COVIDLies: Detecting COVID-19 Misinformation on Social Media

Hossain, Tamanna; Logan, Robert L.; Ugarte, Arjuna; Matsubara, Yoshitomo; Young, Sean D.; Singh, Sameer

doi:10.18653/v1/2020.nlpcovid19-2.11

Cited by 171 publications

(174 citation statements)

References 29 publications

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“…Their Covid-HeRA dataset contains 61,286 tweets labeled as not severe, possibly severe, highly severe, refutes/rebuts, and real news/claims. Hossain et al [68] collaborate with researchers from the UCI school of medicine to establish a set of common Misconceptions. These misconceptions are used to label Tweets.…”

Section: Misinformation Detectionmentioning

confidence: 99%

Deep Learning applications for COVID-19

2021

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This survey explores how Deep Learning has battled the COVID-19 pandemic and provides directions for future research on COVID-19. We cover Deep Learning applications in Natural Language Processing, Computer Vision, Life Sciences, and Epidemiology. We describe how each of these applications vary with the availability of big data and how learning tasks are constructed. We begin by evaluating the current state of Deep Learning and conclude with key limitations of Deep Learning for COVID-19 applications. These limitations include Interpretability, Generalization Metrics, Learning from Limited Labeled Data, and Data Privacy. Natural Language Processing applications include mining COVID-19 research for Information Retrieval and Question Answering, as well as Misinformation Detection, and Public Sentiment Analysis. Computer Vision applications cover Medical Image Analysis, Ambient Intelligence, and Vision-based Robotics. Within Life Sciences, our survey looks at how Deep Learning can be applied to Precision Diagnostics, Protein Structure Prediction, and Drug Repurposing. Deep Learning has additionally been utilized in Spread Forecasting for Epidemiology. Our literature review has found many examples of Deep Learning systems to fight COVID-19. We hope that this survey will help accelerate the use of Deep Learning for COVID-19 research.

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Section: Misinformation Detectionmentioning

confidence: 99%

Deep Learning applications for COVID-19

2021

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“…Elhadad et al [23] constructed a voting ensemble machine learning classifier for fake news detection that uses seven feature extraction techniques and ten machine learning models. Tamanna et al [22] used the COVIDLIES dataset to detect the misinformation by retrieving the misconceptions relevant to the Twitter posts. For COVID-19 fake news detection and fact-checking, Rutvik et al [21] proposed a two-stage transformer model.…”

Section: A Covid-19 Fake News Detectionmentioning

confidence: 99%

CoAID-DEEP: An Optimized Intelligent Framework for Automated Detecting COVID-19 Misleading Information on Twitter

et al. 2021

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COVID-19 has affected all peoples' lives. Though COVID-19 is on the rising, the existence of misinformation about the virus also grows in parallel. Additionally, the spread of misinformation has created confusion among people, caused disturbances in society, and even led to deaths. Social media is central to our daily lives. The Internet has become a significant source of knowledge. Owing to the widespread damage caused by fake news, it is important to build computerized systems to detect fake news. The paper proposes an updated deep neural network for identification of false news. The deep learning techniques are The Modified-LSTM (one to three layers) and The Modified GRU (one to three layers).In particular, we carry out investigations of a large dataset of tweets passing on data with respect to COVID-19. In our study, we separate the dubious claims into two categories: true and false. We compare the performance of the various algorithms in terms of prediction accuracy. The six machine learning techniques are decision trees, logistic regression, k nearest neighbors, random forests, support vector machines, and naïve Bayes (NB). The parameters of deep learning techniques are optimized using Keras-tuner. Four Benchmark datasets were used. Two feature extraction methods were used (TF-ID with N-gram) to extract essential features from the four benchmark datasets for the baseline machine learning model and word embedding feature extraction method for the proposed deep neural network methods. The results obtained with the proposed framework reveal high accuracy in detecting Fake and non-Fake tweets containing COVID-19 information. These results demonstrate significant improvement as compared to the existing state of art results of baseline machine learning models. In our approach, we classify the data into two categories: fake or nonfake. We compare the execution of the proposed approaches with Six machine learning procedures. The six machine learning procedures are Decision Tree (DT), Logistic Regression (LR), K Nearest Neighbor (KNN), Random Forest (RF), Support Vector Machine (SVM), and Naive Bayes (NB). The parameters of deep learning techniques are optimized using Keras-tuner. Four Benchmark datasets were used. Two feature extraction methods were used (TF-ID with N-gram) to extract essential features from the four benchmark datasets for the baseline machine learning model and word embedding feature extraction method for the proposed deep neural network methods. The results obtained with the proposed framework reveal high accuracy in detecting Fake and non-Fake tweets containing COVID-19 information. These results demonstrate significant improvement as compared to the existing state of art results of baseline machine learning models.

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“…The first one includes studies that are based entirely on information related to COVID-19 (without specifically distinguishing disinformation). The most relevant research in this category includes: the creation of a COVID-19 relevant Twitter dataset based on a time period covering the pandemic [ 24 ] or based on certain manually selected COVID-related hashtags [ 25 – 29 ]; sentiment analysis of information spread on Twitter [ 28 , 30 , 30 , 36 – 41 ]; analysis of the spreading pattern of news with different credibility on Twitter [ 28 , 31 ] and other social media platforms [ 32 ]; tweet misconception and stance dataset labelling and classification [ 42 ]; analysis of tweet topics using unsupervised topic modelling [ 30 , 36 – 41 , 43 – 49 ]; classification of informativeness of a tweet related to COVID-19 [ 50 , 51 ]. Among these, the study most similar to ours is Gencoglu (2020) [ 52 ], which classifies tweets into 11 pre-defined classes using BERT and LaBSE [ 53 ].…”

Section: Related Workmentioning

confidence: 99%

Classification aware neural topic model for COVID-19 disinformation categorisation

et al. 2021

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The explosion of disinformation accompanying the COVID-19 pandemic has overloaded fact-checkers and media worldwide, and brought a new major challenge to government responses worldwide. Not only is disinformation creating confusion about medical science amongst citizens, but it is also amplifying distrust in policy makers and governments. To help tackle this, we developed computational methods to categorise COVID-19 disinformation. The COVID-19 disinformation categories could be used for a) focusing fact-checking efforts on the most damaging kinds of COVID-19 disinformation; b) guiding policy makers who are trying to deliver effective public health messages and counter effectively COVID-19 disinformation. This paper presents: 1) a corpus containing what is currently the largest available set of manually annotated COVID-19 disinformation categories; 2) a classification-aware neural topic model (CANTM) designed for COVID-19 disinformation category classification and topic discovery; 3) an extensive analysis of COVID-19 disinformation categories with respect to time, volume, false type, media type and origin source.

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COVIDLies: Detecting COVID-19 Misinformation on Social Media

Cited by 171 publications

References 29 publications

Deep Learning applications for COVID-19

Deep Learning applications for COVID-19

CoAID-DEEP: An Optimized Intelligent Framework for Automated Detecting COVID-19 Misleading Information on Twitter

Classification aware neural topic model for COVID-19 disinformation categorisation

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