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Academic Journal
Business Analytics

“Business Performance Prediction in Location-based Social Commerce”

Social commerce and location-based services provide a data platform for coexisting and competing businesses in geographical neighborhoods. Our research is aimed at mining data from such platforms to gain valuable insights for better support to strategic and operational business decisions. We develop a computational framework for predicting business performance that takes into account both intrinsic (e.g., attributes) and extrinsic (e.g., competitions) factors. Our experiments on synthetic and real datasets demonstrated superiority of a hybrid prediction model that adopts both link-based and context-based assumptions.
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Academic Journal
Business Analytics

“Combating Misinformation by Sharing the Truth: a Study on the Spread of Fact-Checks on Social Media”

Misinformation on social media has become a horrendous problem in our society. Fact-checks on information often fall behind the diffusion of misinformation, which can lead to negative impacts on society. This research studies how different factors may affect the spread of fact-checks over the internet. We collected a dataset of fact-checks in a six-month period and analyzed how they spread on Twitter. The spread of fact-checks is measured by the total retweet count. The factors/variables include the truthfulness rating, topic of information, source credibility, etc. The research identifies truthfulness rating as a significant factor: conclusive fact-checks (either true or false) tend to be shared more than others. In addition, the source credibility, political leaning, and the sharing count also affect the spread of fact-checks. The findings of this research provide practical insights into accelerating the spread of the truth in the battle against misinformation online.
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