'twazn me!!!;(' Automatic Authorship Analysis of Micro-Blogging Messages

• Type: Article in International Conference Proceedings Book
• Date: 2011

Authors

sousa silva, r (Author) Other; laboreiro, g (Author) Other; sarmento, l (Author) Other; grant, t (Author) Other; oliveira, e (Author) FEUP; maia, b (Author) Other

Conference proceedings International

Title: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Pages: 161-168

Natural Language Processing and Information Systems - 16th International Conference on Applications of Natural Language to Information Systems, NLDB 2011, Alicante, Spain, June 28-30, 2011. Proceedings

Indexing

ISI Web of Knowledge - 11 Citations

Scopus - 2 Citations

Other information

Authenticus ID: P-007-YW0

DOI: 10.1007/978-3-642-22327-3_16

Abstract (EN): 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.

Language: English

Type (Professor's evaluation): Scientific

No. of pages: 8