Annotated Arabic Extremism Tweets

We present an Arabic Twitter dataset for online extremism detection consisting of 89K tweets with associated metadata. The dataset was manually annotated by three experts and achieved a Gwet’s AC1 score of 0.6, indicating substantial inter-annotator agreement. We performed further analysis of the tweet metadata to identify important features. For the extremism dataset, there were 89,816 tweets in total published by 52,929 unique users. Moreover, 50,279 tweets (56%) from 22,858 unique users were labeled as extremist, whereas 39,537 tweets (44%) from 30,911 unique users were labeled as non-extremist. We applied Shannon’s entropy measure to check the dataset’s balance, deriving a result of 0.98, which indicates that the dataset is well balanced.