Understanding the Radical Mind: Identifying Signals to Detect Extremist Content on Twitter
TABLE II: Exp 2: Evaluation metrics across all feature groups
['Features', 'AC', 'Precision', 'Recall', 'F-measure']
Exp2: In this experiment, we tested the performance of our classifier in distinguishing between radical and normal tweets that discusses ISIS-related topics. Although this task is more challenging given the similarity of the topic discussed in the two classes, we find that the model still achieves high performance. The FT feature group obtains 80% accuracy, and 91%, 100% for FB and FP feature groups, respectively. The results are consistent with the ones obtained from the first experiment with the features from FP group contributing to the high accuracy of the model. The area under the Receiver Operator Characteristic (ROC) curve, which measures accuracy based on TP, and FP rates, is shown in Fig.