On Unifying Misinformation Detection (2021)

Abstract: In this paper, we introduce UnifiedM2, a general-purpose misinformation model that jointly models multiple domains of misinformation with a single, unified setup. The model is trained to handle four tasks: detecting news bias, clickbait, fake news, and verifying rumors. By grouping these tasks together, UnifiedM2 learns a richer representation of misinformation, which leads to state-of-the-art or comparable performance across all tasks. Furthermore, we demonstrate that UnifiedM2’s learned representation is helpful for few-shot learning of unseen misinformation tasks/datasets and the model’s generalizability to unseen events.

## introduction on any given day, 2.5 quintillion bytes of information are created on the internet, a figure that is only expected to increase in the coming years @xcite . the internet has allowed information to spread rapidly, and studies have found that misinformation spreads quicker and more broadly than true information @xcite . it is thus paramount for misinformation detection approaches to be able to adapt to new, emerging problems in real time, without waiting for thousands of training examples to be collected. in other words, the generalizability of such systems is essential. misinformation detection is not well-studied from a generalizability standpoint. misinformation can manifest in different forms and domains, i.e., fake news, clickbait, and false rumors, and previous literature has mostly focused on building specialized models for a single domain @xcite @xcite . (even prior literature on multi-tasking for misinformation @xcite focuses more on figure 1 : architecture of our unifiedm2 model using auxiliary tasks to boost performance on a single task, rather than on all tasks.) however, though these domains may differ in format (long articles vs. short headlines and tweets) and exact objective ("is this fake" vs. "is this clickbait"), they have the same ultimate goal of deceiving their readers. as a result, their content often exhibits similar linguistic characteristics, such as using a sensational style to incite curiosity or strong emotional responses from readers. furthermore, models trained on multiple tasks are more robust and less prone to overfitting to spurious domain-specific correlations. thus, unifying various domains of misinformation allows us to build a generalizable model that performs well across multiple domains/formats of misinformation. in this work, we propose unified misinfo model (unifiedm2), a misinformation detection model that uses multi-task learning @xcite @xcite to train on different domains of misinformation. through a comprehensive series of empirical evaluations, we demonstrate that our approach is effective on all tasks that we train on, improving f 1 in some cases by an absolute ∼8%. moreover, we conduct ablation studies to more precisely characterize how such positive transfer is attained. beyond improvements on seen datasets, we examine the generalizability of our proposed approach to unseen tasks/datasets and events. this is highly applicable to real-world use cases, where obtaining new misinformation labels is costly and systems often wish to take down misinformation in real time. our experimental results indicate that our unified representation has better generalization ability over other baselines. ## unifiedm2 in this section, we describe the architecture and the training details for our proposed unifiedm2 model. ## experiment here, we provide experimental details (dataset, baselines, experimental setups) and results that empirically show the success of the proposed unifiedm2 model. ## generalizability analysis new types, domains, and subjects of misinformation arise frequently. promptly responding to these new sources is challenging, as they can spread widely before there is time to collect sufficient task-specific training examples. for instance, the rapid spread of covid-19 was accompanied by equally fast spread of large quantities of misinformation @xcite . 3 other datasets show similar findings. therefore, we carry out experiments to evaluate the generalization ability of unifiedm2 representation to unseen misinformation (i) tasks/datasets and (ii) events. the first experiment is about fast adaption ability (few-shot training) to handle a new task/dataset, whereas the second experiment is about the model's ability to perform well on events unseen during training. ## related work existing misinformation works take three main approaches: content-based approaches examine the language of a document only. prior works have looked at linguistic features such as hedging words and emotional words @xcite @xcite . fact-based approaches leverage evidence from external sources (e.g., wikipedia, web) to determine the truthfulness of the information @xcite @xcite @xcite . finally, social-data-based approaches use the surrounding social data-such as the credibility of the authors of the information @xcite @xcite or social engagement data @xcite @xcite . though prior works have explored multi-task learning within misinformation, they have focused exclusively on one domain. these works try to predict two different labels on the same set of examples from a single @xcite or two closely-related datasets @xcite . in contrast, our proposed approach crosses not just task or dataset boundaries, but also format and domain boundaries. furthermore, prior works focus on using an auxiliary task to boost the performance of the main task, while we focus on using multitasking to generalize across many domains. thus, the focus of this work is not the multitask paradigm, but rather the unification of the various domains, using multitasking. ## conclusion in this paper, we introduced unifiedm2, which unifies multiple domains of misinformation with a single multi-task learning setup. we empirically showed that such unification improves the model's performance against strong baselines, and achieves new state-of-the-art results. furthermore, we show that unifiedm2 can generalize to out-of-domain misinformation tasks and events, and thus can serve as a good starting point for others working on misinformation.