A Unified Contrastive Transfer Framework with Propagation Structure for Boosting Low-Resource Rumor Detection

The truth is significantly hampered by massive rumors that spread along with breaking news or popular topics. Since there is sufficient corpus gathered from the same domain for model training, existing rumor detection algorithms show promising performance on yesterday's news. However, due to a lack of training data and prior expert knowledge, they are poor at spotting rumors concerning unforeseen events, especially those propagated in different languages (i.e., low-resource regimes). In this paper, we propose a unified contrastive transfer framework to detect rumors by adapting the features learned from well-resourced rumor data to that of the low-resourced. More specifically, we first represent rumor circulated on social media as an undirected topology, and then train a Multi-scale Graph Convolutional Network via a unified contrastive paradigm. Our model explicitly breaks the barriers of the domain and/or language issues, via language alignment and a novel domain-adaptive contrastive learning mechanism. To enhance the representation learning from a small set of target events, we reveal that rumor-indicative signal is closely correlated with the uniformity of the distribution of these events. We design a target-wise contrastive training mechanism with three data augmentation strategies, capable of unifying the representations by distinguishing target events. Extensive experiments conducted on four low-resource datasets collected from real-world microblog platforms demonstrate that our framework achieves much better performance than state-of-the-art methods and exhibits a superior capacity for detecting rumors at early stages.Comment: A significant extension of the first contrastive approach for low-resource rumor detection (arXiv:2204.08143

A Weakly Supervised Propagation Model for Rumor Verification and Stance Detection with Multiple Instance Learning

The diffusion of rumors on microblogs generally follows a propagation tree structure, that provides valuable clues on how an original message is transmitted and responded by users over time. Recent studies reveal that rumor detection and stance detection are two different but relevant tasks which can jointly enhance each other, e.g., rumors can be debunked by cross-checking the stances conveyed by their relevant microblog posts, and stances are also conditioned on the nature of the rumor. However, most stance detection methods require enormous post-level stance labels for training, which are labor-intensive given a large number of posts. Enlightened by Multiple Instance Learning (MIL) scheme, we first represent the diffusion of claims with bottom-up and top-down trees, then propose two tree-structured weakly supervised frameworks to jointly classify rumors and stances, where only the bag-level labels concerning claim's veracity are needed. Specifically, we convert the multi-class problem into a multiple MIL-based binary classification problem where each binary model focuses on differentiating a target stance or rumor type and other types. Finally, we propose a hierarchical attention mechanism to aggregate the binary predictions, including (1) a bottom-up or top-down tree attention layer to aggregate binary stances into binary veracity; and (2) a discriminative attention layer to aggregate the binary class into finer-grained classes. Extensive experiments conducted on three Twitter-based datasets demonstrate promising performance of our model on both claim-level rumor detection and post-level stance classification compared with state-of-the-art methods.Comment: Accepted by SIGIR 202

WSDMS: Debunk Fake News via Weakly Supervised Detection of Misinforming Sentences with Contextualized Social Wisdom

In recent years, we witness the explosion of false and unconfirmed information (i.e., rumors) that went viral on social media and shocked the public. Rumors can trigger versatile, mostly controversial stance expressions among social media users. Rumor verification and stance detection are different yet relevant tasks. Fake news debunking primarily focuses on determining the truthfulness of news articles, which oversimplifies the issue as fake news often combines elements of both truth and falsehood. Thus, it becomes crucial to identify specific instances of misinformation within the articles. In this research, we investigate a novel task in the field of fake news debunking, which involves detecting sentence-level misinformation. One of the major challenges in this task is the absence of a training dataset with sentence-level annotations regarding veracity. Inspired by the Multiple Instance Learning (MIL) approach, we propose a model called Weakly Supervised Detection of Misinforming Sentences (WSDMS). This model only requires bag-level labels for training but is capable of inferring both sentence-level misinformation and article-level veracity, aided by relevant social media conversations that are attentively contextualized with news sentences. We evaluate WSDMS on three real-world benchmarks and demonstrate that it outperforms existing state-of-the-art baselines in debunking fake news at both the sentence and article levels

Research and experimental verification on low-frequency long-range underwater sound propagation dispersion characteristics under dual-channel sound speed profiles in the Chukchi Plateau

The dual-channel sound speed profiles of the Chukchi Plateau and the Canadian Basin have become current research hotspots due to their excellent low-frequency sound signal propagation ability. Previous research has mainly focused on using sound propagation theory to explain the changes in sound signal energy. This article is mainly based on the theory of normal modes to study the fine structure of low-frequency wide-band sound propagation dispersion under dual-channel sound speed profiles. In this paper, the problem of the intersection of normal mode dispersion curves caused by the dual-channel sound speed profile (SSP) has been explained, the blocking effect of seabed terrain changes on dispersion structures has been analyzed, and the normal modes has been separated by using modified warping operator. The above research results have been verified through a long-range seismic exploration experiment at the Chukchi Plateau. At the same time, based on the acoustic signal characteristics in this environment, two methods for estimating the distance of sound sources have been proposed, and the experiment data at sea has also verified these two methods.Comment: 30 pages, 18 figure

Research and experimental verification on low-frequency long-range sound propagation characteristics under ice-covered and range-dependent marine environment in the Arctic

At present, research on sound propagation under the Arctic ice mainly focuses on modeling and experimental verification of sound propagation under sea ice cover and unique sound velocity profiles. Among them, the main research object of concern is sound transmission loss, and this article will delve into the time-domain waveform and fine dispersion structure of low-frequency broadband acoustic signals. Firstly, based on the theory of normal modes, this article derives the horizontal wavenumber expression and warping transformation operator for refractive normal modes in the Arctic deep-sea environment. Subsequently, based on measured ocean environmental parameters and sound field simulation calculations, this article studied the general laws of low-frequency long-range sound propagation signals in the Arctic deep-sea environment, and elucidated the impact mechanism of environmental factors such as seabed terrain changes, horizontal changes in sound velocity profiles (SSPs), and sea ice cover on low-frequency long-range sound propagation in the Arctic. This article validates the above research viewpoint through a sound propagation experiment conducted in the Arctic with a propagation distance exceeding 1000km. The marine environment of this experiment has obvious horizontal variation characteristics. At the same time, this article takes the lead in utilizing the warping transformation of refractive normal waves in the Arctic waters to achieve single hydrophone based separation of normal waves and extraction of dispersion structures, which is conducive to future research on underwater sound source localization and environmental parameter inversion based on dispersion structures.Comment: 46 pages, 35 figure

The Difference of Chemical Components and Biological Activities of the Crude Products and the Salt-Processed Product from Semen Cuscutae

Semen Cuscutae is a well-known Chinese medicine which has been used to nourish kidney in China for thousands of years. The crude product of semen Cuscutae (CP) and its salt-processed product (SPP) are separately used in clinic for their different effects. The study was designed to investigate the influence of processing from semen Cuscutae on chemical components and biological effects. The principal component analysis and quantitative analysis were used to study the differences of the chemical components. The effects of nourishing kidney were detected to compare the differences between the CP and SPP. The PCA results showed that the obvious separation was achieved in the CP and SPP samples. The results of quantitative analysis showed that quercetin and total flavonoids had significantly increased after salt processing while hyperoside had decreased. The comparison of CP and SPP on biological activities showed that both of them could ameliorate the kidney-yang deficiency syndrome by restoring the level of sex hormone, improving the immune function and antioxidant effect. However, SPP was better in increasing the level of T and the viscera weight of testicle and epididymis, improving the antioxidant effect. The results suggested that salt processing changed its chemical profile, which in turn enhanced its biological activities

IncHI1 plasmids mediated the tet(X4) gene spread in Enterobacteriaceae in porcine

The tigecycline resistance gene tet(X4) was widespread in various bacteria. However, limited information about the plasmid harboring the tet(X4) gene spread among the different species is available. Here, we investigated the transmission mechanisms of the tet(X4) gene spread among bacteria in a pig farm. The tet(X4) positive Escherichia coli, Klebsiella pneumoniae, Enterobacter cloacae and Enterobacter hormaeche were identified in the same farm. The whole genome sequencing (WGS) analysis showed that the K. pneumoniae belonged to ST727 (n = 11) and ST3830 (n = 1), E. cloacae and E. hormaeche belonged to ST524 (n = 1) and ST1862 (n = 1). All tet(X4) genes were located on the IncHI1 plasmids that could be conjugatively transferred into the recipient E. coli C600 at 30°C. Moreover, a fusion plasmid was identified that the IncHI1 plasmid recombined with the IncN plasmid mediated by ISCR2 during the conjugation from strains B12L to C600 (pB12L-EC-1). The fusion plasmid also has been discovered in a K. pneumoniae (K1L) that could provide more opportunities to spread antimicrobial resistance genes. The tet(X4) plasmids in these bacteria are derived from the same plasmid with a similar structure. Moreover, all the IncHI1 plasmids harboring the tet(X4) gene in GenBank belonged to the pST17, the newly defined pMLST. The antimicrobial susceptibility testing was performed by broth microdilution method showing the transconjugants acquired the most antimicrobial resistance from the donor strains. Taken together, this report provides evidence that IncHI1/pST17 is an important carrier for the tet(X4) spread in Enterobacteriaceae species, and these transmission mechanisms may perform in the environment