2018/08/02 by Michał Ptaszyński, Gniewosz Leliwa, Ptaszyński, Michał +5
Computer Science · Engineering · #Advanced Malware Detection Techniques #Computation and Language (cs.CL) #Computer science #Data science #Engineering #Engineering ethics #Engineering management #FOS: Computer and information sciences #Hate Speech and Cyberbullying Detection #Network Security and Intrusion Detection #cs.CL
paper · pdf · doi:10.48550/arxiv.1808.00926
arxiv created 2018/08/02 · openalex publication_date 2018/08/02 · arxiv updated 2018/08/03 · openalex created_date 2018/08/22 · openalex updated_date 2026/07/28
The research described in this paper concerns automatic cyberbullying detection in social media. There are two goals to achieve: building a gold standard cyberbullying detection dataset and measuring the performance of the Samurai cyberbullying detection system. The Formspring dataset provided in a Kaggle competition was re-annotated as a part of the research. The annotation procedure is described in detail and, unlike many other recent data annotation initiatives, does not use Mechanical Turk for finding people willing to perform the annotation. The new annotation compared to the old one seems to be more coherent since all tested cyberbullying detection system performed better on the former. The performance of the Samurai system is compared with 5 commercial systems and one well-known machine learning algorithm, used for classifying textual content, namely Fasttext. It turns out that Samurai scores the best in all measures (accuracy, precision and recall), while Fasttext is the second-best performing algorithm.