Twitter is one of the largest social networking platforms, which allows users to make friends, read the latest news, share personal ideas, and discuss social issues. The huge popularity of Twitter mean it attracts a lot of online spammers. Traditional spam detection approaches have shown the effectiveness for identifying Twitter spammers by extracting handcrafted features and training machine learning models. However, such models need knowledge from domain experts. Moreover, the behaviors of spammers can change according to the defense strategies of Twitter. These result in the ineffectiveness of the traditional feature-based approaches. Although deep-learning-based approaches have been proposed for detecting Twitter spammers, they all treat each tweet equally, and ignore the differences among them. To solve these issues, in this paper, we propose a new attention-based deep learning model to detect social spammers in Twitter. In particular, we first introduce the state-of-the-art pretraining model BERTweet for learning the representation of each tweet, and then use the proposed novel attention-based mechanism to learn the user representations by distinguishing the differences among tweets posted by each user. Moreover, we take social interactions into consideration and propose that a graph attention network is used to update the learned user representations, to further improve the accuracy of identifying spammers. Experiments on a publicly available, real-world Twitter dataset show the effectiveness of the proposed model, which is able to significantly enhance the performance.

The objective of diagnosis prediction involves foreseeing the potential diseases/conditions according to analyzing patients’ historical Electronic Health Records (EHRs). The primary challenge in this task is to develop a predictive model that is both sturdy and accurate, while also being interpretable. The most advanced models usually take recurrent neural networks (RNNs) as backbones and then utilize other techniques, such as attention mechanisms, to address this challenge. However, the effectiveness of these models heavily relies on having ample EHR data. Consequently, when the data is insufficient, the performance of these models declines significantly. Recently, graph-based attention models have been proposed to mitigate the issues caused by insufficient data, although they do not fully capitalize on the knowledge present in medical ontologies. To address these problems, knowledge-based recurrent neural networks (named KARNS) are introduced, which is an end-to-end, robust, and accurate deep learning-based architecture designed to predict patients’ future health information. KARNS explicitly leverages the high-level representations of medical codes within the medical ontologies to enhance the accuracy of predictions. Experimental outcomes demonstrate that the proposed KARNS outperforms existing approaches on three real-world medical datasets. It ensures robustness even with limited training data and learns disease representations that are interpretable.

With the increasing popularity of social network platforms such as Twitter and Sina Weibo, a lot of malicious users, also known as social spammers, disseminate illegal information to normal users. Several approaches are proposed to detect spammers by training a classifier with optimization methods and mainly using content and social following information. Due to the development of spammers’ strategies and the courtesy of some legitimate users, social following information becomes vulnerable to fake by spammers. Meanwhile, the possible social activities and behaviors vary significantly among different users, which leads to a large yet sparse feature space to be modeled by existing approaches. To address issues, in this paper, we propose a new approach named CNMFSD for spammer detection in social networks, which exploits both content information and users interaction relationships in an innovative manner. We have empirically validated the proposed method on a real-world Twitter dataset, and experimental results show that the proposed CNMFSD method improves the detection performance significantly compared with baselines.

As a result of the rapid development of internet technology, images are widely used on various social networks, such as WeChat, Twitter or Facebook. It follows that images with spam can also be freely transmitted on social networks. Most of the traditional methods can only detect spam in the form of links and texts; there are few studies on detecting images with spam. To this end, a novel detection method for identifying social images with spam, based on deep neural network and frequency domain pre-processing, is proposed in this paper. Firstly, we collected several images with embedded spam and combined the DIV2K2017 dataset to build an image dataset for training the proposed detection model. Then, the specific components of the spam in the images were determined through experiments and the pre-processing module was specially designed. Low-frequency domain regions with less spam are discarded through Haar wavelet transform analysis. In addition, a feature extraction module with special convolutional layers was designed, and an appropriate number of modules was selected to maximize the extraction of three different high-frequency feature regions. Finally, the different high-frequency features are spliced along the channel dimension to obtain the final classification result. Our extensive experimental results indicate that the spam element mainly exists in the images as high-frequency information components; they also prove that the proposed model is superior to the state-of-the-art detection models in terms of detection accuracy and detection efficiency.