[Objective] This paper proposes an algorithm with BiGRU and attention mechanism based on the Chinese BERT model,aiming to identify the clickbait from online news titles. [Methods] First, we pre-trained our model as a text encoder using the Chinese BERT. Then, we extracted text features through the fusion attention mechanism, and used BiGRU to model news titles and contents. Finally, we identified clickbait based on their semantic correlation. [Results] This method addressed the issues of complex feature engineering and secondary error amplification in the text similarity calculation. The recognition accuracy rate was 81%, and a browser plug-in was developed to detect clickbait. [Limitations] The proposed model only examined news titles and contents, and did not include pageviews, likes, and comments in the calculation. [Conclusions] Our new method, whose recall is 4% higher than those of the existing methods, could effectively identify the clickbait from online news.
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