[Objective] This paper develops a new model to detect fraud in crowdfunding activities. [Methods] We extracted textual clues from the project description in three dimensions: cognitive load, narrative perspective, and emotional output. Then, we built and optimized ensemble models with resampling and threshold moving methods. [Results] The AUC values of the optimized models reached 0.8. The threshold moving method further improved the models’ performance, and the F1 scores improved by 0.279 on average, with a maximum improvement of 195%. [Limitations] The proposed models only use textual features from the project description and do not consider more dimensional features. [Conclusions] Ensemble model based on resampling and threshold moving methods can effectively identify fraudulent crowdfunding projects.
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