Abstract:

The detection of fake news has become increasingly important in today’s fast-paced, information-driven society, where false information can easily spread across various digital platforms. This study delves into how data science techniques, specifically the Long Short-Term Memory (LSTM) algorithm, can be used to identify fake news. LSTM, a type of recurrent neural network (RNN), is particularly effective at capturing long-term dependencies in textual data, making it ideal for this task. By training an LSTM model on a labeled dataset of news articles, the system learns to recognize patterns and subtle language cues that signal misinformation. The process includes key preprocessing steps like tokenization, word embedding, and text vectorization, which help optimize the model’s performance. This approach takes full advantage of LSTM’s sequential processing capabilities to improve the accuracy and reliability of distinguishing fake news from credible sources. Ultimately, the goal is to create a scalable, effective solution to combat the spread of false information online.

Description:

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