Fake news on social media is a widespread and serious problem in today's society. Existing fake news detection methods focus on finding clues from Long text content, such as original news articles and user comments. This paper solves the problem of fake news detection in more realistic scenarios. Only source shot-text tweet and its retweet users are provided without user comments. We develop a novel neural network based model, Multi-View Attention Networks (MVAN) to detect fake news and provide explanations on social media. The MVAN model includes text semantic attention and propagation structure attention, which ensures that our model can capture information and clues both of source tweet content and propagation structure. In addition, the two attention mechanisms in the model can find key clue words in fake news texts and suspicious users in the propagation structure. We conduct experiments on two real-world datasets, and the results demonstrate that MVAN can significantly outperform state-of-the-art methods by 2.5% in accuracy on average, and produce a reasonable explanation.INDEX TERMS Fake news detection, graph attention networks, attention, deep learning, social media.
Existing rumor detection strategies typically provide detection labels while ignoring their explanation. Nonetheless, providing pieces of evidence to explain why a suspicious tweet is rumor is essential. As such, a novel model, LOSIRD, was proposed in this paper. First, LOSIRD mines appropriate evidence sentences and classifies them by automatically checking the veracity of the relationship of the given claim and its evidence from about 5 million Wikipedia documents. LOSIRD then automatically constructs two heterogeneous graph objects to simulate the propagation layout of the tweets and code the relationship of evidence. Finally, a graphSAGE processing component is used in LOSIRD to provide the label and evidence. To the best of our knowledge, we are the first one who combines objective facts and subjective views to verify rumor. The experimental results on two real-world Twitter datasets showed that our model exhibited the best performance in the early rumor detection task and its rumor detection performance outperformed other baseline and state-of-the-art models. Moreover, we confirmed that both objective information and subjective information are fundamental clues for rumor detection.
Rumors in social media represent a severe problem prevailing in today's society. Previous studies on automated rumor detection have shown that the topological information specific to social media is a vital clue for debunking rumors. However, existing automatic rumor detection approaches either oversimplify the graph structure or ignore this crucial clue. To address this issue, we propose a model that explores homogeneity and conversation structure to identify rumors. Our model learns more comprehensive and precise representations by modeling follower-following relationships of users, simulating the propagation layout of tweets, and connecting responders' behavior. The experimental results on two public Twitter datasets show that our model's performance outperforms other state-of-the-art baseline models. Furthermore, the experimental results prove our hypothesis that birds of a feather rumor together. The results demonstrate that both the conversation structure and the friend network's homogeneity are significant for checking the veracity of a suspicious tweet.
Inosine monophosphate dehydrogenase (IMPDH), the rate-limiting enzyme catalyzing de novo biosynthesis of guanine nucleotides, aggregates under certain circumstances into a type of non-membranous filamentous macrostructure termed “cytoophidium” or “rod and ring” in several types of cells. However, the biological significance and underlying mechanism of IMPDH assembling into cytoophidium remain elusive. In mouse ovaries, IMPDH is reported to be crucial for the maintenance of oocyte–follicle developmental synchrony by providing GTP substrate for granulosa cell natriuretic peptide C/natriuretic peptide receptor 2 (NPPC/NPR2) system to produce cGMP for sustaining oocyte meiotic arrest. Oocytes and the associated somatic cells in the ovary hence render an exciting model system for exploring the functional significance of formation of IMPDH cytoophidium within the cell. We report here that IMPDH2 cytoophidium forms in vivo in the growing oocytes naturally and in vitro in the cumulus-enclosed oocytes treated with IMPDH inhibitor mycophenolic acid (MPA). Inhibition of IMPDH activity in oocytes and preimplantation embryos compromises oocyte meiotic and developmental competences and the development of embryos beyond the 4-cell stage, respectively. IMPDH cytoopidium also forms in vivo in the granulosa cells of the preovulatory follicles after the surge of luteinizing hormone (LH), which coincides with the resumption of oocyte meiosis and the reduction of IMPDH2 protein expression. In cultured COCs, MPA-treatment causes the simultaneous formation of IMPDH cytoopidium in cumulus cells and the resumption of meiosis in oocytes, which is mediated by the MTOR pathway and is prevented by guanosine supplementation. Therefore, our results indicate that cytoophidia do form in the oocytes and granulosa cells at particular stages of development, which may contribute to the oocyte acquisition of meiotic and developmental competences and the induction of meiosis re-initiation by the LH surge, respectively.
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