# Machine Learning in Detecting COVID-19 Misinformation on Twitter
## 2 Related Work
### Misinformation on Twitter  
In an exploratory study to counter Twitter misinformation, [PERSON] et al. [82] collected 1500 tweets from between January and mid-July 2020. The study used tweets from fact-checking websites that had been classified as false (or partially false) in addition to a random sample of tweets from publicly available corpus TweetsCOV19. To crawl the websites, BeautifulSoup was used to collect URLs and tweet IDs, which were then used to fetch the tweet using a Python library to access Twitter API, Tweepy. The content of the tweets was analyzed in terms of the used hashtags and emojis, distinctive terms, and emotional and psychological processes using the Linguistic Inquiry and Word Count program. Results suggested that the speed of propagation for Twitter misinformation is the highest among other tweets, and that misinformation tweets are more concerned with discrediting other news. [PERSON] et al. [83] presented a study on the propagation of misinformation via Twitter. They first studied conversations and discussions related to COVID-19. They presented this work as a starting point to social media discussions regarding myths, real news, and fake news related to the new pandemic; the world is now severely affected. Twitter shared both reliable and unreliable information on COVID-19, which helps to detect and estimate propagation, and causes misunderstandings among people. Accurate information from reliable sources and misinformation were retweeted several times, and the misinformation retweets were more than those of information from the CDC and WHO. They also revealed myths spreading through Twitter related to COVID-19. [PERSON] et al. [4] proposed a dense neural-network-based approach for detecting fake news using FNC-1 data. The study began with data preprocessing (removing stop words and punctuation, stemming) to implement the deep machine-learning algorithms. Their proposed model also indicated the problem of identifying the relationship with actual news, whether related or unrelated, when evaluating the correctness of the reported news with the actual news, which most of the models ignored. They represented text features using the TF-IDF vector model. On the basis of their simulation, they concluded that their proposed model outperformed all the other available models by 2.5%. They obtained entire system accuracy of 94.21%, and the considered stances between news articles with the headline articles were agree, unrelated, disagree, and discuss. The model achieved better results for all these stances except disagree, for which they obtained only 44%. They also implemented this model with BoW and Word2 Vec embedding methods. However, accuracy with the pretrained Word2 Vec model was very low as compared with that of the BoW and TF-IDF feature-extraction models. Detecting misinformation on Twitter is also a concern for Arabic tweets. [PERSON] et al. [84] created a dataset of Arabic tweets related to COVID-19 misinformation and annotation, categorizing them into fake or not. Different machine-learning algorithms are applied to detect misinformation: convolutional neural networks, recurrent neural networks, convolutional recurrent neural networks, support vector machine, multinomial naive Bayes, extreme gradient boosting, random forest, and stochastic gradient descent. Misinformation was collected from two websites--the Ministry of Health in Saudi Arabia websites and the WHO--and it was used to manually label misinformation tweets. Although Arabic is considered to be a challenging language in the area of natural language processing due to the existence of different dialects and many grammatical rules, results were promising. Feature selection was effective as a technique to improve the performance of a classifier. Likewise, [PERSON] et al. [85] used a deep-learning algorithm to detect misinformation in the LIAR dataset, which includes short online statements labeled for fakeness. Data preprocessing at the sentence level included removing stop words and stemming.