The spread of false content on microblogging platforms has created information security threats for users and platforms alike. The confusion caused by false content complicates feature selection during credibility evaluation. To solve this problem, a collaborative key point-based content credibility evaluation model, CECKP, is proposed in this paper. The model obtains the key points of the microblog text from the word level to the sentence level, then evaluates the credibility according to the semantics of the key points. In addition, a rumor lexicon constructed collaboratively during word-level coding strengthens the semantics of related words and solves the feature selection problem when using deep learning methods for content credibility evaluation. Experimental results show that, compared with the Att-BiLSTM model, the F1 score of the proposed model increases by 3.83% and 3.8% when the evaluation results are true and false respectively. The proposed model accordingly improves the performance of content credibility evaluation based on optimized feature selection.
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