Microblog Rumor Detection Method Based on Propagation Path Tree Kernel Learning

The rapid development of online social platforms such as microblog promotes the widespread propagation of various rumors information,thereby posing potential threats to social order.Rumor detection on microblog can effectively curb the spread of rumors and is of great significance for purifying the network environment and maintaining social stability.In view of the fact that the traditional rumor detection model only considers the characteristics of users,contents and communication statistics,and ignores the structural problem that the characteristics of users′ influence and emotional feedback increase with the forwarding and comment relationship in the process of rumor communication,a path tree kernel rumor automatic detection model based on the microblog information propagation tree is proposed in this paper.It embeds users’ influence,emotional feedback,contents into the nodes ofpropagation tree.By calculating the path similarity from the root node to the leaf node in propagation tree,the similarity between the microblog information propagation tree structure is obtained.Furthermore,the model uses the support vector machine classifier based on the propagation path tree kernel todetect microblog rumors.Experimental results show that the accuracy of the proposed model reaches 93.5%,which is better than that of the rumor detection models without considering the structure of propagation path

Fully Automated Fact Checking Using External Sources

Given the constantly growing proliferation of false claims online in recent years, there has been also a growing research interest in automatically distinguishing false rumors from factually true claims. Here, we propose a general-purpose framework for fully-automatic fact checking using external sources, tapping the potential of the entire Web as a knowledge source to confirm or reject a claim. Our framework uses a deep neural network with LSTM text encoding to combine semantic kernels with task-specific embeddings that encode a claim together with pieces of potentially-relevant text fragments from the Web, taking the source reliability into account. The evaluation results show good performance on two different tasks and datasets: (i) rumor detection and (ii) fact checking of the answers to a question in community question answering forums.Comment: RANLP-201

A Semi-automatic Method for Efficient Detection of Stories on Social Media

Twitter has become one of the main sources of news for many people. As real-world events and emergencies unfold, Twitter is abuzz with hundreds of thousands of stories about the events. Some of these stories are harmless, while others could potentially be life-saving or sources of malicious rumors. Thus, it is critically important to be able to efficiently track stories that spread on Twitter during these events. In this paper, we present a novel semi-automatic tool that enables users to efficiently identify and track stories about real-world events on Twitter. We ran a user study with 25 participants, demonstrating that compared to more conventional methods, our tool can increase the speed and the accuracy with which users can track stories about real-world events.Comment: ICWSM'16, May 17-20, Cologne, Germany. In Proceedings of the 10th International AAAI Conference on Weblogs and Social Media (ICWSM 2016). Cologne, German