Name: Author Attribution Tweets
Description:  consists of 71,397 tweets for 45 authors for MSA collected from twitter. 
Provider: Birzeit University
Paper Title: Authorship Attribution of Modern Standard Arabic Short Texts
Tasks: authorship attribution
Venue Name: nan
Authors: YARA ABUHAMMAD, YARA ADDABE, NATALY AYYAD, ADNAN YAHYA
Abstract: 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