Detect Rumors in Microblog Posts for Low-Resource Domains via Adversarial Contrastive Learning (2022)

Abstract: Massive false rumors emerging along with breaking news or trending topics severely hinder the truth. Existing rumor detection approaches achieve promising performance on the yesterday’s news, since there is enough corpus collected from the same domain for model training. However, they are poor at detecting rumors about unforeseen events especially those propagated in minority languages due to the lack of training data and prior knowledge (i.e., low-resource regimes). In this paper, we propose an adversarial contrastive learning framework to detect rumors by adapting the features learned from well-resourced rumor data to that of the low-resourced. Our model explicitly overcomes the restriction of domain and/or language usage via language alignment and a novel supervised contrastive training paradigm. Moreover, we develop an adversarial augmentation mechanism to further enhance the robustness of low-resource rumor representation. Extensive experiments conducted on two 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.

## introduction with the proliferation of social media such as twitter and weibo, the emergence of breaking events provides opportunities for the spread of rumors, which is difficult to be identified due to limited domain expertise and relevant data. for instance, along with the unprecedented covid-19 pandemic, a false rumor claimed that "everyone who gets the vaccine will die or suffer from auto-immune diseases" 1 was translated into many languages and spread at lightning speed on social media, which seriously confuses the public and destroys the achievements of epidemic prevention in related countries or regions of the world. although some recent works focus on collecting microblog posts corresponding to covid-19 @xcite @xcite , existing rumor detection methods perform poorly without a large-scale qualified training corpus, i.e., in a low-resource scenario @xcite . thus there is an urgent need to develop automatic approaches to identify rumors in such low-resource domains especially amid breaking events. social psychology literature defines a rumor as a story or a statement whose truth value is unverified or deliberately false @xcite . recently, techniques using deep neural networks (dnns) @xcite @xcite have achieved promising results for detecting rumors on microblogging websites by learning rumor-indicative features from sizeable rumor corpus with veracity annotation. however, such dnn-based approaches are purely data-driven and have a major limitation on detecting emerging events concerning about low-resource domains, i.e., the distinctive topic coverage and word distribution @xcite required for detecting low-resource rumors are often not covered by the public benchmarks @xcite @xcite . on another hand, for rumors propagated in different languages, existing monolingual approaches are not applicable since there are even no sufficient open domain data for model training in the target language. in this paper, we assume that the close correlations between the well-resourced rumor data and the low-resourced could break the barriers of domain and language, substantially boosting lowresource rumor detection within a more general framework. taking the breaking event covid-19 as an example, we collect corresponding rumorous and non-rumorous claims with propagation threads from twitter and sina weibo which are the most popular microblogging websites in english and chinese, respectively. figure 1 illustrates the word clouds of rumor and non-rumor data from an open domain benchmark (i.e., twitter @xcite ) and two covid-19 datasets (i.e., twitter-covid19 and weibo-covid19). it can be seen that both twitter and twitter-covid19 contain denial opinions towards rumors, e.g., "fake", "joke", "stupid" in figure 1 (a) and "wrong symptom", "exactly sick", "health panic" in figure 1 (b). in contrast, supportive opinions towards non-rumors can be drawn from figure 1(d )-1(e). moreover, considering that covid-19 is a global disease, massive misinformation could be widely propagated in different languages such as arabic @xcite , indic @xcite , english @xcite and chinese @xcite . similar identical patterns can be observed in chinese on weibo from figure 1(c ) and figure 1(f ). although the covid-19 data tend to use expertise words or language-related slang, we argue that aligning the representation space of identical rumorindicative patterns of different domains and/or languages could adapt the features captured from wellresourced data to that of the low-resourced. to this end, inspired by contrastive learning @xcite , we propose an adversarial contrastive learning approach for lowresource rumor detection (aclr), to encourage effective alignment of rumor-indicative features in the well-resourced and low-resource data. more specifically, we first transform each microblog post into a language-independent vector by semantically aligning the source and target language in a shared vector space. as the diffusion of rumors generally follows a propagation tree that provides valuable clues on how a claim is transmitted @xcite , we thus resort to a structure-based neural network @xcite to catch informative patterns. then, we propose a novel supervised contrastive learning paradigm to minimize the intraclass variance of source and target instances with same veracity, and maximize inter-class variance of instances with different veracity. to further enhance the feature adaption of contrastive learning, we exploit adversarial attacks @xcite to plenish noise to the original event-level representation by computing adversarial worst-case perturbations, forcing the model to learn non-trivial but effective features. extensive experiments conducted on two real-world low-resource datasets confirm that (1) our model yields outstanding performances for detecting low-resource rumors over the state-of-the-art baselines with a large margin; and (2) our method performs particularly well on early rumor detection which is crucial for timely intervention and debunking especially for breaking events. the main contributions of this paper are of three-fold: ## related work pioneer studies for automatic rumor detection focus on learning a supervised classifier utilizing features crafted from post contents, user profiles, and propagation patterns @xcite @xcite . subsequent studies then propose new features such as those representing rumor diffusion and cascades @xcite @xcite . @xcite alleviate the engineering effort by using a set of regular expressions to find questing and denying tweets. dnn-based models such as recurrent neural networks @xcite , convolutional neural networks @xcite , and attention mechanism @xcite are then employed to learn the features from the stream of social media posts. however, these approaches simply model the post structure as a sequence while ignoring the complex propagation structure. to extract useful clues jointly from content semantics and propagation structures, some approaches propose kernel-learning models @xcite to make a comparison between propagation trees. tree-structured recursive neural networks (rvnn) @xcite and transformer-based models @xcite are proposed to generate the representation of each post along a propagation tree guided by the tree structure. more recently, graph neural networks @xcite have been exploited to encode the conversation thread for higher-level representations. however, such data-driven approaches fail to detect rumors in low-resource regimes @xcite because they often require sizeable training data which is not available for low-resource domains and/or languages. in this paper, we propose a novel framework to adapt existing models with the effective propagation structure for detecting rumors from different domains and/or languages. to facilitate related fact-checking tasks in lowresource settings, domain adaption techniques are utilized to detect fake news @xcite @xcite by learning features from multi-modal data such as texts and images. @xcite proposed a simple way of leveraging the perplexity score obtained from pre-trained language models (lms) for the few-shot fact-checking task. different from these works of adaption on multi-modal data and transfer learning of lms, we focus on language and domain adaptation to detect rumors from low-resource microblog posts corresponding to breaking events. contrastive learning (cl) aims to enhance representation learning by maximizing the agreement among the same types of instances and distinguishing from the others with different types @xcite . in recent years, cl has achieved great success in unsupervised visual representation learning @xcite @xcite . besides computer vision, recent studies suggest that cl is promising in the semantic textual similarity @xcite , stance detection @xcite , short text clustering @xcite , unknown intent detection @xcite , and abstractive summarization @xcite , etc. however, the above cl frameworks are specifically proposed to augment unstructured textual data such as sentence and document, which are not suitable for the lowresource rumor detection task considering claims together with more complex propagation structures of community response. ## problem statement in this work, we define the low-resource rumor detection task as: given a well-resourced dataset as source, classify each event in the target lowresource dataset as a rumor or not, where the source and target data are from different domains and/or languages. specifically, we define a well-resourced source dataset for training as a set of events d @xmath0, c s 2 , • • • , c s m }, where m is the number of source events. each event c @xmath1, c, t (c)) is a tuple representing a given claim c which is associated with a veracity label @xmath2, non-rumor}, and ideally all its relevant responsive microblog post in chronolog- sentence encoder xlm-r structure-based network adversarial contrastive training paradigm post-level representation original post event-level representation pull closer pull closer push apart push apart adversarial augmentation adversarial augmentation source samples source samples target samples target samples augmented samples augmented samples figure 2 : the overall architecture of our proposed method. for source and small target training data, we first obtain post-level representations after cross-lingual sentence encoding, then train the structure-based network with the adversarial contrastive objective. for target test data, we extract the event-level representations to detect rumors. we formulate the task of low-resource rumor detection as a supervised classification problem that trains a domain/language-agnostic classifier f (•) adapting the features learned from source datasets to that of the target events, that is, f (c t |d s ) → y. note that although the tweets are notated sequentially, there are connections among them based on their responsive relationships. so most previous works represent the conversation thread as a tree structure @xcite . ## our approach in this section, we introduce our adversarial contrastive learning framework to adapt the features captured from the well-resourced data to detect rumors from low-resource events, which considers cross-lingual and cross-domain alignment. figure 2 illustrates an overview of our proposed model, which will be depicted in the following subsections.