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TSYS School Computer Scientist Develops Computational Process for Detecting Fake News

The notion of "fake news" has risen in prominence in the U.S. political arena over the past decade, largely due to the proliferation of social media platforms. A recently published study by TSYS School computer scientist Yi Zhou and colleagues from Yangzhou University and Auburn University proposes a computational approach for detecting fake news in a real time. Their method, described in the 2023 volume of Expert Systems with Applicationsleverages event and topic extraction techniques in tandem with a topic merging mechanism to process news data and reduce the number of topics. "Our approach incorporates a two-stage procedure to optimize the cold-start ratio between initial and other data batches to improve memory management during the processing of streaming data," Zhou explained. Computational experiments in different system settings for benchmarking the proposed methodology demonstrate that it takes less time in detecting fake news by reducing the number of topics by 19.8% and the number of data clusters by 26.9%, both compared to other baselines.

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