The week's pick
Reseach Article
Approaches of Authorship Verification
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 175 - Number 18 |
| Year of Publication: 2020 |
| Authors: Shaimaa Ayman, Mohamed Eisa, Fifi Farouk |
10.5120/ijca2020920692
|
Shaimaa Ayman, Mohamed Eisa, Fifi Farouk . Approaches of Authorship Verification. International Journal of Computer Applications. 175, 18 ( Sep 2020), 11-18. DOI=10.5120/ijca2020920692
Abstract
Nowadays, there are massive amounts of texts in digital form in digital libraries, online journalism, and social networks; for example, Twitter is estimated half a billion tweets are sent out each day. The expanded usage of Online Social Network (OSN) has become necessary to appear to grow of Authorship Verification (AV), OSN is the environment in which users can connect with other users to discuss ideas of any topics then expansion data and information. AV considered as a resource of researches and information in different ways, as is the case Sentiment Analysis (SA). Information that gained from Twitter and Facebook or any other OSN is considered valuable in some areas such as public opinion organizations and online marketing. The crimes also increased over on the internet with textual data. To reduce the problems raised on text through the internet, the researchers have attracted to authorship analysis which is one of the important areas. AV is a type of authorship analysis that is used to verify an author by checking whether the text document is written by the disputed author. The accuracy of AV depends primarily on the features used to distinguish the writing style of documents. In previous works of AV, researchers proposed several types of stylistic features for distinguishing the writing style of the authors. The researchers analyzed that the AV performance was weak when used stylistic features alone in the experiments. Therefore, researchers resorted to more accurate methods that compute the features by using the weight measures. The weight measures calculate the document weights of training, and test documents. Then, the competition between the weights of training document and the weights of test document were implemented; to verify the author of the document.
References
- Da Silva, N.F., Hruschka, E.R., and Hruschka Jr, E.R. 2014 Tweet sentiment analysis with classifier ensembles. Decision Support Systems.
- Juola, P., Jr., J.I.N., Stolerman, A., Ryan, M.V., Brennan, P., and Greenstadt, R. 2013 A Dataset for Active Linguistic Authentication, Book A Dataset for Active Linguistic Authentication.
- Halteren, H.v. 2004 Linguistic profiling for authorship recognition and verification, Book Linguistic profiling for authorship recognition and verification.
- Koppel, M., and Schler, J. 2004 Authorship verification as a one-class classification problem. Authorship verification as a one-class classification problem.
- Stein, B., Lipka, N., and zu Eissen, S.M. 2008 Meta analysis within authorship verification, Book Meta analysis within authorship verification.
- Kestemont, M., Luyckx, K., Daelemans, W., and Crombez, T. 2012 Cross-genre authorship verification using unmasking. English Studies.
- Mosteller, F., and Wallace, D.L. 1963 Inference in an authorship problem: A comparative study of discrimination methods applied to the authorship of the disputed Federalist Papers. Journal of the American Statistical Association.
- Carbonell, J.G., Michalski, R.S., and Mitchell, T.M. 1983 An overview of machine learning, Machine learning (Elsevier).
- Stamatatos, E. 2009 A survey of modern authorship attribution methods, Journal of the American Society for information Science and Technology.
- Potha, N., and Stamatatos, E. 2014 A profile-based method for authorship verification, Book A profile-based method for authorship verification (Springer).
- Kešelj, V., Peng, F., Cercone, N., and Thomas, C. 2003 N-gram-based author profiles for authorship attribution, Book N-gram-based author profiles for authorship attribution.
- Ma, J., Xue, B., and Zhang, M. 2016 A profile-based authorship attribution approach to forensic identification in Chinese online messages, Book A profile-based authorship attribution approach to forensic identification in Chinese online messages (Springer).
- Mosteller, F., and Wallace, D. 1964 Inference and Disputed Authorship The Federalist Reading, Massachusetts: Addison-Wesley.
- Argamon, S., Šaric, M., and Stein, S.S. 2003 Style mining of electronic messages for multiple authorship discrimination: first results, Book Style mining of electronic messages for multiple authorship discrimination: first results.
- Koppel, M., and Schler, J. 2003 Exploiting stylistic idiosyncrasies for authorship attribution, Book Exploiting stylistic idiosyncrasies for authorship attribution.
- De Vel, O., Anderson, A., Corney, M., and Mohay, G. 2001 Mining e-mail content for author identification forensics, ACM Sigmod Record.
- Zheng, R., Li, J., Chen, H., and Huang, Z. 2006 A framework for authorship identification of online messages: Writing-style features and classification techniques, Journal of the American society for information science and technology.
- Jankowska, M., Milios, E., and Keselj, V. 2014 Author verification using common n-gram profiles of text documents, Book Author verification using common n-gram profiles of text documents.
- Li, Z. 2013 An Exploratory Study on Authorship Verification Models for Forensic Purpose.
- Brocardo, M.L., Traore, I., Saad, S., and Woungang, I. 2013 Authorship verification for short messages using stylometry, Book Authorship verification for short messages using stylometry (IEEE).
- Halvani, O., Steinebach, M., and Zimmermann, R. 2013 Authorship verification via k-nearest neighbor estimation, Notebook PAN at CLEF.
- Koppel, M., Schler, J., and Bonchek-Dokow, E. 2007 Measuring differentiability: Unmasking pseudonymous authors, Journal of Machine Learning Research.
- Nirk, S. 2016 ship Veri’, Procedia Computer Science.
- Boenninghoff, B., Hessler, S., Kolossa, D., and Nickel, R.M. 2019 Explainable authorship verification in social media via attention-based similarity learning, Book Explainable authorship verification in social media via attention-based similarity learning (IEEE).
- Lambers, M., and Veenman, C.J. 2009 Forensic authorship attribution using compression distances to prototypes, Book Forensic authorship attribution using compression distances to prototypes (Springer.).
- Loper, E., and Bird, S. 2002 NLTK: the natural language toolkit.
- Chen, X., Hao, P., Chandramouli, R., and Subbalakshmi, K. 2011 Authorship similarity detection from email messages, Book Authorship similarity detection from email messages (Springer).
- Vilariño, D., Pinto, D., Gómez, H., León, S., and Castillo, E. 2013 Lexical-syntactic and graph-based features for authorship verification, Book Lexical-syntactic and graph-based features for authorship verification.
- Iqbal, F., Khan, L.A., Fung, B.C., and Debbabi, M. 2010 E-mail authorship verification for forensic investigation, Book E-mail authorship verification for forensic investigation.
- Hoover, D.L. 2003 Another perspective on vocabulary richness, Computers and the Humanities.
- Stamatatos, E. 2017 Authorship attribution using text distortion, Book Authorship attribution using text distortion.
- Grieve, J. 2007 Quantitative authorship attribution: An evaluation of techniques, Literary and linguistic computing.
- Koppel, M., Schler, J., and Argamon, S. 2009 Computational methods in authorship attribution, Journal of the American Society for information Science and Technology.
- Tanguy, L., Urieli, A., Calderone, B., Hathout, N., and Sajous, F. 2011 A multitude of linguistically-rich features for authorship attribution, Book A multitude of linguistically-rich features for authorship attribution.
- Miller, G.A. 1998 WordNet: An electronic lexical database, (MIT press).
- Argamon, S., Whitelaw, C., Chase, P., Hota, S.R., Garg, N., and Levitan, S. 2007 Stylistic text classification using functional lexical features, Journal of the American Society for Information Science and Technology.
- Halliday, M.A.K., and Matthiessen, C.M. 2013 Halliday's introduction to functional grammar, (Routledge).
- McCarthy, D. 2006 Relating WordNet senses for word sense disambiguation, Book Relating WordNet senses for word sense disambiguation.
- Zhang, D., and Lee, W.S. 2006 Extracting key-substring-group features for text classification, Book Extracting key-substring-group features for text classification.
- Marton, Y., Wu, N., and Hellerstein, L. 2005 On compression-based text classification, Book On compression-based text classification, (Springer)
- Keogh, E., Lonardi, S., and Ratanamahatana, C.A. 2004 Towards parameter-free data mining, Book Towards parameter-free data mining.
- Li, M., Chen, X., Li, X., Ma, B., and Vitányi, P.M. 2004 The similarity metric, IEEE transactions on Information Theory.
- Cilibrasi, R., and Vitányi, P.M. 2005 Clustering by compression, IEEE Transactions on Information theory.
- Li, M., Badger, J.H., Chen, X., Kwong, S., Kearney, P., and Zhang, H. 2001 An information-based sequence distance and its application to whole mitochondrial genome phylogeny, Bioinformatics.
- Chen, X., Francia, B., Li, M., Mckinnon, B., and Seker, A. 2004 Shared information and program plagiarism detection, IEEE Transactions on Information Theory.
- Sculley, D., and Brodley, C.E. 2006 Compression and machine learning: A new perspective on feature space vectors, Book Compression and machine learning: A new perspective on feature space vectors, (IEEE).
- Forman, G. 2003 An extensive empirical study of feature selection metrics for text classification, Journal of machine learning research.
- Sebastiani, F. 2002 Machine learning in automated text categorization, ACM computing surveys (CSUR).
- Olson, D.L., and Delen, D. 2008 Advanced data mining techniques, Springer Science & Business Media.
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