Predicting Twitter Posts from Fake Accounts Using XGBoost Model

In recent years, the population of social media has raised an increasing problem of fake accounts. The main objective of this study is to predict whether the content of a tweet originates from a fake account or not. To address this issue, we analyze the tweets of real users and fake accounts on Twitter to identify potential differences between these two types of accounts. First, we collected tweets from both real users and fake accounts and preprocess the data. And then, we adopted preprocessing to transform these tweets into vector and employ XGBoost for learning to discriminate between tweets from real users and fake accounts. Experimental results demonstrate that our approach achieves an accuracy of 88%. Therefore, the results show that our method can identify lexical differences and propose an effective solution for detecting fake accounts.