Name
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. |
Year | Venue | Keywords |
|---|---|---|
2011 | NLDB | proposed marker,svm classifier,stylistic marker,idiosyncratic editing option,stylistic feature,twitter message,low-level feature,automatic authorship analysis,stylometric model,different combination,micro-blogging message |
Field | DocType | Volume |
Social media,Computer science,Microblogging,Support vector machine,Natural language processing,Artificial intelligence,Punctuation | Conference | 6716 |
ISSN | Citations | PageRank |
0302-9743 | 21 | 1.13 |
References | Authors | |
11 | 6 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Rui Sousa Silva | 1 | 21 | 1.13 |
| Gustavo Laboreiro | 2 | 58 | 4.51 |
| Luís Sarmento | 3 | 377 | 31.16 |
| Tim Grant | 4 | 23 | 1.58 |
| Eugénio Oliveira | 5 | 974 | 111.00 |
| Belinda Maia | 6 | 24 | 2.75 |