Exploiting User Comments for Early Detection of Fake News Prior to Users' Commenting

Both accuracy and timeliness are key factors in detecting fake news on social media. However, most existing methods encounter an accuracy-timeliness dilemma: Content-only methods guarantee timeliness but perform moderately because of limited available information, while social context-based ones generally perform better but inevitably lead to latency because of social context accumulation needs. To break such a dilemma, a feasible but not well-studied solution is to leverage social contexts (e.g., comments) from historical news for training a detection model and apply it to newly emerging news without social contexts. This requires the model to (1) sufficiently learn helpful knowledge from social contexts, and (2) be well compatible with situations that social contexts are available or not. To achieve this goal, we propose to absorb and parameterize useful knowledge from comments in historical news and then inject it into a content-only detection model. Specifically, we design the Comments Assisted Fake News Detection method (CAS-FEND), which transfers useful knowledge from a comments-aware teacher model to a content-only student model during training. The student model is further used to detect newly emerging fake news. Experiments show that the CAS-FEND student model outperforms all content-only methods and even those with 1/4 comments as inputs, demonstrating its superiority for early detection.

Online Misinformation Video Detection: A Survey

With information consumption via online video streaming becoming increasingly popular, misinformation video poses a new threat to the health of the online information ecosystem. Though previous studies have made much progress in detecting misinformation in text and image formats, video-based misinformation brings new and unique challenges to automatic detection systems: 1) high information heterogeneity brought by various modalities, 2) blurred distinction between misleading video manipulation and ubiquitous artistic video editing, and 3) new patterns of misinformation propagation due to the dominant role of recommendation systems on online video platforms. To facilitate research on this challenging task, we conduct this survey to present advances in misinformation video detection research. We first analyze and characterize the misinformation video from three levels including signals, semantics, and intents. Based on the characterization, we systematically review existing works for detection from features of various modalities to techniques for clue integration. We also introduce existing resources including representative datasets and widely used tools. Besides summarizing existing studies, we discuss related areas and outline open issues and future directions to encourage and guide more research on misinformation video detection. Our corresponding public repository is available at https://github.com/ICTMCG/Awesome-Misinfo-Video-Detection.

Improving Fake News Detection of Influential Domain via Domain- and Instance-Level Transfer

Both real and fake news in various domains, such as politics, health, and entertainment are spread via online social media every day, necessitating fake news detection for multiple domains. Among them, fake news in specific domains like politics and health has more serious potential negative impacts on the real world (e.g., the infodemic led by COVID-19 misinformation). Previous studies focus on multi-domain fake news detection, by equally mining and modeling the correlation between domains. However, these multi-domain methods suffer from a seesaw problem: the performance of some domains is often improved at the cost of hurting the performance of other domains, which could lead to an unsatisfying performance in specific domains. To address this issue, we propose a Domain- and Instance-level Transfer Framework for Fake News Detection (DITFEND), which could improve the performance of specific target domains. To transfer coarse-grained domain-level knowledge, we train a general model with data of all domains from the meta-learning perspective. To transfer fine-grained instance-level knowledge and adapt the general model to a target domain, we train a language model on the target domain to evaluate the transferability of each data instance in source domains and re-weigh each instance's contribution. Offline experiments on two datasets demonstrate the effectiveness of DITFEND. Online experiments show that DITFEND brings additional improvements over the base models in a real-world scenario.

Characterizing Multi-Domain False News and Underlying User Effects on Chinese Weibo

False news that spreads on social media has proliferated over the past years and has led to multi-aspect threats in the real world. While there are studies of false news on specific domains (like politics or health care), little work is found comparing false news across domains. In this article, we investigate false news across nine domains on Weibo, the largest Twitter-like social media platform in China, from 2009 to 2019. The newly collected data comprise 44,728 posts in the nine domains, published by 40,215 users, and reposted over 3.4 million times. Based on the distributions and spreads of the multi-domain dataset, we observe that false news in domains that are close to daily life like health and medicine generated more posts but diffused less effectively than those in other domains like politics, and that political false news had the most effective capacity for diffusion. The widely diffused false news posts on Weibo were associated strongly with certain types of users -- by gender, age, etc. Further, these posts provoked strong emotions in the reposts and diffused further with the active engagement of false-news starters. Our findings have the potential to help design false news detection systems in suspicious news discovery, veracity prediction, and display and explanation. The comparison of the findings on Weibo with those of existing work demonstrates nuanced patterns, suggesting the need for more research on data from diverse platforms, countries, or languages to tackle the global issue of false news. The code and new anonymized dataset are available at https://github.com/ICTMCG/Characterizing-Weibo-Multi-Domain-False-News.

A Prompting-based Approach for Adversarial Example Generation and Robustness Enhancement

Recent years have seen the wide application of NLP models in crucial areas such as finance, medical treatment, and news media, raising concerns of the model robustness and vulnerabilities. In this paper, we propose a novel prompt-based adversarial attack to compromise NLP models and robustness enhancement technique. We first construct malicious prompts for each instance and generate adversarial examples via mask-and-filling under the effect of a malicious purpose. Our attack technique targets the inherent vulnerabilities of NLP models, allowing us to generate samples even without interacting with the victim NLP model, as long as it is based on pre-trained language models (PLMs). Furthermore, we design a prompt-based adversarial training method to improve the robustness of PLMs. As our training method does not actually generate adversarial samples, it can be applied to large-scale training sets efficiently. The experimental results show that our attack method can achieve a high attack success rate with more diverse, fluent and natural adversarial examples. In addition, our robustness enhancement method can significantly improve the robustness of models to resist adversarial attacks. Our work indicates that prompting paradigm has great potential in probing some fundamental flaws of PLMs and fine-tuning them for downstream tasks.

DRAG: Dynamic Region-Aware GCN for Privacy-Leaking Image Detection

The daily practice of sharing images on social media raises a severe issue about privacy leakage. To address the issue, privacy-leaking image detection is studied recently, with the goal to automatically identify images that may leak privacy. Recent advance on this task benefits from focusing on crucial objects via pretrained object detectors and modeling their correlation. However, these methods have two limitations: 1) they neglect other important elements like scenes, textures, and objects beyond the capacity of pretrained object detectors; 2) the correlation among objects is fixed, but a fixed correlation is not appropriate for all the images. To overcome the limitations, we propose the Dynamic Region-Aware Graph Convolutional Network (DRAG) that dynamically finds out crucial regions including objects and other important elements, and models their correlation adaptively for each input image. To find out crucial regions, we cluster spatially-correlated feature channels into several region-aware feature maps. Further, we dynamically model the correlation with the self-attention mechanism and explore the interaction among the regions with a graph convolutional network. The DRAG achieved an accuracy of 87% on the largest dataset for privacy-leaking image detection, which is 10 percentage points higher than the state of the art. The further case study demonstrates that it found out crucial regions containing not only objects but other important elements like textures.

Prompt Learning for Few-Shot Dialogue State Tracking

Collecting dialogue state labels, slots and values, for learning dialogue state tracking (DST) models can be costly, especially with the wide application of dialogue systems in new-rising domains. In this paper, we focus on how to learn a DST model efficiently with limited labeled data. We design a prompt learning framework for few-shot DST, which consists of two main components: value-based prompt and inverse prompt mechanism. This framework aims to utilize the language understanding and generation ability of pre-trained language models (PLM). First, we design value-based prompt functions to probe the DST-related knowledge from PLM, which do not rely on the known ontology of slots. Further, an inverse prompt mechanism is utilized to self-check the "prompted" knowledge and help the PLM understand the essence of DST task further. Experiments show that our model can generate unseen slots and outperforms existing state-of-the-art few-shot methods. It indicates that DST-related knowledge can be probed from PLM and utilized to address low-resource DST efficiently with the help of prompt learning.

Quantifying Robustness to Adversarial Word Substitutions

Deep-learning-based NLP models are found to be vulnerable to word substitution perturbations. Before they are widely adopted, the fundamental issues of robustness need to be addressed. Along this line, we propose a formal framework to evaluate word-level robustness. First, to study safe regions for a model, we introduce robustness radius which is the boundary where the model can resist any perturbation. As calculating the maximum robustness radius is computationally hard, we estimate its upper and lower bound. We repurpose attack methods as ways of seeking upper bound and design a pseudo-dynamic programming algorithm for a tighter upper bound. Then verification method is utilized for a lower bound. Further, for evaluating the robustness of regions outside a safe radius, we reexamine robustness from another view: quantification. A robustness metric with a rigorous statistical guarantee is introduced to measure the quantification of adversarial examples, which indicates the model's susceptibility to perturbations outside the safe radius. The metric helps us figure out why state-of-the-art models like BERT can be easily fooled by a few word substitutions, but generalize well in the presence of real-world noises.

MDFEND: Multi-domain Fake News Detection

Fake news spread widely on social media in various domains, which lead to real-world threats in many aspects like politics, disasters, and finance. Most existing approaches focus on single-domain fake news detection (SFND), which leads to unsatisfying performance when these methods are applied to multi-domain fake news detection. As an emerging field, multi-domain fake news detection (MFND) is increasingly attracting attention. However, data distributions, such as word frequency and propagation patterns, vary from domain to domain, namely domain shift. Facing the challenge of serious domain shift, existing fake news detection techniques perform poorly for multi-domain scenarios. Therefore, it is demanding to design a specialized model for MFND. In this paper, we first design a benchmark of fake news dataset for MFND with domain label annotated, namely Weibo21, which consists of 4,488 fake news and 4,640 real news from 9 different domains. We further propose an effective Multi-domain Fake News Detection Model (MDFEND) by utilizing a domain gate to aggregate multiple representations extracted by a mixture of experts. The experiments show that MDFEND can significantly improve the performance of multi-domain fake news detection. Our dataset and code are available at https://github.com/kennqiang/MDFEND-Weibo21.

DPUV3INT8: A Compiler View to programmable FPGA Inference Engines

We have a FPGA design, we make it fast, efficient, and tested for a few important examples. Now we must infer a general solution to deploy in the data center. Here, we describe the FPGA DPUV3INT8 design and our compiler effort. The hand-tuned SW-HW solution for Resnet50\_v1 has (close to) 2 times better images per second (throughput) than our best FPGA implementation; the compiler generalizes the hand written techniques achieving about 1.5 times better performance for the same example, the compiler generalizes the optimizations to a model zoo of networks, and it achieves 80+\% HW efficiency.