A Multi-Modal Approach for the Detection of Account Anonymity on Social Media Platforms.

Anonymous users on Twitter are highly related to network hazards such as cyberbullying, fraud, rumor spread, etc. Given the inefficient and expensive manual review approach, an automated method to predict account anonymity is urgently needed. However, there are three challenges when employing deep learning algorithms to detect account anonymity: the lack of open-source public datasets, tedious manual collection and labeling for training data, and abundant types of information that the existing single-modal methods are not competent for. Our approach includes an automated labeling system for data collection and a multi-modal method for account anonymity detection. The labeling system is based on name extraction and identity verification and, to ensure the quality of the dataset, web mining and face similarity measurement are applied. We establish a dataset containing 20133 accounts and make it available to the public. The multi-modal method exploits the visual, textual, and numerical features extracted with ViT, BERT, and MLP, and uses the Transformer and MLP for feature fusion. Experimental results prove the effectiveness of the feature fusion and the accuracy of classification with accuracy of 86.21% and 92.46%.