Rangsipan MarukatatRobroo SomkiadcharoenRatthanan NalintasnaiTappasarn AramboonpongMahidol University2018-11-092018-11-092014-01-01ICISA 2014 - 2014 5th International Conference on Information Science and Applications. (2014)2-s2.0-84904490792https://repository.li.mahidol.ac.th/handle/123456789/33691This paper presents a framework to identify the authors of Thai online messages. The identification is based on 53 writing attributes and the selected algorithms are support vector machine (SVM) and C4.5 decision tree. Experimental results indicate that the overall accuracies achieved by the SVM and the C4.5 were 79% and 75%, respectively. This difference was not statistically significant (at 95% confidence interval). As for the performance of identifying individual authors, in some cases the SVM was clearly better than the C4.5. But there were also other cases where both of them could not distinguish one author from another. © 2014 IEEE.Mahidol UniversityComputer ScienceConference PaperSCOPUS10.1109/ICISA.2014.6847369