Covid-19 and E-Learning: An Exploratory Analysis of Research Topics and Interests in E-Learning During the Pandemic

E-learning has gained further importance and the amount of e-learning research and applications has increased exponentially during the COVID-19 pandemic. Therefore, it is critical to examine trends and interests in e-learning research and applications during the pandemic period. This paper aims to identify trends and research interests in e-learning articles related to COVID-19 pandemic. Consistent with this aim, a semantic content analysis was conducted on 3562 peer-reviewed journal articles published since the beginning of the COVID-19 pandemic, using the N-gram model and Latent Dirichlet Allocation (LDA) topic modeling approach. Findings of the study revealed the high-frequency bigrams such as "online learn ", "online education ", "online teach " and "distance learn ", as well as trigrams such as "higher education institution ", "emergency remote teach ", "education online learn " and "online teach learn ". Moreover, the LDA topic modeling analysis revealed 42 topics. The topics of "Learning Needs ", "Higher Education " and "Social Impact " respectively were the most focused topics. These topics also revealed concepts, dimensions, methods, tools, technologies, applications, measurement and evaluation models, which are the focal points of e-learning field during the pandemic. The findings of the study are expected to provide insights to researchers and future studies.

Analysis of Research Paper Titles Containing Covid-19 Keyword Using Various Visualization Techniques

It’s been around two years from the outbreak of the coronavirus, thus labeled as Covid-19, and there has been an explosion of literature being published by research scholars related to work done on Covid-19. Covid-19 as a keyword has been mentioned in the titles of most of these papers. It was thought to analyse the number of papers and the titles of papers which include Covid-19 in the title of the research papers. The various combinations of other words like, prefixes, suffixes, N-gram combinations with the keyword Covid- 19 in the titles of these papers were also analysed. The research publication repositories analysed were: IEEE Explore, ACM Digital Library, Semantic Scholar, Google Scholar, Cornel University etc. The domains of research publication title analysis were restricted to computer science/computer engineering related papers. As the term labeling the corona virus outbreak as Covid-19 was labeled in 2020, the timeline of title analysis was restricted from 2019 till December 2021. The term Covid-19 is also one of the most searched terms in most of these research repositories as is evident from the search suggestions offered by them. Considering the usefulness of Bag of Words and N Gram algorithm in analytics and data visualization, a methodology is proposed and implemented based on bag of words algorithm to do prefix and suffix words analysis. This methodology is working correctly to state different prefix and suffix words used by various researchers to demonstrate significance of their titles. Methodology based on N Gram analysis is found effective to find topic on which most of the researchers have done work. Word Clouds are generated to demonstrate different buzz words used by researchers in their respective paper titles. These are useful for providing visualization of the data if it is in big size. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

A comparative study of syllables and character level N-grams for Dravidian multi-script and code-mixed offensive language identification

Curfews and lockdowns around the world in the Covid-19 era have increased the usage of the internet drastically and accordingly the amount of data shared on social media. In addition to using social media for sharing useful information, some miscreants are using the power of social media to spread hate speech and offensive content. Filtering the offensive language content manually is a laborious task due to the huge volume of data. Further, rapid developments in hardware and software technology have provided opportunities for users to post their comments not only in English but also in their native language scripts. However, based on the ease of Roman script usage, social media users specifically in multilingual countries like India, prefer to comment in code-mixed and multi-script texts. The typical systems that are employed to process and analyze monolingual texts are usually not appropriate for these kinds of texts. Further, as these texts do not adhere to the rules and regulations of any language to frame the words and sentences, the complexity of analyzing such texts increases. The novelty of the present study is to address the Offensive Language Identification (OLI) task in code-mixed and multi-script texts, this paper proposes to use relevant syllable and character n-grams features to train Machine Learning (ML) classifiers. The performance of the proposed models is evaluated on three Dravidian language pairs, namely: Malayalam-English, Tamil-English, and Kannada-English. The performances of ML classifiers prove the effectiveness of syllable and character n-grams features for code-mixed and multi-script texts analysis. [ FROM AUTHOR] Copyright of Journal of Intelligent & Fuzzy Systems is the property of IOS Press 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 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 . (Copyright applies to all s.)

A study and identification of COVID-19 viruses using N-grams with Naïve Bayes, K-Nearest Neighbors, Artificial Neural Networks, Decision tree and Support Vector Machine

Coronavirus disease 2019 or COVID-19 is a global health crisis caused by a virus officially named as severe acute respiratory syndrome coronavirus 2 and well known with the acronym (SARS-CoV-2). This very contagious illness has severely impacted people and business all over the world and scientists are trying so far to discover all useful information about it, including its potential origin(s) and inter-host(s). This study is a part of this scientific inquiry and it aims to identify precisely the origin(s) of a large set of genomes of SARS-COV-2 collected from different geographic locations in all over the world. This research is performed through the combination of five powerful techniques of machine learning (Naïve Bayes, K-Nearest Neighbors, Artificial Neural Networks, Decision tree and Support Vector Machine) and a widely known tool of language modeling (N-grams). The experimental results have shown that the majority of the aforementioned techniques gave the same global results concerning the origin(s) and inter-host(s) of SARS-COV-2. These results demonstrated that this virus has one zoonotic source which is Pangolin. © 2022 IEEE.

CoSaD-Code-Mixed Sentiments Analysis for Dravidian Languages

Analyzing sentiments or opinions in code-mixed languages is gaining importance due to increase in the use of social media and online platforms especially during the Covid-19 pandemic. In a multilingual society like India, code-mixing and script mixing is quite common as people especially the younger generation are quite familiar in using more than one language. In view of this, the current paper describes the models submitted by our team MUCIC for the shared task in’Sentiments Analysis (SA) for Dravidian Languages in Code-Mixed Text’. The objective of this shared task is to develop and evaluate models for code-mixed datasets in three Dravidian languages, namely: Kannada, Malayalam, and Tamil mixed with English language resulting in Kannada-English (Ka-En), Malayalam-English (Ma-En), and Tamil-English (Ta-En) language pairs. N-grams of char, char sequences, and syllables features are transformed into feature vectors and are used to train three Machine Learning (ML) classifiers with majority voting. The predictions on the Test set obtained average weighted F1-scores of 0.628, 0.726, and 0.619 securing 2nd, 4th, and 5th ranks for Ka-En, Ma-En, and Ta-En language pairs respectively. © 2021 Copyright for this paper by its authors.

Exploring Public Trust on State Initiatives During the COVID-19 Pandemic

As most governments in the world currently face the pandemic, various policies and initiatives have been put in place in order to help control the spread of the COVID-19 outbreak. While these initiatives and interventions are taking place, a pandemic still creates a reality of risk and uncertainty. In these kinds of situations, public trust is greatly important to properly mitigate health and societal impacts of the pandemic. Social media platforms could be utilized as sources of information to gain insight on public sentiment, especially with the rise of social media utilization during the quarantine [13]. Given this, the study attempts to analyze social media sentiments particularly found in Twitter in order to not only look into the polarity of public sentiment on the government, its processes, and its policies, but particularly, to detect trust between the governed and the ones governing. Furthermore, it seeks to examine and analyze the trust narratives present in the Philippines currently. In this study, a supervised machine learning model was created using Linear SVC, utilizing TF-IDF and n-grams for feature extraction and selection in order to detect the respective trust category of a given sentiment and predict the trust category of new data points. While the results are overall negative, examining the trust categories individually demonstrates different narratives that dictate, affect, and express citizen trust towards different aspects of the government. The behavioral trust group provided narratives on certain political figures involved in a string of anomalies for the negative category, while the positive category lauded the VP for her continued service amidst the pandemic. On the other hand, narratives in the institutional trust group revolved around national and local institutions, where talks about national institutions being more prominent in the negative category, while local institutions, such as local government units, are found in the positive category. Lastly, narratives on the operational trust group focused on certain pandemic policies (lockdowns, mass testing, contact tracing) for the negative side, while vaccines and vaccinations were the focus for the positive side. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Development of a Text Classification Model to Detect Disinformation About COVID-19 in Social Media: Understanding the Features and Narratives of Disinformation in the Philippines

As of January 02, 2022, the Philippines is combating another surge in COVID-19 cases. With vaccinations still ongoing, the country remains vigilant and the government continues to promote compliance to minimum health standards as preventive measures to minimize the spread. Disinformation remains a challenge especially if compliance to minimum health standards and adoption of health interventions are necessary to curb the spread of COVID-19. Incorrect and unverified information about the virus increased as well which continues to run rampant in social media and with minimal models to detect disinformation in a Philippine context. The study aimed to understand the features of disinformation of COVID-19 in a Philippine context with the goal of creating a text classification model to detect disinformation of COVID-19 in social media to promote vaccine usage in the country. The usage of social network analysis was performed to understand the narratives present regarding COVID-19 disinformation. Words related to vaccines, government corruption, and government mismanagement were prevalent under the disinformation categories of “False” and “Mostly False” while words related to health information such as cases or vaccine counts were prevalent under the “Mostly True” and “True” category. Linear SVM text classification model performed the best through accuracy, precision, and recall in detecting disinformation by using TF-IDF as a feature compared to using both TF-IDF and n-grams. Disinformation narratives revolved around the idea of COVID-19 cases/vaccines, government mismanagement, and regulations. Results showed that disinformation caused distrust of the government’s management over the pandemic. Moreover, the spread of disinformation was contained to the user itself and spread to at least one other user. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Summary Generation Using Natural Language Processing Techniques and Cosine Similarity

The COVID-19 pandemic has led to an unprecedented challenge to public health. It resulted in global efforts to understand, record, and alleviate the disease. This research serves the purpose of generating a relevant summary related to Coronavirus. The research uses the COVID-19 Open Research Dataset (CORD-19) provided by Allen Institute for AI. The dataset contains 236,336 academic full-text articles as of July 19, 2021. This paper introduces a web-based system to handle user questions over the Coronavirus full-text scholarly articles. The system periodically runs backend services to process such large amount article with basic Natural Language Processing (NLP) techniques that include tokenization, N-Grams extraction, and part-of-speech (PoS) tagging. It automatically identifies the keywords from the question and uses cosine similarity to summarize the associated content and present to the user. This research will possibly benefit researchers, health workers as well as other individuals. Moreover, the same service can be used to train with the datasets of different domains (e.g., education) to generate a relevant summary for other user groups (e.g., students). © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Deep Ensemble Approach for COVID-19 Fake News Detection from Social Media

Social media networks such as Facebook and Twitter are overwhelmed with COVID-19-related posts during the outbreak. People have also posted several fake news among the massive COVID-19-related social media posts. Fake news has the potential to create public fear, weaken government credibility, and pose a serious threat to social order. This paper provides a deep ensemble-based method for detecting COVID-19 fake news. An ensemble classifier is made up of three different classifiers: Support Vector Machine, Dense Neural Network, and Convolutional Neural Network. The extensive experiments with the proposed ensemble model and eight different conventional machine learning classifiers are carried out using the character and word n-gram TF-IDF features. The results of the experiments show that character n-gram features outperform word n-gram features. The proposed deep ensemble classifier performed better, with a weighted Fl -score of 0.97 in contrast to numerous conventional machine learning classifiers and deep learning classifiers. © 2021 IEEE

Sentiment Analysis in Indian Sub-continent during COVID-19 Second Wave using Twitter Data

In Indian sub-continent COVID-19 second wave started in early March 2021 and its effect was more lethal than the first wave, the confirmed cases and the death rate was higher than in the first wave. Unlike the national lockdown in 2020, this year different states have started imposing lockdown like restrictions spanning April-June 2021. This paper investigates the sentiments of the people using twitter messages during early period of the second wave. Two-weeks data is manually annotated and several machine learning models were built. The best performing models were used to predict sentiments for the next 2-3 weeks and analysis is presented. Predictions of public, commercial libraries were also analysed in the same context. © 2021 IEEE.

Named Entity Recognition and Relation Extraction for COVID-19: Explainable Active Learning with Word2vec Embeddings and Transformer-Based BERT Models

Deep learning for natural language processing acquires dense vector representations for n-grams from large-scale unstructured corpora. Converting static embeddings of n-grams into a dataset of interlinked concepts with explicit contextual semantic dependencies provides the foundation to acquire reusable knowledge. However, the validation of this knowledge requires cross-checking with ground-truths that may be unavailable in an actionable or computable form. This paper presents a novel approach from the new field of explainable active learning that combines methods for learning static embeddings (word2vec models) with methods for learning dynamic contextual embeddings (transformer-based BERT models). We created a dataset for named entity recognition (NER) and relation extraction (REX) for the Coronavirus Disease 2019 (COVID-19). The COVID-19 dataset has 2,212 associations captured by 11 word2vec models with additional examples of use from the biomedical literature. We propose interpreting the NER and REX tasks for COVID-19 as Question Answering (QA) incorporating general medical knowledge within the question, e.g. “does ‘cough’ (n-gram) belong to ‘clinical presentation/symptoms’ for COVID-19?”. We evaluated biomedical-specific pre-trained language models (BioBERT, SciBERT, ClinicalBERT, BlueBERT, and PubMedBERT) versus general-domain pre-trained language models (BERT, and RoBERTa) for transfer learning with COVID-19 dataset, i.e. task-specific fine-tuning considering NER as a sequence-level task. Using 2,060 QA for training (associations from 10 word2vec models) and 152 QA for validation (associations from 1 word2vec model), BERT obtained an F-measure of 87.38%, with precision = 93.75% and recall = 81.82%. SciBERT achieved the highest F-measure of 94.34%, with precision = 98.04% and recall = 90.91%. © 2021, Springer Nature Switzerland AG.

An exploration of methods using social media to examine local attitudes towards mask-wearing during a pandemic

During the COVID-19 health crisis, local public officials continue to expend considerable energy encouraging citizens to comply with prevention measures in order to reduce the spread of infection. During the pandemic, mask-wearing has been accepted among health officials as a simple preventative measure;however, some local areas have been more likely to comply than others. This paper explores methods to better understand local attitudes towards mask-wearing as a tool for public health officials' situational awareness when preparing public messaging campaigns. This exploration compares three methods to explore local attitudes: sentiment analysis, n-grams, and hashtags. We also explore hashtag co-occurrence networks as a possible starting point to begin the filtering process. The results show that while sentiment analysis is quick and easy to employ, the results offer little insight into specific local attitudes towards mask-wearing, while examining hashtags and hashtag co-occurrence networks may be used a tool for a more robust understanding of local areas when attempting to gain situational awareness. © 2021 Information Systems for Crisis Response and Management, ISCRAM. All rights reserved.