Abstract
The Internet is increasing with huge amount of textual data. The crimes also increased in the Internet along with textual data. The authorship analysis is one important area attracted by the several researchers to reduce the problems raised through the text in the Internet. Authorship verification is one type of authorship analysis which is used to verify an author by checking whether the textual document is written by the disputed author or not. The accuracy of authorship verification majorly depends on the features that are used for distinguishing the style of writing followed in the documents. In the previous works of authorship verification, the researchers proposed various types of stylistic features to distinguish the authors writing style. The researchers analyzed that the performance of authorship verification was poor when the stylistic features were used alone in the experiment. In this work, a new approach is proposed for authorship verification where the content-based features were used in the experiment. The term importance is computed by using term weight measure, and these term weights were used to calculate the document weight. The document weights of training document and document weights of test documents were compared to verify the test document. The proposed approach accuracy is good when compared with state-of-the-art existing approaches for authorship verification.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Raghunadha Reddy, T., Vishnu Vardhan, B., Vijayapal Reddy, P., A survey on author profiling techniques. Int. J. Appl. Eng. Res. 11(5), 3092–3102 (2016)
Zheng, R., Li, J., Chen, H., Huang, Z.: A framework for authorship identification of online messages: writing style features and classification techniques. J. Am. Soc. Inf. Sci. Technol. 57(3), 378–393 (2006)
Argamon, S., Koppel, M., Pennebaker, J.W., Schler, J.: Automatically profiling the author of an anonymous text. Commun. ACM 52(2), 119–123 (2009)
Bhanu Prasad, A., Rajeswari, S., Venkanna Babu, A., Raghunadha Reddy, T.: Author verification using rich set of linguistic features. Proc. Adv. Intell. Syst. Comput. 701, 197–203 (2018)
Koppel, M., Schler, J., Argamon, S.: Computational methods in authorship attribution. J. Am. Soc. Inf. Sci. Technol. 60(1), 9–26 (2009)
Raghunadha Reddy, T., Vishnu Vardhan, B., Vijayapal Reddy, P.: Author profile prediction using pivoted unique term normalization. Indian J. Sci. Technol. 9(46) (2016)
Feng, V.W., Hirst, G.: Authorship verification with entity coherence and other rich linguistic features. In: Proceedings of CLEF 2013 Evaluation Labs (2013)
Bobicev, V.B.: Authorship detection with PPM. In: Proceedings of CLEF 2013 Evaluation Labs (2013)
Vilariño, D., Pinto, D., Gómez, H., León, S., Castillo, E.: Lexical-syntactic and graph-based features for authorship verification. In: Proceedings of CLEF 2013 Evaluation Labs (2013)
van Dam, M.: A basic character N-gram approach to authorship verification. In: Proceedings of CLEF 2013 Evaluation Labs (2013)
Veenman, C.J., Li, Z.: Authorship verification with compression features. In: Proceedings of CLEF 2013 Evaluation Labs (2013)
Bartoli, A., Dagri, A., De Lorenzo, A., Medvet, E., Tarlao, F.: An author verification approach based on differential features. In: Proceedings of CLEF 2013 Evaluation Labs (2015)
Seidman, S.: Authorship verification using the impostors method. In: Proceedings of CLEF 2013 Evaluation Labs (2013)
Petmanson, T.: Authorship identification using correlations of frequent features. In: Proceedings of CLEF 2013 Evaluation Labs (2013)
Gollub, T., Potthast, M., Beyer, A., Busse, M., Pardo, F.M.R., Rosso, P., Stamatatos, E., Stein, B.: Recent trends in digital text forensics and its evaluation—plagiarism detection, author identification, and author profiling. In: Proceedings of the 4th International Conference of the CLEF Initiative, pp. 282–302 (2013)
Raghunadha Reddy, T., Vishnu Vardhan, B., Vijayapal Reddy, P.: Profile specific document weighted approach using a new term weighting measure for author profiling. Int. J. Intell. Eng. Syst. 9(4), 136–146 (2016)
Sreenivas, M., Raghunadha Reddy, T., Vishnu Vardhan, B.: A novel document representation approach for authorship attribution. Int. J. Intell. Eng. Syst. 11(3):261–270 (2018)
Swathi, C., Karunakar, K., Archana, G., Raghunadha Reddy, T.: A new term weight measure for gender prediction in author profiling. In: Proceedings in Advances in Intelligent Systems and Computing, vol. 695, pp. 11–18 (2018)
Dennis, S.F.: The design and testing of a fully automated indexing-searching system for documents consisting of expository text. In: Schecter, G. (ed.) Informational Retrieval: A Critical Review, pp. 67–94. Thompson Book Company, Washington D.C. (1967)
Raghunadha Reddy, T., Vishnu Vardhan, B., Vijayapal Reddy, P.: A document weighted approach for gender and age prediction. Int. J. Eng. Trans. B: Appl. 30(5), 647–653 (2017)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Buddha Reddy, P., Murali Mohan, T., Vamsi Krishna Raja, P., Raghunadha Reddy, T. (2020). A Novel Approach for Authorship Verification. In: Raju, K.S., Senkerik, R., Lanka, S.P., Rajagopal, V. (eds) Data Engineering and Communication Technology. Advances in Intelligent Systems and Computing, vol 1079. Springer, Singapore. https://doi.org/10.1007/978-981-15-1097-7_37
Download citation
DOI: https://doi.org/10.1007/978-981-15-1097-7_37
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-15-1096-0
Online ISBN: 978-981-15-1097-7
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)