BM5-SP-SC: A Dual Model Architecture for Contradiction Detection on Crowdfunding Projects

Despite the prevalence of scams in crowdfunding projects, currently, there is little research into the identification of fraudulent or infeasible crowdfunding projects. Since detecting fraudulent crowdfunding projects is challenging, most existing research on fake information has focused on detecting fake news or fake charity crowdfunding projects based on social media, but research on fraudulent or infeasible crowdfunding projects is very limited. Therefore, to solve this problem, we focus on how to detect fraudulent crowdfunding projects based on knowledge extraction and contradiction detection. We proposed a novel method called BM5-SP-SC (BERT-MT5-Sentence Pattern-Sentiment Classification). BM5 (BERT-MT5), which is built from a combination of a key-BERT and a fine-tuned MT5 transformers, was used to extract feature information from crowdfunding projects. We proposed a novel method for MT5 training to construct an adaptive BM5 model. The correct rate of keywords extracted by our novel adaptive BM5 model was up to 72.7%, the recall was 100%, and the F-measure was up to 84.19%. The minimum train loss of the BM5 model was up to 0.1342, and the evaluation loss achieved was 0.3064. The BLEU score of summary-to-keyword was 37.336. Moreover, we proposed an SP (Sentence Pattern) matching method to achieve knowledge extraction. Furthermore, SC (Sentiment Classification) was used to build a sentiment classifier thesaurus for identifying fraudulent and infeasible crowdfunding projects. Our proposed BM5-SP-SC achieved an overall accuracy of 85.26% in detecting fraudulent crowdfunding projects.