Multi-modal detection of cyberbullying on Twitter

Jiabao Qiu, Melody Moh, Teng-Sheng Moh

202215 citationsDOI

Abstract

Cyberbullying detection is one of the trending topics of research in recent years, due to the popularity of social media and the lack of limitations about using electronic communications. Detection of cyberbullying may prevent some bullying behaviors online. This paper introduces a Multi-modal system that makes use of Convolutional Neural Network (CNN), Tensor Fusion Network, VGG-19 Network, and Multi-Layer Perceptron model, for the purpose of cyberbullying detection. This system can not only analyze the messages sent but also the extra information related to the messages (meta-information) and the images contained in the messages. The proposed system is trained and tested on Twitter datasets, achieving accuracy scores of 93%, which is 4% higher than scores of the benchmark text-only model using the same dataset and 6.6% higher than previous work. With the results, we believe that the proposed system performs well and it will provide new ideas for future works.

Topics & Concepts

Computer sciencePopularityBenchmark (surveying)Convolutional neural networkSocial mediaPerceptronModalArtificial intelligenceMachine learningMultilayer perceptronArtificial neural networkData miningInformation retrievalWorld Wide WebPsychologyPolymer chemistryGeographySocial psychologyChemistryGeodesyHate Speech and Cyberbullying DetectionBullying, Victimization, and AggressionAdvanced Malware Detection Techniques