[Objective] This paper aims to identify authors with features extracted from non-standard online texts. [Methods] First, we used the non-standard text similarity M defined by the Jaccard coefficient. Second, we adopted the frequency of non-standard text from the corpus. [Results] The recognition accuracy of the two features were 85.1% and 80.2%. Adding the two features to the traditional recognition mechanism, the precision of the system increased by 5.8% and 4%, respectively. [Limitations] We did not study the online texts from the syntactic and structure levels. [Conclusions] The proposed method could effectively extract the non-standard text features and then improve the accuracy of author identification.
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