An Ensemble Learning Based Approach for Detecting and Tracking COVID19 Rumors

Abstract

Rumors regarding epidemic diseases such as COVID 19, medicines and treatments, diagnostic methods and public emergencies can have harmful impacts on health and political, social and other aspects of people's lives, especially during emergency situations and health crises. With huge amounts of content being posted to social media every second during these situations, it becomes very difficult to detect fake news (rumors) that poses threats to the stability and sustainability of the healthcare sector. A rumor is defined as a statement for which truthfulness has not been verified. During COVID 19, people found difficulty in obtaining the most truthful news easily because of the huge amount of unverified information on social media. Several methods have been applied for detecting rumors and tracking their sources for COVID 19-related information. However, very few studies have been conducted for this purpose for the Arabic language, which has unique characteristics. Therefore, this paper proposes a comprehensive approach which includes two phases: detection and tracking. In the detection phase of the study carried out, several standalone and ensemble machine learning methods were applied on the Arcov-19 dataset. A new detection model was used which combined two models: The Genetic Algorithm Based Support Vector Machine (that works on users' and tweets' features) and the stacking ensemble method (that works on tweets' texts). In the tracking phase, several similarity-based techniques were used to obtain the top 1% of similar tweets to a target tweet/post, which helped to find the source of the rumors. The experiments showed interesting results in terms of accuracy, precision, recall and F1-Score for rumor detection (the accuracy reached 92.63%), and showed interesting findings in the tracking phase, in terms of ROUGE L precision, recall and F1-Score for similarity techniques.

An Intelligent COVID-19-Related Arabic Text Detection Framework Based on Transfer Learning Using Context Representation

“…At the same time, they created a new dataset called SenAIT, by merging the common emotions of the SenWave dataset with AIT datasets. Recently, the authors Qasem et al [12] proposed a new approach based on ensemble techniques for detecting and tracking COVID-19 rumors.…”

“…Many researchers have proposed several models for detecting COVID-19 information from social media. For example, an ensemble technique for detecting and tracking COVID-19 rumors has been used [12]. Te authors [13] proposed a DL-based model.…”

The Detection of Fake News in Arabic Tweets Using Deep Learning

“…The effect of misinformation is not confined to individual lives but also includes society and the economy. Several studies [3,6,14,[16][17][18] have concentrated on assessing the credibility of information related to the spread of COVID-19 in Arabic communities via social media platforms such as Twitter.…”

“…Qasem et al [17] proposed a comprehensive approach that comprised two models. The first model was used to assess the reliability of Twitter posts relating to COVID-19, whereas tracking and identifying the source user who released the misleading news is the purpose of another model.…”

Hard Voting Approach using SVM, Naïve Bays and Decision Tree for Kurdish Fake News Detection

“…In addition to this, [18] Employed soft voting on XGBoost, RF, and MLP on 4024 Arabic tweets during the FIRE 2019 forum, and the model scored 84.4% F1-points. The work [19] combined stacking classifier Genetic Algorithm Based Support Vector Machine and the Logistic Regression on the Arabic rumors dataset. The accuracy reached 92.63%.…”