COVID-19 Open Research Dataset Research Articles

New trends in scientific knowledge graphs and research impact assessment

New trends in scientific knowledge graphs and research impact assessment

Covid-on-the-Web: Exploring the COVID-19 scientific literature through visualization of linked data from entity and argument mining

Abstract The unprecedented mobilization of scientists caused by the COVID-19 pandemic has generated an enormous number of scholarly articles that are impossible for a human being to keep track of and explore without appropriate tool support. In this context, we created the Covid-on-the-Web project, which aims to assist the accessing, querying, and sense-making of COVID-19-related literature by combining efforts from the semantic web, natural language processing, and visualization fields. In particular, in this paper we present an RDF data set (a linked version of the “COVID-19 Open Research Dataset” (CORD-19), enriched via entity linking and argument mining) and the “Linked Data Visualizer” (LDViz), which assists the querying and visual exploration of the referred data set. The LDViz tool assists in the exploration of different views of the data by combining a querying management interface, which enables the definition of meaningful subsets of data through SPARQL queries, and a visualization interface based on a set of six visualization techniques integrated in a chained visualization concept, which also supports the tracking of provenance information. We demonstrate the potential of our approach to assist biomedical researchers in solving domain-related tasks, as well as to perform exploratory analyses through use case scenarios.

Converting Biomedical Text Annotated Resources into FAIR Research Objects with an Open Science Platform

Today, there are excellent resources for the semantic annotation of biomedical text. These resources span from ontologies, tools for NLP, annotators, and web services. Most of these are available either in the form of open source components (i.e., MetaMap) or as web services that offer free access (i.e., Whatizit). In order to use these resources in automatic text annotation pipelines, researchers face significant technical challenges. For open-source tools, the challenges include the setting up of the computational environment, the resolution of dependencies, as well as the compilation and installation of the software. For web services, the challenge is implementing clients to undertake communication with the respective web APIs. Even resources that are available as Docker containers (i.e., NCBO annotator) require significant technical skills for installation and setup. This work deals with the task of creating ready-to-install and run Research Objects (ROs) for a large collection of components in biomedical text analysis. These components include (a) tools such as cTAKES, NOBLE Coder, MetaMap, NCBO annotator, BeCAS, and Neji; (b) ontologies from BioPortal, NCBI BioSystems, and Open Biomedical Ontologies; and (c) text corpora such as BC4GO, Mantra Gold Standard Corpus, and the COVID-19 Open Research Dataset. We make these resources available in OpenBio.eu, an open-science RO repository and workflow management system. All ROs can be searched, shared, edited, downloaded, commented on, and rated. We also demonstrate how one can easily connect these ROs to form a large variety of text annotation pipelines.

Demystifying COVID-19 publications: institutions, journals, concepts, and topics.

Objective:We analyzed the COVID-19 Open Research Dataset (CORD-19) to understand leading research institutions, collaborations among institutions, major publication venues, key research concepts, and topics covered by pandemic-related research.Methods:We conducted a descriptive analysis of authors' institutions and relationships, automatic content extraction of key words and phrases from titles and abstracts, and topic modeling and evolution. Data visualization techniques were applied to present the results of the analysis.Results:We found that leading research institutions on COVID-19 included the Chinese Academy of Sciences, the US National Institutes of Health, and the University of California. Research studies mostly involved collaboration among different institutions at national and international levels. In addition to bioRxiv, major publication venues included journals such as The BMJ, PLOS One, Journal of Virology, and The Lancet. Key research concepts included the coronavirus, acute respiratory impairments, health care, and social distancing. The ten most popular topics were identified through topic modeling and included human metapneumovirus and livestock, clinical outcomes of severe patients, and risk factors for higher mortality rate.Conclusion:Data analytics is a powerful approach for quickly processing and understanding large-scale datasets like CORD-19. This approach could help medical librarians, researchers, and the public understand important characteristics of COVID-19 research and could be applied to the analysis of other large datasets.

Efficient Self-Supervised Metric Information Retrieval: A Bibliography Based Method Applied to COVID Literature.

The literature on coronaviruses counts more than 300,000 publications. Finding relevant papers concerning arbitrary queries is essential to discovery helpful knowledge. Current best information retrieval (IR) use deep learning approaches and need supervised training sets with labeled data, namely to know a priori the queries and their corresponding relevant papers. Creating such labeled datasets is time-expensive and requires prominent experts’ efforts, resources insufficiently available under a pandemic time pressure. We present a new self-supervised solution, called SUBLIMER, that does not require labels to learn to search on corpora of scientific papers for most relevant against arbitrary queries. SUBLIMER is a novel efficient IR engine trained on the unsupervised COVID-19 Open Research Dataset (CORD19), using deep metric learning. The core point of our self-supervised approach is that it uses no labels, but exploits the bibliography citations from papers to create a latent space where their spatial proximity is a metric of semantic similarity; for this reason, it can also be applied to other domains of papers corpora. SUBLIMER, despite is self-supervised, outperforms the Precision@5 (P@5) and Bpref of the state-of-the-art competitors on CORD19, which, differently from our approach, require both labeled datasets and a number of trainable parameters that is an order of magnitude higher than our.

Models and Processes to Extract Drug-like Molecules From Natural Language Text.

Researchers worldwide are seeking to repurpose existing drugs or discover new drugs to counter the disease caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). A promising source of candidates for such studies is molecules that have been reported in the scientific literature to be drug-like in the context of viral research. However, this literature is too large for human review and features unusual vocabularies for which existing named entity recognition (NER) models are ineffective. We report here on a project that leverages both human and artificial intelligence to detect references to such molecules in free text. We present 1) a iterative model-in-the-loop method that makes judicious use of scarce human expertise in generating training data for a NER model, and 2) the application and evaluation of this method to the problem of identifying drug-like molecules in the COVID-19 Open Research Dataset Challenge (CORD-19) corpus of 198,875 papers. We show that by repeatedly presenting human labelers only with samples for which an evolving NER model is uncertain, our human-machine hybrid pipeline requires only modest amounts of non-expert human labeling time (tens of hours to label 1778 samples) to generate an NER model with an F-1 score of 80.5%—on par with that of non-expert humans—and when applied to CORD’19, identifies 10,912 putative drug-like molecules. This enriched the computational screening team’s targets by 3,591 molecules, of which 18 ranked in the top 0.1% of all 6.6 million molecules screened for docking against the 3CLPro protein.

Shall I Work with Them? A Knowledge Graph-Based Approach for Predicting Future Research Collaborations.

We consider the prediction of future research collaborations as a link prediction problem applied on a scientific knowledge graph. To the best of our knowledge, this is the first work on the prediction of future research collaborations that combines structural and textual information of a scientific knowledge graph through a purposeful integration of graph algorithms and natural language processing techniques. Our work: (i) investigates whether the integration of unstructured textual data into a single knowledge graph affects the performance of a link prediction model, (ii) studies the effect of previously proposed graph kernels based approaches on the performance of an ML model, as far as the link prediction problem is concerned, and (iii) proposes a three-phase pipeline that enables the exploitation of structural and textual information, as well as of pre-trained word embeddings. We benchmark the proposed approach against classical link prediction algorithms using accuracy, recall, and precision as our performance metrics. Finally, we empirically test our approach through various feature combinations with respect to the link prediction problem. Our experimentations with the new COVID-19 Open Research Dataset demonstrate a significant improvement of the abovementioned performance metrics in the prediction of future research collaborations.

KAAPA: Knowledge Aware Answers from PDF Analysis

We present KaaPa (Knowledge Aware Answers from Pdf Analysis), an integrated solution for machine reading comprehension over both text and tables extracted from PDFs. KaaPa enables interactive question refinement using facets generated from an automatically induced Knowledge Graph. In addition it provides a concise summary of the supporting evidence for the provided answers by aggregating information across multiple sources. KaaPa can be applied consistently to any collection of documents in English with zero domain adaptation effort. We showcase the use of KaaPa for QA on scientific literature using the COVID-19 Open Research Dataset.

Queries related to COVID-19: a more effective retrieval through finetuned ALBERT with BM25L question answering system

PurposeThe purpose of this paper is to build a better question answering (QA) system that can furnish more improved retrieval of answers related to COVID-19 queries from the COVID-19 open research data set (CORD-19). As CORD-19 has an up-to-date collection of coronavirus literature, text mining approaches can be successfully used to retrieve answers pertaining to all coronavirus-related questions. The existing a lite BERT for self-supervised learning of language representations (ALBERT) model is finetuned for retrieving all COVID relevant information to scientific questions posed by the medical community and to highlight the context related to the COVID-19 query.Design/methodology/approachThis study presents a finetuned ALBERT-based QA system in association with Best Match25 (Okapi BM25) ranking function and its variant BM25L for context retrieval and provided high scores in benchmark data sets such as SQuAD for answers related to COVID-19 questions. In this context, this paper has built a QA system, pre-trained on SQuAD and finetuned it on CORD-19 data to retrieve answers related to COVID-19 questions by extracting semantically relevant information related to the question.FindingsBM25L is found to be more effective in retrieval compared to Okapi BM25. Hence, finetuned ALBERT when extended to the CORD-19 data set provided accurate results.Originality/valueThe finetuned ALBERT QA system was developed and tested for the first time on the CORD-19 data set to extract context and highlight the span of the answer for more clarity to the user.

Using a Secure, Continually Updating, Web Source Processing Pipeline to Support the Real-Time Data Synthesis and Analysis of Scientific Literature: Development and Validation Study.

BackgroundThe scale and quality of the global scientific response to the COVID-19 pandemic have unquestionably saved lives. However, the COVID-19 pandemic has also triggered an unprecedented “infodemic”; the velocity and volume of data production have overwhelmed many key stakeholders such as clinicians and policy makers, as they have been unable to process structured and unstructured data for evidence-based decision making. Solutions that aim to alleviate this data synthesis–related challenge are unable to capture heterogeneous web data in real time for the production of concomitant answers and are not based on the high-quality information in responses to a free-text query.ObjectiveThe main objective of this project is to build a generic, real-time, continuously updating curation platform that can support the data synthesis and analysis of a scientific literature framework. Our secondary objective is to validate this platform and the curation methodology for COVID-19–related medical literature by expanding the COVID-19 Open Research Dataset via the addition of new, unstructured data.MethodsTo create an infrastructure that addresses our objectives, the PanSurg Collaborative at Imperial College London has developed a unique data pipeline based on a web crawler extraction methodology. This data pipeline uses a novel curation methodology that adopts a human-in-the-loop approach for the characterization of quality, relevance, and key evidence across a range of scientific literature sources.ResultsREDASA (Realtime Data Synthesis and Analysis) is now one of the world’s largest and most up-to-date sources of COVID-19–related evidence; it consists of 104,000 documents. By capturing curators’ critical appraisal methodologies through the discrete labeling and rating of information, REDASA rapidly developed a foundational, pooled, data science data set of over 1400 articles in under 2 weeks. These articles provide COVID-19–related information and represent around 10% of all papers about COVID-19.ConclusionsThis data set can act as ground truth for the future implementation of a live, automated systematic review. The three benefits of REDASA’s design are as follows: (1) it adopts a user-friendly, human-in-the-loop methodology by embedding an efficient, user-friendly curation platform into a natural language processing search engine; (2) it provides a curated data set in the JavaScript Object Notation format for experienced academic reviewers’ critical appraisal choices and decision-making methodologies; and (3) due to the wide scope and depth of its web crawling method, REDASA has already captured one of the world’s largest COVID-19–related data corpora for searches and curation.

COVID-19 information retrieval with deep-learning based semantic search, question answering, and abstractive summarization

The COVID-19 global pandemic has resulted in international efforts to understand, track, and mitigate the disease, yielding a significant corpus of COVID-19 and SARS-CoV-2-related publications across scientific disciplines. Throughout 2020, over 400,000 coronavirus-related publications have been collected through the COVID-19 Open Research Dataset. Here, we present CO-Search, a semantic, multi-stage, search engine designed to handle complex queries over the COVID-19 literature, potentially aiding overburdened health workers in finding scientific answers and avoiding misinformation during a time of crisis. CO-Search is built from two sequential parts: a hybrid semantic-keyword retriever, which takes an input query and returns a sorted list of the 1000 most relevant documents, and a re-ranker, which further orders them by relevance. The retriever is composed of a deep learning model (Siamese-BERT) that encodes query-level meaning, along with two keyword-based models (BM25, TF-IDF) that emphasize the most important words of a query. The re-ranker assigns a relevance score to each document, computed from the outputs of (1) a question–answering module which gauges how much each document answers the query, and (2) an abstractive summarization module which determines how well a query matches a generated summary of the document. To account for the relatively limited dataset, we develop a text augmentation technique which splits the documents into pairs of paragraphs and the citations contained in them, creating millions of (citation title, paragraph) tuples for training the retriever. We evaluate our system (http://einstein.ai/covid) on the data of the TREC-COVID information retrieval challenge, obtaining strong performance across multiple key information retrieval metrics.

A comparative analysis of system features used in the TREC-COVID information retrieval challenge

The COVID-19 pandemic has resulted in a rapidly growing quantity of scientific publications from journal articles, preprints, and other sources. The TREC-COVID Challenge was created to evaluate information retrieval (IR) methods and systems for this quickly expanding corpus. Using the COVID-19 Open Research Dataset (CORD-19), several dozen research teams participated in over 5 rounds of the TREC-COVID Challenge. While previous work has compared IR techniques used on other test collections, there are no studies that have analyzed the methods used by participants in the TREC-COVID Challenge. We manually reviewed team run reports from Rounds 2 and 5, extracted features from the documented methodologies, and used a univariate and multivariate regression-based analysis to identify features associated with higher retrieval performance. We observed that fine-tuning datasets with relevance judgments, MS-MARCO, and CORD-19 document vectors was associated with improved performance in Round 2 but not in Round 5. Though the relatively decreased heterogeneity of runs in Round 5 may explain the lack of significance in that round, fine-tuning has been found to improve search performance in previous challenge evaluations by improving a system’s ability to map relevant queries and phrases to documents. Furthermore, term expansion was associated with improvement in system performance, and the use of the narrative field in the TREC-COVID topics was associated with decreased system performance in both rounds. These findings emphasize the need for clear queries in search. While our study has some limitations in its generalizability and scope of techniques analyzed, we identified some IR techniques that may be useful in building search systems for COVID-19 using the TREC-COVID test collections.

HITS-based attentional neural model for abstractive summarization

Automatic abstractive summary generation is still an open problem in natural language processing field. Conventional encoder–decoder model based abstractive summarization methods often suffer from repetition and semantic irrelevance. Recent studies apply traditional attention or graph-based attention on the encoder–decoder model to tackle the problem, under the assumption that all the sentences in the original document are indistinguishable from each other. But in a document, the same words in different sentences are not equally important, i.e., the words in a trivial sentence are less important than the words in a salient sentence. Based on it, we develop a HITS-based attention mechanism in this paper, which fully leverages sentence-level and word-level information by considering sentences and words in the original document as authorities and hubs. Based on it, we present a novel abstractive summarization method, with Kullback–Leibler (KL) divergence to refine the attention value, meanwhile we propose a comparison mechanism in summary generation to further improve the summarization performance. When evaluated on the CNN/Daily Mail and NYT datasets, the experimental results demonstrate the improvement of summarization performance and show the performance of our proposed method is comparable with that of the other summarization methods. Besides, we also conduct experiments on CORD-19 dataset (COVID-19 Open Research Dataset) which is a biomedical domain dataset, and the experimental results show great performance of our proposed method compared with that of the other state-of-the-art summarization methods.

Revealing Opinions for COVID-19 Questions Using a Context Retriever, Opinion Aggregator, and Question-Answering Model: Model Development Study.

BackgroundCOVID-19 has challenged global public health because it is highly contagious and can be lethal. Numerous ongoing and recently published studies about the disease have emerged. However, the research regarding COVID-19 is largely ongoing and inconclusive.ObjectiveA potential way to accelerate COVID-19 research is to use existing information gleaned from research into other viruses that belong to the coronavirus family. Our objective is to develop a natural language processing method for answering factoid questions related to COVID-19 using published articles as knowledge sources.MethodsGiven a question, first, a BM25-based context retriever model is implemented to select the most relevant passages from previously published articles. Second, for each selected context passage, an answer is obtained using a pretrained bidirectional encoder representations from transformers (BERT) question-answering model. Third, an opinion aggregator, which is a combination of a biterm topic model and k-means clustering, is applied to the task of aggregating all answers into several opinions.ResultsWe applied the proposed pipeline to extract answers, opinions, and the most frequent words related to six questions from the COVID-19 Open Research Dataset Challenge. By showing the longitudinal distributions of the opinions, we uncovered the trends of opinions and popular words in the articles published in the five time periods assessed: before 1990, 1990-1999, 2000-2009, 2010-2018, and since 2019. The changes in opinions and popular words agree with several distinct characteristics and challenges of COVID-19, including a higher risk for senior people and people with pre-existing medical conditions; high contagion and rapid transmission; and a more urgent need for screening and testing. The opinions and popular words also provide additional insights for the COVID-19–related questions.ConclusionsCompared with other methods of literature retrieval and answer generation, opinion aggregation using our method leads to more interpretable, robust, and comprehensive question-specific literature reviews. The results demonstrate the usefulness of the proposed method in answering COVID-19–related questions with main opinions and capturing the trends of research about COVID-19 and other relevant strains of coronavirus in recent years.

A Comprehensive Overview of the COVID-19 Literature: Machine Learning-Based Bibliometric Analysis.

BackgroundShortly after the emergence of COVID-19, researchers rapidly mobilized to study numerous aspects of the disease such as its evolution, clinical manifestations, effects, treatments, and vaccinations. This led to a rapid increase in the number of COVID-19–related publications. Identifying trends and areas of interest using traditional review methods (eg, scoping and systematic reviews) for such a large domain area is challenging.ObjectiveWe aimed to conduct an extensive bibliometric analysis to provide a comprehensive overview of the COVID-19 literature.MethodsWe used the COVID-19 Open Research Dataset (CORD-19) that consists of a large number of research articles related to all coronaviruses. We used a machine learning–based method to analyze the most relevant COVID-19–related articles and extracted the most prominent topics. Specifically, we used a clustering algorithm to group published articles based on the similarity of their abstracts to identify research hotspots and current research directions. We have made our software accessible to the community via GitHub.ResultsOf the 196,630 publications retrieved from the database, we included 28,904 in our analysis. The mean number of weekly publications was 990 (SD 789.3). The country that published the highest number of COVID-19–related articles was China (2950/17,270, 17.08%). The highest number of articles were published in bioRxiv. Lei Liu affiliated with the Southern University of Science and Technology in China published the highest number of articles (n=46). Based on titles and abstracts alone, we were able to identify 1515 surveys, 733 systematic reviews, 512 cohort studies, 480 meta-analyses, and 362 randomized control trials. We identified 19 different topics covered among the publications reviewed. The most dominant topic was public health response, followed by clinical care practices during the COVID-19 pandemic, clinical characteristics and risk factors, and epidemic models for its spread.ConclusionsWe provide an overview of the COVID-19 literature and have identified current hotspots and research directions. Our findings can be useful for the research community to help prioritize research needs and recognize leading COVID-19 researchers, institutes, countries, and publishers. Our study shows that an AI-based bibliometric analysis has the potential to rapidly explore a large corpus of academic publications during a public health crisis. We believe that this work can be used to analyze other eHealth-related literature to help clinicians, administrators, and policy makers to obtain a holistic view of the literature and be able to categorize different topics of the existing research for further analyses. It can be further scaled (for instance, in time) to clinical summary documentation. Publishers should avoid noise in the data by developing a way to trace the evolution of individual publications and unique authors.

Impact of COVID-19 on longitudinal ophthalmology authorship gender trends

BackgroundThe COVID-19 pandemic increased the gender gap in academic publishing. This study assesses COVID-19’s impact on ophthalmology gender authorship distribution and compares the gender authorship proportion of COVID-19 ophthalmology-related articles to previous ophthalmology articles.MethodsThis cohort study includes authors listed in all publications related to ophthalmology in the COVID-19 Open Research Dataset and CDC COVID-19 research database. Articles from 65 ophthalmology journals from January to July 2020 were selected. All previous articles published in the same journals were extracted from PubMed. Gender-API determined authors’ gender.ResultsOut of 119,457 COVID-19-related articles, we analyzed 528 ophthalmology-related articles written by 2518 authors. Women did not exceed 40% in any authorship positions and were most likely to be middle, first, and finally, last authors. The proportions of women in all authorship positions from the 2020 COVID-19 group (29.6% first, 31.5% middle, 22.1% last) are significantly lower compared to the predicted 2020 data points (37.4% first, 37.0% middle, 27.6% last) (p < .01). The gap between the proportion of female authors in COVID-19 ophthalmology research and the 2020 ophthalmology-predicted proportion (based on 2002–2019 data) is 6.1% for overall authors, 7.8% for first authors, and 5.5% for last and middle authors. The 2020 COVID-19 authorship group (1925 authors) was also compared to the 2019 group (33,049 authors) based on journal category (clinical/basic science research, general/subspecialty ophthalmology, journal impact factor).ConclusionsCOVID-19 amplified the authorship gender gap in ophthalmology. When compared to previous years, there was a greater decrease in women’s than men’s academic productivity.

WITHDRAWN: Classification of covid related articles using machine learning

Covid 19 pandemic has placed the entire world in a precarious condition. Earlier it was a serious issue in china whereas now it is being witnessed by citizens all over the world. Scientists are working hard to find treatment and vaccines for the coronavirus, also termed as covid. With the growing literature, it has become a major challenge for the medical community to find answers to questions related to covid-19. We have proposed a machine learning-based system that uses text classification applications of NLP to extract information from the scientific literature. Classification of large textual data makes the searching process easier thus useful for scientists. The main aim of our system is to classify the abstracts related to covid with their respective journals so that a researcher can refer to articles of his interest from the required journals instead of searching all the articles. In this paper, we describe our methodology needed to build such a system. Our system experiments on the COVID-19 open research dataset and the performance is evaluated using classifiers like KNN, MLP, etc. An explainer was also built using XGBoost to show the model predictions.

The nurse COVID and historical epidemics literature repository: Development, description, and summary

BackgroundDuring COVID-19, a Kaggle challenge was issued to data scientists to leverage text mining to provide high-level summaries of full-text articles in the COVID-19 Open Research Dataset (CORD-19) data set, a data set containing articles around COVID-19 and other epidemics. A question was asked: “What if nursing had something similar?” PurposeDescribe the development and function of the Nursing COVID and Historical Epidemic Literature and describe high-level summaries of abstracts within the repository. MethodNurse-specific literature was abstracted from two data sets: CORD-19 and LitCOVID. LitCOVID is a data set containing the most up-to-date literature around COVID-19. Multiple text mining algorithms were utilized to provide summaries of the articles. DiscussionAs of July 2020, the repository contains 760 articles. Summaries indicate the importance of psychological support for nurses and of high-impact rapid education. ConclusionTo our knowledge, this repository is the only repository specific for nursing that utilizes text mining to provide summaries.

Convergence in Viral Outbreak Research: Using Natural Language Processing to Define Network Bridges in the Bench-Bedside-Population Paradigm

Research on viral outbreaks at the pandemic scale responds to heightened social urgency and the need to expedite scientific discovery from the ‘bench’ to the ‘bedside’ to the wider population. We sought to better understand translational research within the context of pandemics, both historical and present day, by tracking publication trends in the immediate aftermath of virus outbreaks. We used a blend of natural language processing (NLP), social network analysis, and human annotation approaches to analyze the 85,663 articles in the COVID-19 Open Research Dataset (CORD-19). We found stable and repeated characteristics throughout subsets of peer-reviewed published literature corresponding to seven different viral outbreaks over the last several decades. Three distinct groups or ‘neighborhoods’ recurred across all of the model networks: (1) bench science, (2) clinical treatments, and (3) broader public health trends. Notably, in each historical virus model, small ‘bridge’ nodes representing translational research connect the three otherwise disconnected neighborhoods. These bridging studies embody research convergence by both integrating the vocabulary and methods of different disciplines and bodies of previous work and by citing other papers beyond their narrow field. In the case of COVID-19, the literature continues to evolve apace along with the virus, and we can witness the phases of response unfold as the science progresses. This study demonstrates how the different sectors of biomedical research respond independently to public health emergencies and how translational research can facilitate greater information synthesis and exchange between disciplinary silos.Â

Classification of a COVID-19 dataset by using labels created from clustering algorithms

&lt;span&gt;Novel coronavirus (COVID-19) is a newly discovered infectious disease that has received much attention in the literature because of its rapid spread and daily global deaths attributable to such disease. The White House, together with a coalition of leading research groups, has published the freely available COVID-19 Open Research Dataset to help the global research community apply the recent advances in natural language processing and other AI techniques in generating novel insights that can support the ongoing fight against this disease. In this paper, the hierarchical and k-means clustering techniques are used to create a tool for identifying similar articles on COVID-19 and filtering them based on their titles. These articles are classified by applying three data mining techniques, namely, random forest (RF), decision tree (DT) and bagging. By using this tool, specialists can limit the number of articles they need to study and pre-process these articles via data framing, tokenisation, normalisation and term frequency-inverse document frequency. Given its 2D nature, the dimensionality of this dataset is reduced by applying t-SNE. The aforementioned data mining techniques are then cross validated to test the accuracy, precision and recall performance of the proposed tool. Results show that the proposed tool effectively extracts the keywords for each cluster, with RF, DT and bagging achieving optimal accuracies of 98.267%, 97.633% and 97.833%, respectively.&lt;/span&gt;