# Machine Learning in Detecting COVID-19 Misinformation on Twitter
## 2 Related Work
### Machine-Learning Models to Detect Misinformation
[59] used three machine-learning methods in a proposed system that could identify misinformation from Twitter data, naive Bayes (accuracy, 95.55%), neural network (accuracy, 97.09%), and support vector machine (accuracy, 98.15%). The system consisted of three major parts: data collection, data preprocessing, and machine-learning methods. The data extracted from Twitter API spanned two months in 2017 for the selected topics, including natural events, general issues, and the Thai monarchy. Tweets were collected using hashtags for each topic, and manually labeled as valid or fake. The authors selected the 22 most relevant attributes used in previous studies of the available attributes in a Twitter message. Among those attributes are ID, location, time zone, follower count, retweet count, and creation date. Weka was used to run the three machine-learning methods, and results suggested that fake news has a shorter lifetime than that of valid news. The accuracy of the approach highly depends on correctly labeled training data. The method did not depend on any semantic analyses on Twitter data. The authors in [80], instead of focusing on machine-learning algorithms to detect Twitter misinformation, presented approaches to assist users in validating news on social media. Existing approaches such as TweetCred, B.S. Detector, Fake News Detector, Fake News AO, and Fake News Check label news as fake or valid. Therefore, the authors presented a browser plugin for Twitter, TrustyTweet, using the design science approach to create new and innovative artifacts. The main objective is to improve media literacy by providing neutral and intuitive hints. It is dependent on indicators of fake news from previous studies such as consecutive capitalization, excessive usage of punctuation, wrong punctuation at the end of sentences, excessive usage and attention grabbing of emoticons, a default account image, and the absence of an official account verification seal (p. 1848). The plugin was developed for the Firefox browser, which shows indicators related to a specific tweet. The results of the study showed that the approach is promising in supporting users on social media by encouraging a learning effect to determine where Twitter data are fake. [PERSON] et al. [81] presented a methodology to characterize the two online communities on Twitter of COVID-19 misinformation: misinformed and informed users. Misinformed users are those who are actively posting misinformation, as opposed to informed users who spread true information. The methodology is unique and different from previous studies in that it investigates the sociolinguistic patterns of the two communities. Instead of focusing on Twitter data misinformation, the research focuses on the communities using their content, behaviors, and interactions. The Twitter dataset was collected using the Twitter search API with a set of hashtags and keywords. It was manually categorized by using previously identified 17 categories (e.g., irrelevant, conspiracy, true treatment, true prevention, sarcasm, fake cure, fake treatment). The authors collected 4573 annotated tweets from 3629 authors. The new dataset is called CMU-MisCOV19. A +1 was assigned to true data, and \(-1\) was assigned to misinformation according to the defined categories to differentiate between the two groups of users (informed and misinformed). Network analysis was performed by using the network density of a given tweet. They also used BottHunter to perform bot analyses within the two groups, and found that the percentage of bots within misinformed users was higher. Lastly, linguistic analysis was performed using the Linguistic Inquiry and Word Count program. The results indicated that misinformed users tend to be more informal and use fewer swear words. One of the limitations of the study is the limited timeline of the collected data, which was annotated by only one annotator.