RESUMEN
Nowadays, a massive number of people are involved in various social media. This fact enables organizations and institutions to more easily access their audiences across the globe. Some of them use social bots as an automatic entity to gain intangible access and influence on their users by faster content propagation. Thereby, malicious social bots are populating more and more to fool humans with their unrealistic behavior and content. Hence, that's necessary to distinguish these fake social accounts from real ones. Multiple approaches have been investigated in the literature to answer this problem. Statistical machine learning methods are one of them focusing on handcrafted features to represent characteristics of social bots. Although they reached successful results in some cases, they relied on the bot's behavior and failed in the behavioral change patterns of bots. On the other hands, more advanced deep neural network-based methods aim to overcome this limitation. Generative adversarial network (GAN) as new technology from this domain is a semi-supervised method that demonstrates to extract the behavioral pattern of the data. In this work, we use GAN to leak more information of bot samples for state-of-the-art textual bot detection method (Contextual LSTM). Although GAN augments low labeled data, original textual GAN (Sequence Generative Adversarial Net (SeqGAN)) has the known limitation of convergence. In this paper, we invested this limitation and customized the GAN idea in a new framework called GANBOT, in which the generator and classifier connect by an LSTM layer as a shared channel between them. Our experimental results on a bench-marked dataset of Twitter social bot show our proposed framework outperforms the existing contextual LSTM method by increasing bot detection probabilities.
RESUMEN
Introduction: The severity of COVID-19 may be correlated with the risk of liver injury development. An increasing number of studies indicate that degrees of hepatotoxicity has been associated with using some medications in the management of COVID-19 patients. However, limited studies had systematically investigated the evidence of drug-induced liver injury (DILI) in COVID-19 patients. Thus, this study aimed to examine DILI in COVID-19 patients. Methods: A systematic search was carried out in PubMed/Medline, EMBASE, and Web of Science up to December 30, 2020. Search items included "SARS-CoV-2", "Coronavirus," COVID-19, and liver injury. Results: We included 22 related articles. Among included studies, there was five case report, five case series, four randomizes control trial (RCT), seven cohort studies, and one cross-sectional study. The drugs included in this systematic review were remdesivir, favipiravir, tocilizumab, hydroxychloroquine, and lopinavir/ritonavir. Among included studies, some studies revealed a direct role of drugs, while others couldn't certainly confirm that the liver injury was due to SARS-CoV-2 itself or administration of medications. However, a significant number of studies reported that liver injury could be attributable to drug administration. Discussion: Liver injury in COVID-19 patients could be caused by the virus itself or the administration of some types of drug. Intensive liver function monitoring should be considered for patients, especially patients who are treated with drugs such as remdesivir, lopinavir/ritonavir, and tocilizumab.
RESUMEN
The diffusion process in networks is studied with the objective of identifying the dynamics and for predicting the behavior of network entities. Social media plays an important role in people's lives. Diffusion processes, as one of the most important branches of social media analysis, have their presence in various domains such as information spreading, diffusion of innovation, idea dissemination, and product acceptance to identify user's pattern and their behavior in social media networks. Users are not limited to one social network and are engaged in multiple social media such as Twitter, Instagram, Telegram, and Facebook. This fact has created new phenomena in social network analysis, called multiplex network analysis. Thus, the scope of diffusion process analysis has been transferred from single layer networks to multiplex networks. Diffusion process analysis can be studied at both infrastructure-level and diffusion-level; at infrastructure-level, the structural network's properties such as clustering coefficient and degree centrality are being studied; and in diffusion-level the diffusion network's properties such as diffusion depth and seed nodes are being studied. On the other hand, a reliable analysis requires complete information on both infrastructure and diffusion networks. However, complete data is not accessible forever, this fact is due to some limitations such as crawling big data, gathering social media policies, and user privacy. Incomplete data can lead to poor analysis, so in this work we, first of all, investigate the impact of missing data in both infrastructure and diffusion networks, the impact of random and non-random missing infrastructure data on nine diffusion network's properties such as number of infected nodes, number of infected edges, diffusion length and number of seed nodes. Secondly, based on the multiplex diffusion tree, we introduce a new model named as MLC-tree for an incomplete diffusion network. Finally, we evaluate our model on both synthetic and real social networks; these results show that the MLC-tree can decrease the relative error more than 50 percent while missing 20 to 80 percent of complete data.
