{"title":"Authorship Attribution of Russian Forum Posts with Different Types of N-gram Features","authors":"T. Litvinova, O. Litvinova, Polina Panicheva","doi":"10.1145/3342827.3342834","DOIUrl":null,"url":null,"abstract":"Authorship attribution is an important field in online security. Recently there have been numerous successful works in authorship attribution in various European languages. Character n-grams are reported to be the best choice in authorship attribution, as they encode both style and content information. We evaluate different types of character n-gram features in an authorship attribution task in a real-world noisy dataset of Russian forum posts. We also supplement them with a number of new simple n-gram features capturing syntactic and discourse patterns. We perform authorship attribution in a single-topic and a cross-topic setting, as the research question is whether character n-grams capture both style and content information. Our results show that character n-grams are indeed very successful in Russian forum post authorship attribution. However, there is no clear distinction of style and content n-grams, as the same types of n-grams work well for both single-topic and cross-topic settings. In our experiments the generalized simple n-gram features which reveals syntactic and discourse patterns were proved to be also very important in authorship attribution of short informal Russian texts. They represent a different kind of authorship information and are a successful addition to the character n-grams in authorship attribution of forum texts in the Russian language.","PeriodicalId":254461,"journal":{"name":"Proceedings of the 2019 3rd International Conference on Natural Language Processing and Information Retrieval","volume":"13 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2019-06-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"4","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings of the 2019 3rd International Conference on Natural Language Processing and Information Retrieval","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/3342827.3342834","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 4
Abstract
Authorship attribution is an important field in online security. Recently there have been numerous successful works in authorship attribution in various European languages. Character n-grams are reported to be the best choice in authorship attribution, as they encode both style and content information. We evaluate different types of character n-gram features in an authorship attribution task in a real-world noisy dataset of Russian forum posts. We also supplement them with a number of new simple n-gram features capturing syntactic and discourse patterns. We perform authorship attribution in a single-topic and a cross-topic setting, as the research question is whether character n-grams capture both style and content information. Our results show that character n-grams are indeed very successful in Russian forum post authorship attribution. However, there is no clear distinction of style and content n-grams, as the same types of n-grams work well for both single-topic and cross-topic settings. In our experiments the generalized simple n-gram features which reveals syntactic and discourse patterns were proved to be also very important in authorship attribution of short informal Russian texts. They represent a different kind of authorship information and are a successful addition to the character n-grams in authorship attribution of forum texts in the Russian language.