Author Attribution Tweets
 consists of 71,397 tweets for 45 authors for MSA collected from twitter. 
Birzeit University
Authorship Attribution of Modern Standard Arabic Short Texts
authorship attribution
nan
YARA ABUHAMMAD, YARA ADDABE, NATALY AYYAD, ADNAN YAHYA
Text data, including short texts, constitute a major share of web content. The availability of this data to billions of users triggers
frequent plagiarism attacks. Authorship Attribution (AA) seeks to identify the most probable author of a given text based on similarity
to the writing style of potential authors. In this paper, we approach AA as a writing style profile generation process, where we group
text instances for each author into a single profile. We use Twitter as the source for our short Modern Standard Arabic (MSA) texts.
Numerous experiments with various training approaches, tools and features allowed us to settle on a text representation method that
relies on text concatenation of Arabic tweets to form chunks, which are then duplicated to reach a precalculated length. These chunks
are used to train machine learning models for our 45 author profiles. This allowed us to achieve accuracies up to 99%, which compares
favorably with the best results reported in the literature