DeepTweet: Leveraging Transformer-based Models for Enhanced Fake News Detection in Twitter Sentiment Analysis

Abstract

With the increase in the popularity of the social media sites, there arises lot of adverse effects including the spread of fake news. This research study proposes a novel algorithm, called DeepTweet, which leverages transformer-based models for enhanced fake news detection in Twitter sentiment analysis. The aim is to improve the accuracy and efficiency of identifying fake news in real-time by exploiting the contextual information and semantic representations captured by the transformer-based models. The proposed DeepTweet algorithm utilizes a pre-trained transformer model, such as BERT or GPT, to encode the textual content of tweets and extract rich contextual embeddings. These embeddings capture both local and global dependencies, enabling a more comprehensive understanding of the sentiment expressed in a tweet. Furthermore, a novel attention mechanism tailored for fake news detection is introduced to focus on key textual features that are indicative of potential misinformation. To assess the efficiency of DeepTweet, extensive simulations using a large dataset of labelled tweets containing both genuine and fake news are done. Further, this study compares the performance of DeepTweet with existing algorithms, including LSTM, CNN, and traditional machine learning approaches. Various simulation metric measures such as accuracy, precision, recall, and F1 score are employed to analyze the algorithm's performance. The simulation grades demonstrate that DeepTweet outperforms the prevailing algorithms by means of accuracy and overall detection performance. The proposed attention mechanism proves to be particularly effective in identifying subtle linguistic cues and deceptive patterns associated with fake news. The enhanced contextual embeddings captured by the transformer-based models contribute to improved sentiment analysis and subsequently lead to more accurate fake news detection.