Yanyuet Man

Jun Wu, Xuesong Ye, Yanyuet Man

The rapid and accurate identification of bot accounts in online social networks is an ongoing challenge. In this paper, we propose BOTTRINET, a unified embedding framework that leverages the textual content posted by accounts to detect bots. Our approach is based on the premise that account personalities and habits can be revealed through their contextual content. To achieve this, we designed a triplet network that refines raw embeddings using metric learning techniques. The BOTTRINET framework produces word, sentence, and account embeddings, which we evaluate on a real-world dataset, CRESCI2017, consisting of three bot account categories and five bot sample sets. Our approach achieves state-of-the-art performance on two content-intensive bot sets, with an average accuracy of 98.34% and f1score of 97.99%. Moreover, our method makes a significant breakthrough on four content-less bot sets, with an average accuracy improvement of 11.52% and an average f1score increase of 16.70%. Our contribution is twofold: First, we propose a unified and effective framework that combines various embeddings for bot detection. Second, we demonstrate that metric learning techniques can be applied in this context to refine raw embeddings and improve classification performance. Our approach outperforms prior works and sets a new standard for bot detection in social networks.

Yanyuet Man, Xiangyun Ding, Xingcheng Yao et al.

Throughout the world, breast cancer is one of the leading causes of female death. Recently, deep learning methods are developed to automatically grade breast cancer of histological slides. However, the performance of existing deep learning models is limited due to the lack of large annotated biomedical datasets. One promising way to relieve the annotating burden is to leverage the unannotated datasets to enhance the trained model. In this paper, we first apply active learning method in breast cancer grading, and propose a semi-supervised framework based on expectation maximization (EM) model. The proposed EM approach is based on the collaborative filtering among the annotated and unannotated datasets. The collaborative filtering method effectively extracts useful and credible datasets from the unannotated images. Results of pixel-wise prediction of whole-slide images (WSI) demonstrate that the proposed method not only outperforms state-of-art methods, but also significantly reduces the annotation cost by over 70%.