Recently, the proliferation of fake news has imposed a dramatic impact on the public, so the fake news detection has been attracting increasing attention. Most of existing methods employ traditional classification models or neural networks that consider news content, comments, and context to detect fake news. However, the characteristics of news vary by type, topic, and context, which are difficult to uniformly represent for fake news detection. In this article, we propose a novel fake news detection model based on retweeting user embedding using only the stable and easily accessible information of retweeting users. Our model includes three components. First, we use all retweeting users to construct the retweeting graph and then learn the user representation. Second, for each tweet, we aggregate the representations of its retweeting users to learn the tweet representation. Finally, the tweet representations are fed into a feedforward neural network to train the news classifier. To demonstrate the effectiveness of our method, we conduct experiments on two public datasets. The results show that our classifier performs well and achieves better results than other state-of-the-art methods.

User identification across social sites, which benefits many applications, has recently been attracting considerable attention. Most existing methods focused more on the effectiveness of user identification, rather than on efficiency. Matching as many crosssite user accounts as possible, which causes very high computation overhead posed by the full-scale pairwise comparisons, remains unsolved, especially when the number of users reaches tens of millions or more. To address this issue, we present a novel locality sensitive hashing method for user identification (UI-LSH), which consists of four components. (1) It involves embedding stay points into vectors, (2) and constructing locality-sensitive hashing families suitable for stay points. (3) It presents a method for projecting stay points into hash buckets that ensures the close stay points are placed in the same bucket with high probability. (4) It constructs the candidate user pairs based on the projection results. The experiments on three ground-truth datasets show that our method reduces the number of user pairs to be compared by as much as 81.87%, 67.68%, and 63.15%, respectively. Overall, UI-LSH holds great promise for significantly improving the efficiency of user identification.

User Identification with Spatio-Temporal awareness has been attracting much attention from academia. The existing methods not only handle temporal and spatial data separately but also do not consider conflictive check-in records. To tackle these problems, we propose a novel approach that consists of three parts. 1) Measure the similarity of users with a kernel density estimation based method, which handles the spatial and temporal data together; 2) Assign weights to check-in records following the idea of TFIDF, which highlights the discriminative check-in records; 3) Penalize the similarity of users based on the number of conflictive check-in records. The pair-wise users with similarity higher than a predefined threshold are considered to belong to the same individual. Experiments demonstrate the superiority of the proposed approach.