The advancement of Internet technology has spurred a rise in the dissemination of misinformation, which has had profoundly negative impacts across a wide array of fields. To address this issue, the field of Misinformation Detection (MD), which focuses on the automated identification of online misinformation, has gained significant traction among researchers. In our study, we introduce an innovative plugand- play augmentation technique for MD, termed DEtecting Misinformation by Uncovering Commonsense Conflict (DEMUC). Our approach is grounded in previous psychological research that suggests that fake content often contains commonsense. Accordingly, we develop commonsense expressions for articles to highlight potential conflicts between the inferred commonsense triplets and the established ones derived from reliable commonsense reasoning tools. According to the used tools, we induce two variants DEMUC-KLM using the knowledge language model COMET and DEMUC-LLM using the large language models. These generated expressions are then applied as augmentations to each article, enabling any MD method to be trained on these augmented datasets. Additionally, we have manually compiled a new dataset CoMis, which consists exclusively of fake articles characterized by commonsense conflicts. By integrating DEMUC with various existing MD frameworks and evaluating them on four public benchmark datasets and CoMis, our empirical findings show that both DEMUC-KLM and DEMUC-LLM consistently and significantly outperform current MD baselines, while also generating precise commonsense expressions.

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.

Over the past decade, social media platforms have been key in spreading rumors, leading to significant negative impacts. To counter this, the community has developed various Rumor Detection (RD) algorithms to automatically identify them using user comments as evidence. However, these RD methods often fail in the early stages of rumor propagation when only limited user comments are available, leading the community to focus on a more challenging topic named Rumor Early Detection (RED). Typically, existing RED methods learn from limited semantics in early comments. However, our preliminary experiment reveals that the RED models always perform best when the number of training and test comments is consistent and extensive. This inspires us to address the RED issue by generating more human-like comments to support this hypothesis. To implement this idea, we tune a comment generator by simulating expert collaboration and controversy and propose a new RED framework named CAMERED. Specifically, we integrate a mixture-of-expert structure into a generative language model and present a novel routing network for expert collaboration. Additionally, we synthesize a knowledgeable dataset and design an adversarial learning strategy to align the style of generated comments with real-world comments. We further integrate generated and original comments with a mutual controversy fusion module. Experimental results show that CAMERED outperforms state-of-the-art RED baseline models and generation methods, demonstrating its effectiveness.

Various social media platforms, e.g., Twitter and Reddit, allow people to disseminate a plethora of information more efficiently and conveniently. However, they are inevitably full of misinformation, causing damage to diverse aspects of our daily lives. To reduce the negative impact, timely identification of misinformation, namely Misinformation Detection (MD), has become an active research topic receiving widespread attention. As a complex phenomenon, the veracity of an article is influenced by various aspects. In this paper, we are inspired by the opposition of intents between misinformation and real information. Accordingly, we propose to reason the intent of articles and form the corresponding intent features to promote the veracity discrimination of article features. To achieve this, we build a hierarchy of a set of intents for both misinformation and real information by referring to the existing psychological theories, and we apply it to reason the intent of articles by progressively generating binary answers with an encoder-decoder structure. We form the corresponding intent features and integrate it with the token features to achieve more discriminative article features for MD. Upon these ideas, we suggest a novel MD method, namely Detecting Misinformation by Integrating Intent featuRes (DM-INTER). To evaluate the performance of DM-INTER, we conduct extensive experiments on benchmark MD datasets. The experimental results validate that DM-INTER can outperform the existing baseline MD methods.

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.

In real-world scenarios, the data are widespread that are annotated with a set of candidate labels but a single ground-truth label per-instance. The learning paradigm with such data, formally referred to as Partial Label (PL) learning, has recently drawn much attention. The traditional PL methods estimate the confidences being the ground-truth label of candidate labels with various regularizations and constraints, however, they only consider the local information, resulting in potentially less accurate estimations as well as worse classification performance. To alleviate this problem, we propose a novel PL method, namely PArtial label learNing by simultaneously leveraging GlObal and Local consIsteNcies (Pangolin). Specifically, we design a global consistency regularization term to pull instances associated with similar labeling confidences together by minimizing the distances between instances and label prototypes, and a local consistency term to push instances marked with no same candidate labels away by maximizing their distances. We further propose a nonlinear kernel extension of Pangolin, and employ the Taylor approximation trick for efficient optimization. Empirical results demonstrate that Pangolin significantly outperforms the existing PL baseline methods.

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.

Automatically classifying extremely short texts, such as social media posts and web page titles, plays an important role in a wide range of content analysis applications. However, traditional classifiers based on bag-of-words (BoW) representations often fail in this task. The underlying reason is that the document similarity can not be accurately measured under BoW representations due to the extreme sparseness of short texts. This results in significant difficulty to capture the generality of short texts. To address this problem, we use a better regularized word mover's distance (RWMD), which can measure distances among short texts at the semantic level. We then propose a RWMD-based centroid classifier for short texts, named RWMD-CC. Basically, RWMD-CC computes a representative semantic centroid for each category under the RWMD measure, and predicts test documents by finding the closest semantic centroid. The testing is much more efficient than the prior art of K nearest neighbor classifier based on WMD. Experimental results indicate that our RWMD-CC can achieve very competitive classification performance on extremely short texts.

Recently, dataless text classification has attracted increasing attention. It trains a classifier using seed words of categories, rather than labeled documents that are expensive to obtain. However, a small set of seed words may provide very limited and noisy supervision information, because many documents contain no seed words or only irrelevant seed words. In this paper, we address these issues using document manifold, assuming that neighboring documents tend to be assigned to a same category label. Following this idea, we propose a novel Laplacian seed word topic model (LapSWTM). In LapSWTM, we model each document as a mixture of hidden category topics, each of which corresponds to a distinctive category. Also, we assume that neighboring documents tend to have similar category topic distributions. This is achieved by incorporating a manifold regularizer into the log-likelihood function of the model, and then maximizing this regularized objective. Experimental results show that our LapSWTM significantly outperforms the existing dataless text classification algorithms and is even competitive with supervised algorithms to some extent. More importantly, it performs extremely well when the seed words are scarce.

In this paper, we develop a generic black-box expectation propagation (BBEP) algorithm that can be directly applied to Bayesian models without model-specific derivations. BBEP is built on the spirit of using Monte Carlo estimates, where the moment matching step in EP is replaced with Monte Carlo approximations. To avoid high variance, we employ importance sampling for variance reduction and analyze how to find an optimal proposal distribution. We compare BBEP against the state-of-the-art black-box algorithms on both synthetic and real-world data sets. The experimental results indicate that BBEP can reach better predictive performance than baseline algorithms, and even can be on a par with analytical solutions in some settings.