The proliferation of misinformation across diverse social media platforms has drawn significant attention from both academic and industrial communities due to its detrimental effects. Accordingly, automatically distinguishing misinformation, dubbed as Misinformation Detection (MD), has become an increasingly active research topic. The mainstream methods formulate MD as a static learning paradigm, which learns the mapping between the content, links, and propagation of news articles and the corresponding manual veracity labels. However, the static assumption is often violated, since in real-world scenarios, the veracity of news articles may vacillate within the dynamically evolving social environment. To tackle this problem, we propose a novel framework, namely Misinformation detection with Dynamic Environmental Representations (MISDER). The basic idea of MISDER lies in learning a social environmental representation for each period and employing a temporal model to predict the representation for future periods. In this work, we specify the temporal model as the LSTM model, continuous dynamics equation, and pre-trained dynamics system, suggesting three variants of MISDER, namely MISDER-LSTM, MISDER-ODE, and MISDER-PT, respectively. To evaluate the performance of MISDER, we compare it to various MD baselines across 2 prevalent datasets, and the experimental results can indicate the effectiveness of our proposed model.

Weakly-supervised text classification aims to train predictive models with unlabeled texts and a few representative words of classes, referred to as category words, rather than labeled texts. These weak supervisions are much more cheaper and easy to collect in real-world scenarios. To resolve this task, we propose a novel deep classification model, namely Weakly-supervised Text Classification with Wasserstein Barycenter Regularization (WTC-WBR). Specifically, we initialize the pseudo-labels of texts by using the category word occurrences, and formulate a weakly self-training framework to iteratively update the weakly-supervised targets by combining the pseudo-labels with the sharpened predictions. Most importantly, we suggest a Wasserstein barycenter regularization with the weakly-supervised targets on the deep feature space. The intuition is that the texts tend to be close to the corresponding Wasserstein barycenter indicated by weakly-supervised targets. Another benefit is that the regularization can capture the geometric information of deep feature space to boost the discriminative power of deep features. Experimental results demonstrate that WTC-WBR outperforms the existing weakly-supervised baselines, and achieves comparable performance to semi-supervised and supervised baselines.

Ambiguous Label Learning (ALL), as an emerging paradigm of weakly supervised learning, aims to induce the prediction model from training datasets with ambiguous supervision, where, specifically, each training instance is annotated with a set of candidate labels but only one is valid. To handle this task, the existing shallow methods mainly disambiguate the candidate labels by leveraging various regularization techniques. Inspired by the great success of deep generative adversarial networks, we apply it to perform effective candidate label disambiguation from a new instance-pivoted perspective. Specifically, for each ALL instance, we recombine its feature representation with each of candidate labels to generate a set of candidate instances, where only one is real and all others are fake. We formulate a unified adversarial objective with respect to three players, i.e., a discriminator, a generator, and a classifier. The discriminator is used to detect the fake candidate instances, so that the classifier can be trained without them. With this insight, we develop a novel ALL method, namely Adversarial Ambiguous Label Learning with Candidate Instance Detection (A2L2CID). Theoretically, we analyze that there is a global equilibrium point between the three players. Empirically, extensive experimental results indicate that A2L2CID outperforms the state-of-the-art ALL methods.

Neural topic modeling provides a flexible, efficient, and powerful way to extract topic representations from text documents. Unfortunately, most existing models cannot handle the text data with network links, such as web pages with hyperlinks and scientific papers with citations. To resolve this kind of data, we develop a novel neural topic model , namely Layer-Assisted Neural Topic Model (LANTM), which can be interpreted from the perspective of variational auto-encoders. Our major motivation is to enhance the topic representation encoding by not only using text contents, but also the assisted network links. Specifically, LANTM encodes the texts and network links to the topic representations by an augmented network with graph convolutional modules, and decodes them by maximizing the likelihood of the generative process. The neural variational inference is adopted for efficient inference. Experimental results validate that LANTM significantly outperforms the existing models on topic quality, text classification and link prediction..

Wasserstein distance, defined as the cost (measured by word embeddings) of optimal transport plan for moving between two histograms, has been proven effective in tasks of natural language processing. In this paper, we extend Nonnegative Matrix Factorization (NMF) to a novel Wasserstein topic model, namely Semantics-Assisted Wasserstein Learning (SAWL), with simultaneous learning of topics and word embeddings. In Sawl, we formulate an NMF-like unified objective that integrates the regularized Wasserstein distance loss with a context factorization of word context information. Therefore, Sawl can refine the word embeddings for capturing corpus-specific semantics, enabling to boost topics and word embeddings each other. We analyze Sawl, and provide its dimensionality-dependent generalization bounds of reconstruction errors. Experimental results indicate that Sawl outperforms the state-of-the-art baseline models.