Abstract
In this paper we propose a set of stylistic markers for automatically attributing authorship to micro-blogging messages. The proposed markers include highly personal and idiosyncratic editing options, such as ‘emoticons’, interjections, punctuation, abbreviations and other low-level features. We evaluate the ability of these features to help discriminate the authorship of Twitter messages among three authors. For that purpose, we train SVM classifiers to learn stylometric models for each author based on different combinations of the groups of stylistic features that we propose. Results show a relatively good-performance in attributing authorship of micro-blogging messages (F = 0.63) using this set of features, even when training the classifiers with as few as 60 examples from each author (F = 0.54). Additionally, we conclude that emoticons are the most discriminating features in these groups.
Access provided by Autonomous University of Puebla. Download to read the full chapter text
Chapter PDF
Similar content being viewed by others
Keywords
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
References
Grant, T.: Txt 4n6: Idiolect free authorship analysis. In: Coulthard, M., Johnson, A. (eds.) Routledge Handbook of Forensic Linguistics. Routledge, New York (2010)
de Vel, O., Anderson, A., Corney, M., Mohay, G.: Mining e-mail content for author identification forensics, vol. 30, pp. 55–64. ACM, New York (2001)
Park, T., Li, J., Zhao, H., Chau, M.: Analyzing writing styles of bloggers with different opinions. In: Proceedings of the 19th Annual Workshop on Information Technologies and Systems (WITS 2009), Phoenix, Arizona, USA, December 14-15 (2009)
Goswami, S., Sarkar, S., Rustagi, M.: Stylometric analysis of bloggers’ age and gender. In: International AAAI Conference on Weblogs and Social Media (2009)
Koppel, M., Schler, J., Argamon, S.: Computational methods in authorship attribution. Journal of the American Society for Information Science and Technology 60(1), 9–26 (2009)
Jindal, N., Liu, B.: Opinion spam and analysis. In: WSDM 2008: Proceedings of the International Conference on Web Search and Web Data Mining, pp. 219–230. ACM, New York (2008)
Pavelac, D., Justino, E., Olivera, L.S.: Author identification using stylometric features. Intelligencia Artificial,Revista Iberoamericana de IA 11(36), 59–66 (2007)
Sousa-Silva, R., Sarmento, L., Grant, T., Oliveira, E.C., Maia, B.: Comparing sentence-level features for authorship analysis in portuguese. In: PROPOR, pp. 51–54 (2010)
Hirst, G., Feiguina, O.: Bigrams of syntactic labels for authorship discrimination of short texts. Lit. Linguist. Computing 22(4), 405–417 (2007)
Abbasi, A., Chen, H.: Writeprints: A stylometric approach to identity-level identification and similarity detection in cyberspace. ACM Trans. Inf. Syst. 26(2), 1–29 (2008)
Layton, R., Watters, P., Dazeley, R.: Authorship attribution for twitter in 140 characters or less. In: Workshop Cybercrime and Trustworthy Computing, pp. 1–8 (2010)
Raghavan, S., Kovashka, A., Mooney, R.: Authorship attribution using probabilistic context-free grammars, pp. 38–42 (2010)
Eagleson, R.: Forensic analysis of personal written texts: a case study. In: Gibbons, J. (ed.) Forensic Linguistics: An Introduction to Language in the Justice System, pp. 362–373. Longman, Harlow (1994)
Joachims, T.: Text categorization with support vector machines: learning with many relevant features. In: Nédellec, C., Rouveirol, C. (eds.) ECML 1998. LNCS, vol. 1398, pp. 137–142. Springer, Heidelberg (1998)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2011 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Sousa Silva, R., Laboreiro, G., Sarmento, L., Grant, T., Oliveira, E., Maia, B. (2011). ‘twazn me!!! ;(’ Automatic Authorship Analysis of Micro-Blogging Messages. In: Muñoz, R., Montoyo, A., Métais, E. (eds) Natural Language Processing and Information Systems. NLDB 2011. Lecture Notes in Computer Science, vol 6716. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-22327-3_16
Download citation
DOI: https://doi.org/10.1007/978-3-642-22327-3_16
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-22326-6
Online ISBN: 978-3-642-22327-3
eBook Packages: Computer ScienceComputer Science (R0)