A Multi-classification Division-aggregation Framework for Fake News Detection

Nowadays, as human activities are shifting to social media, fake news detection has been a crucial problem. Existing methods ignore the classification difference in online news and cannot take full advantage of multi-classification knowledges. For example, when coping with a post &#x201C;A mouse is frightened by a cat,&#x201D; a model that learns &#x201C;computer&#x201D; knowledge tends to misunderstand &#x201C;mouse&#x201D; and give a fake label, but a model that learns &#x201C;animal&#x201D; knowledge tends to give a true label. Therefore, this research proposes a multi-classification division-aggregation framework to detect fake news, named <italic>CKA</italic>, which innovatively learns classification knowledges during training stages and aggregates them during prediction stages. It consists of three main components: a news characterizer, an ensemble coordinator, and a truth predictor. The news characterizer is responsible for extracting news features and obtaining news classifications. Cooperating with the news characterizer, the ensemble coordinator generates classification-specifical models for the maximum reservation of classification knowledges during the training stage, where each classification-specifical model maximizes the detection performance of fake news on corresponding news classifications. Further, to aggregate the classification knowledges during the prediction stage, the truth predictor uses the truth discovery technology to aggregate the predictions from different classification-specifical models based on reliability evaluation of classification-specifical models. Extensive experiments prove that our proposed <italic>CKA</italic> outperforms state-of-the-art baselines in fake news detection.