Rumour Source Identification Using Machine Learning Algorithms
Keywords:
Real-time rumour prediction, social media, Support Vector MachineAbstract
Rumour is one of the most prevalent types of false information on social media, and it should be prevented as soon as possible to avoid significant repercussions. We provide another task termed "talk expectation," which assesses the likelihood of a report appearing from an online entertainment stream turning into gossip from now on. This is largely owing to the ease with which knowledge can be promptly disseminated to the general population, as well as the low cost of access. To overcome this challenge, several studies employ social media rumour detection. Four machine learning techniques—LR, SVM, RF, and XGBoost—were evaluated on various rumour debunking microblogs. In our technique for forecasting rumours, we mix content-based and novelty-based elements. In comparison to existing models, the suggested rumour prediction model outperforms them all. The SVM rumour Prediction Model technique was chosen because it enhanced accuracy by 89 percent, recall by 64 percent, and F-measure by 85 percent. The experiment findings revealed that the suggested approach will be beneficial for evading social problems produced by pieces of gossip in online entertainment.
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