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 Applications, leverages 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.
The long-awaited journal review being conducted by the Australian Business Deans Council (ABDC) has been released and there are a number of news items that relate to faculty in the Turner College. One of these is the ABDC's decision to now include Compensation and Benefits Review in its journal rankings. This is big news for the Turner College as its editor, Phil Bryant , is a professor of management in the Turner College. The ABDC is proposing that the journal enter its system for the first time as a C-rated journal. Acting Turner College Dean Tesa Leonce sits on the journal's editorial board, while Turner College management professor Mark James has guest-edited an issue of the journal. Published by SAGE, Compensation & Benefits Review is the leading journal for senior executives and professionals who design, implement, evaluate and communicate compensation and benefits policies and programs. The journal supports compensation and benefits specialists and academic ex...

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