper describes the dataset used in the shared task, and compares the performance of different classification that are provax, antivax and last neutral for identifying effective tweets related to Covid vaccines.We experimented with a classification-based approach. Our experiment shows that SVM classification performs well in order to effiective post.We're going to do this because vaccination is an important step for Covid19 so people can easily fix the news about the vaccine and grab their own slot and symptom detection is also playing a important part to arrest the spread of disease. © 2022 Copyright for this paper by its authors.

AN INVESTIGATION INTO THE UTILISATION OF SOCIAL MEDIA TO FOSTER TEAM COLLABORATION IN A HIGHER EDUCATION INSTITUTION

The COVID-19 pandemic introduced new challenges with subsequent opportunities to teach innovative ways of team collaboration. One example is the utilisation of social media to foster online team collaboration. This study investigates the use of Discord by students, a social media platform originally developed for online gamers, to collaborate virtually to complete project team tasks. The research question – what role a social media tool, namely Discord, plays in fostering team collaboration – was investigated using a qualitative, interpretative approach. Topic modeling identified ten themes, with the most vital theme indicating that students initially used Discord due to the academic requirement but later extensively used the platform because of its convenience and usefulness. Most students continued to use Discord even after completing their studies. While the main reason for adopting the tool was convenience due to peers using the platform, it became a logical and practical platform to communicate with friends, work on completing tasks together, and as a result, create a strong sense of belonging. © (2022) by Association for Information Systems (AIS) All rights reserved.

Hunter Prey Optimization with Hybrid Deep Learning for Fake News Detection on Arabic Corpus

Nowadays, the usage of social media platforms is rapidly increasing, and rumours or false information are also rising, especially among Arab nations. This false information is harmful to society and individuals. Blocking and detecting the spread of fake news in Arabic becomes critical. Several artificial intelligence (AI) methods, including contemporary transformer techniques, BERT, were used to detect fake news. Thus, fake news in Arabic is identified by utilizing AI approaches. This article develops a new hunter-prey optimization with hybrid deep learning-based fake news detection (HPOHDL-FND) model on the Arabic corpus. The HPOHDL-FND technique undergoes extensive data pre-processing steps to transform the input data into a useful format. Besides, the HPOHDL-FND technique utilizes long-term memory with a recurrent neural network (LSTM-RNN) model for fake news detection and classification. Finally, hunter prey optimization (HPO) algorithm is exploited for optimal modification of the hyperparameters related to the LSTM-RNN model. The performance validation of the HPOHDL-FND technique is tested using two Arabic datasets. The outcomes exemplified better performance over the other existing techniques with maximum accuracy of 96.57% and 93.53% on Covid19Fakes and satirical datasets, respectively. © 2023 Tech Science Press. All rights reserved.

Deploying Artificial Intelligence to Combat Covid-19 Misinformation on Social Media: Technological and Ethical Considerations

This paper reports on AI research into online misinformation pertaining to the COVID-19 pandemic within the Canadian context. This is part of our longer-term goal, i.e., development of a machine-learning tool to assist social media platforms, online service providers and government agencies in identifying and responding to misinformation on social media. We report on predictive accuracies accomplished by applying a combination of technologies, including a custom-designed web-crawler, The Dark Crawler, the Posit toolkit, and four different machine-learning models based on Naïve Bayes, Support Vector Machines, LibLinear and LibShortText. Overall, we found that Posit and LibShortText models showed higher levels of correlation to the pre-determined (manual and machine-driven) data classifications than the other machine-learning algorithms tested. We further argue that the harms associated with COVID-19 misinformation - e.g., the social and economic damage, and the deaths and severe illnesses - outweigh the right to personal privacy and freedom of speech considerations. © 2023 IEEE Computer Society. All rights reserved.

Social Media and Healthcare Technology in 2022: Macro analytics, text and data mining, social media platforms and COVID-19

Social media has changed the way individuals and institutions approach healthcare and health information and offers opportunities to understand health-related interactions at all levels, from the micro to the macro. The Social Media and Healthcare Technology mini-track presents research papers that address a diverse array of social media and associated technology within healthcare and healthcare research;including macro analytics, text and data mining and the role of social media platforms and influencers in health care and health-related decision making. © 2022 IEEE Computer Society. All rights reserved.

Is Twitter Enough? Investigating Situational Awareness in Social and Print Media during the Second COVID-19 Wave in India

The COVID-19 pandemic required efficient allocation of public resources and transforming existing ways of societal functions. To manage any crisis, governments and public health researchers ex-ploit the information available to them in order to make informed decisions, also defined as situational awareness. Gathering situational awareness using so-cial media, has been functional to manage epidemics. Previous research focused on using discussions during periods of epidemic crises on social media platforms like Twitter, Reddit, or Facebook and developing NLP techniques to filter out important/relevant discussions from a huge corpus of messages and posts. Social media usage varies with internet penetration and other socio-economic factors, which might induce disparity in an-alyzing discussions across different geographies. How-ever, print media is a ubiquitous information source, irrespective of geography. Further, topics discussed in news articles are already 'newsworthy', while on social media 'newsworthiness' is a product of techno-social processes. Developing this fundamental difference, we study Twitter data during the second wave in India focused on six high-population cities with varied macro-economic factors. Through a mixture of qualitative and quantitative methods, we further analyze two Indian newspapers during the same period and compare topics from both Twitter and the newspapers to evaluate sit-uational awareness around the second phase of COVID on each of these platforms. We conclude that factors like internet penetration and GDP in a specific city influence the discourse surrounding situational updates on social media. Thus, augmenting information from newspapers to information extracted from social media would provide a more comprehensive perspective in resource-deficit cities © 2022 IEEE.

Analysing Shifts in Perceptions of Indians during COVID-19 Pandemic by Mining Tweets

Current pandemic situation has a significant impact affecting human life not only socially and economically, but emotionally and psychologically as well. This impact can be easily observed on social media platforms. Along with the knowledge exchange related to Covid-19 pandemic on social media, there is an emotional trauma wave that can be felt by carefully analyzing the activities of this social media. Keeping this view in thought, we analyze around 12000 tweets of Indian people to find out whether there is a trend shift of thinking pattern and mindset of Indian people as the pandemic progresses. The study is bifurcated into stages to clearly see the paradigm shift. We use tweets since twitter is a rich medium that can be leveraged to its optimum to have a good amount of understanding of the sentiments of the people. Analyzing the twitter dataset, we derive results and find out whether the amount of negative tweets v/s positive (or motivational) tweets have increased or not as the pandemic progresses. The study is supported by graphical visualizations of the polarity of the tweets month wise. Further, Wordmap approach is used to perform qualitative mining analysis in addition to the sentiment score based calculation. This work helps us to understand how the public opinions are changing with the changes in the spread dynamics of the virus. This kind of mood mining helps in identifying the Covid-19 situation from the psychological perspective that whether there is a sense of fear among people or they are quite optimistic of the situation. It can help in a great extend to the strategic and decision making bodies to plan out for future decisions. Further, such kind of studies can be used as reference to provide insights about mental health of people for any future incident or event of such nature. © 2023 IEEE.

D-insta: A Decentralized Image Sharing Platform

Due to the covid-19 pandemic, people have moved toward digitization and using digital technologies in their daily life. For instance, photographers and artists use social media platforms or stock photo websites to showcase their art to people to get recognition and credit. Since social media platforms attract people more than stock photo websites, we consider incorporating the stock photo website features into the social media platforms. Currently, such platforms are running in a centralized fashion where their proprietary algorithms mask most of the content to which some users and advertisement posts are given more priority. Due to the centralization, such hidden algorithms create trust issues among the users along with other issues such as single point of failure, identity theft, etc. This causes genuine artists and photographers to lose their interest and motivation. Providing due credit to the authors and deserved recognition are significant concerns for photographers who share images on stock photo websites or social media platforms. In this paper, we propose a decentralized image-sharing platform/application utilizing blockchain and a distributed file storage system to address all these issues. The proposed platform leverages Ethereum-based smart contracts to maintain trust as deployed smart contracts are immutable, and the logic written in them is publicly available. We leverage a distributed file storage system to solve the blockchain scalability issue in terms of storage. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Unmuting leadership: the impact of Zelensky's social media strategy at the inset of the Ukrainian War

Since the start of the war in Ukraine in February 2022, President Zelensky has used his social networks to request international support. This research analyzes the audiovisual discourse of the hegemonic networks during the first 40 days of the humanitarian war crisis in the context of risk communication. The contribution of this research relates to the unveiling of a new era where social media platforms are no longer the underdog of traditional media. This paper analyzes the visual content of President Zelensky's most followed social media profile, Instagram. This paper builds on previous work examining the political leaders of the most affected European countries during the first days of the COVID-19 pandemic in 2020 and how they enhanced the use of their social media profiles in order to communicate about the crisis. In contrast to the use of social media during the COVID-19 pandemic, the audiovisual narrative created by the president of Ukraine optimized the social network's resources and achieved an impactful and authentic approach to leadership during the first 40 days of crisis. © 2023 Informa UK Limited, trading as Taylor & Francis Group.

Virality and the Virus: COVID-19 Cures on Twitter in India

Social media platforms often become environments of information ambiguity during crisis events. We studied the discussion around four”cures” for COVID-19 in India, where the highest number of cases were recorded between 2020 and May 2021, focusing on the role played by high network accounts on social media such as those of journalists, politicians, and celebrities. We find that information scarcity and anxiety among citizens enabled non-experts, particularly the aforementioned social media influencers. We find that this undermined institutional sources of information and led to massive spikes in online interest around unproven cures during the peak of the crisis. © 2022 IEEE Computer Society. All rights reserved.

COVID-19 Fake News Detector

COVID-19 pandemic has been impacting people's everyday life for more than two years. With the fast spreading of online communication and social media platforms, the number of fake news related to COVID-19 is in a rapid growth and propagates misleading information to the public. To tackle this challenge and stop the spreading of fake news, this project proposes to build an online software detector specifically for COVID-19 news to classify whether the news is trustworthy. Specifically, as it is difficult to train a generic model for all domains, a base model is developed and fine-tuned to adapt the specific domain context. In addition, a data collection mechanism is developed to get latest COVID-19 news data and to keep the model fresh. We then conducted performance comparisons among different models using traditional machine learning techniques, ensemble machine learning, and the state-of-the-art deep learning mechanism. The most effective model is deployed to our online website for COVID-19 related fake news detection. © 2023 IEEE.

From Small to Big: Smartwatch Use in Mitigating COVID-19 - Understanding User Experience from Social Media Content Analysis

Smartwatches offer both functions and convenience that can have great potentials for technological interventions. Despite widespread discussion of technological interventions for COVID-19, smartwatch use has received little attention in the literature. This research aims to fill the literature gap by providing a broad understanding of smartwatch use for COVID-19 mitigation. We investigate smartwatch use through content analysis of the data collected from two social media platforms. The method allows us to draw on user experience beyond technological features and functions. In addition to functions, we also identified the concerns of using smartwatches for mitigating COVID-19. Furthermore, we uncovered both similarities and differences between the different social media platforms in terms of functions and concerns of smartwatch use. Our findings have implications for various stakeholders of the smartwatch technology and for mitigating the impact of the pandemic. © 2022 IEEE Computer Society. All rights reserved.

Portuguese Twitter Dataset on COVID-19

Over the last two years, the COVID-19 pandemic has affected hundreds of millions of people around the world. As in many crises, people turn to social media platforms, like Twitter, to communicate and share information. Twitter datasets have been used over the years in many research studies to extract valuable information. Therefore, several large COVID-19 Twitter datasets have been released over the last two years. However, none of these datasets contains only Portuguese Tweets, despite the Portuguese Language being reported as one of the top five languages used on Twitter. In this paper, we present the first large-scale Portuguese COVID-19 Twitter dataset. The dataset contains over 19 million Tweets spanning 2020 and 2021, allowing the entire pandemic to be analyzed. We also conducted a sentiment analysis on the dataset and correlated the various spikes in Tweet count and sentiment scores to various news articles and government announcements in Portugal and Brazil. The dataset is available at: https://github.com/bioinformatics-ua/Portuguese-Covid19-Dataset © 2022 IEEE.

Boundary Crossing through Text and Image on Instagram in an Online Community of Practice

During COVID-19 lockdown many social media challenges captured the attention of users all around the world, and many online communities of practice used social media platforms for their daily interactions. On Instagram these communities gather around common interests through the platform's sociotechnical affordances. We examined the role that these features play in boundary maintenance processes and boundary crossing practices, analyzing posts from four online communities of practice (CoPs), who were bounded by their hashtags and shared an art recreation challenge that was popular on Instagram at the start of COVID-19 lockdown. We found that while some practices are shared across CoPs, boundary maintenance processes sometimes are not, and the boundaries of some of these CoPs are more permeable than others. Cultural differences, language, and script were critical for boundary maintenance regardless of the platform's visual affordances that served the boundary crossing practices. © 2023 IEEE Computer Society. All rights reserved.

Sequential Topic Modelling: A Case Study on One Health Conversation on Twitter

In the past few years, HIV, SARS, cryptococcal meningoencephalitis, and COVID-19 have been worsening. The world is exterminated by pandemic COVID-19, causing tremendous death tolls, economic chaos, and social disruptions. Since the COVID-19 pandemic, the wildlife trade has been seriously re-evaluated. Twitter, as a social media platform, can be a challenging place to collect data in the form of tweets that are currently attracting the attention of many people. Nevertheless, human beings find it relatively difficult to extract latent information from a set of texts to generate particular topics. The process of evaluating the topic model started with understanding its importance. As a next step, we reviewed existing methods for topic coherence, along with the available measures of topic coherence. In order to establish a baseline coherence score, we used Gensim to implement a default Latent Dirichlet Allocation (LDA) model and discuss ways to optimize the LDA hyperparameters. © 2022 IEEE.

Classification of Covid-19 Vaccines tweets using Naïve Bayes Classification

Recently COVID-19 has become the most discussed topic in different social media platforms like Twitter, Facebook, Instagram etc. As time moves on, lot of messages and videos are posted in social media. As expected, most of the public followed these messages and becomes panic because of lack of information, misinformation about COVID-19 and its impact. This research study proposes a Twitter sentiment analysisbased on the most popular vaccines Covaxin, Covishield, and Pfizer. Most of the people expressed their feelings about vaccines in the twitter. Twitter API authentication is used here to extract the tweets. These extracted tweets are difficult to analyze, hence pre-processing has been done i.e., unstructured data is converted into structured format. After completion of preprocessing, the data is further classified by using Naïve Bayes algorithm. This algorithm performs data classification and divides it into three major classes as positive, negative, and neutral. The result shows that the covaxin yields 48.36% positive, 35.6% negative, and 16.04% neutral, Covishield yields 44.25% positive, 39.67% negative, and 16.08% neutral, Pfizer yields 42.95% positive, 39.45% negative, and 17.6% neutral sentiment. © 2022 IEEE.