Understanding the Radical Mind: Identifying Signals to Detect Extremist Content on Twitter
TABLE I: Exp 1: Evaluation metrics across all feature groups
['Features', 'AC', 'Precision', 'Recall', 'F-measure']
Exp 1: The table shows the average accuracy, precision, recall and f-measure scores obtained from each feature category (FT, FP, FB) and their combination (FAll). We also compared the two textual models, and find that results obtained from using word embedding outperforms the use of n-grams tf-idf scores. This confirms that contextual information is important in detecting radicalization activities. Furthermore, our model performed best using the FP features across all metrics. This means that the model is able to distinguish between both radical and non-radical with high confidence using only FP.