Online deception is disrupting our daily life, organizational process, and even national security. Existing deception detection approaches followed a traditional paradigm by using a set of cues as antecedents, and used a variety of data sets and common classification models to detect deception, which were demonstrated to be an accurate technique, but the previous results also showed the necessity to expand the deception feature set in order to improve the accuracy. In our study, we propose a novel feature selection method of the combination of CHI statistics and hypothesis testing, and achieve the accuracy level of 86% and F-measure of 0.84 by using the novel feature sets and SVM classification models, which exceeds the previous experiment results.

This paper presents a rule-based chunking approach. Rule-based method does well in analyzing the structure of natural language. In order to avoid the confliction of the rules, we extract a small scale chunking rule set for chunking first. Then we define more rules to check and correct the inconsistency phenomena. We also adopt man-machine interaction method to solve some special language phenomena. Experimental results show that our approach achieves high accuracy.

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