首页|Researcher at Hong Kong Polytechnic University Describes Research in Machine Lea rning (Augmenting fake content detection in online platforms: A domain adaptive transfer learning via adversarial training approach)
Researcher at Hong Kong Polytechnic University Describes Research in Machine Lea rning (Augmenting fake content detection in online platforms: A domain adaptive transfer learning via adversarial training approach)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – Research findings on artificial intell igence are discussed in a new report. According to news reporting originating fr om Hong Kong Polytechnic University by NewsRx correspondents, research stated, “ Online platforms are experimenting with interventions such as content screening to moderate the effects of fake, biased, and incensing content.” The news journalists obtained a quote from the research from Hong Kong Polytechn ic University: “Yet, online platforms face an operational challenge in implement ing machine learning algorithms for managing online content due to the labeling problem, where labeled data used for model training are limited and costly to ob tain. To address this issue, we propose a domain adaptive transfer learning via adversarial training approach to augment fake content detection with collective human intelligence. We first start with a source domain dataset containing decep tive and trustworthy general news constructed from a large collection of labeled news sources based on human judgments and opinions. We then extract discriminat ing linguistic features commonly found in source domain news using advanced deep learning models. We transfer these features associated with the source domain t o augment fake content detection in three target domains: political news, financ ial news, and online reviews. We show that domain invariant linguistic features learned from a source domain with abundant labeled examples can effectively impr ove fake content detection in a target domain with very few or highly unbalanced labeled data.”
Hong Kong Polytechnic UniversityCyborg sEmerging TechnologiesMachine Learning