Combating Fake News on Twitter: A Machine Learning Approach for Detection and Classification of Fake Tweets
Keywords:
BERT, Twitter, Fakes News detectionAbstract
The proliferation of false information across social media has emerged as a significant challenge in recent times. Within this landscape, the platform formerly known as Twitter, now referred to as X, stands as a widely used medium for disseminating news and data, rendering it especially susceptible to the proliferation of misleading or fabricated content. In response to this issue, this research paper presents an innovative strategy hinging on BERT (Bidirectional Encoder Representations from Transformers) technology to address the propagation of deceptive news on Twitter. By means of meticulously acquired Twitter data pertaining to news items, the dataset undergoes careful pre-processing to render it compatible with the BERT model. This data is subsequently partitioned into training and validation subsets. The BERT model is then meticulously fine-tuned via a feed-forward neural network and optimized leveraging the Adam optimizer. Throughout the training process, vigilance is maintained over loss values, augmented by techniques such as dropout and regularization to enhance generalization capabilities. The final model selection is dictated by the validation loss metric. Harnessing the capabilities of advanced natural language processing methods, this endeavour contributes to the evolution of robust instruments for unearthing misleading content, fostering a more dependable informational milieu across social media domains with an accuracy of 77.29%. The study's conclusions carry implications for elevating the integrity and credibility of news propagation within the digital epoch
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