Correcting misinformation on social media with a large language model

Misinformation undermines public trust in science and democracy, particularly on social media where inaccuracies can spread rapidly. Experts and laypeople have shown to be effective in correcting misinformation by manually identifying and explaining inaccuracies. Nevertheless, this approach is difficult to scale, a concern as technologies like large language models (LLMs) make misinformation easier to produce. LLMs also have versatile capabilities that could accelerate misinformation correction; however, they struggle due to a lack of recent information, a tendency to produce plausible but false content and references, and limitations in addressing multimodal information. To address these issues, we propose MUSE, an LLM augmented with access to and credibility evaluation of up-to-date information. By retrieving contextual evidence and refutations, MUSE can provide accurate and trustworthy explanations and references. It also describes visuals and conducts multimodal searches for correcting multimodal misinformation. We recruit fact-checking and journalism experts to evaluate corrections to real social media posts across 13 dimensions, ranging from the factuality of explanation to the relevance of references. The results demonstrate MUSE's ability to correct misinformation promptly after appearing on social media; overall, MUSE outperforms GPT-4 by 37% and even high-quality corrections from laypeople by 29%. This work underscores the potential of LLMs to combat real-world misinformation effectively and efficiently.

3D-Printed Hydraulic Fluidic Logic Circuitry for Soft Robots

Fluidic logic circuitry analogous to its electric counterpart could potentially provide soft robots with machine intelligence due to its supreme adaptability, dexterity, and seamless compatibility using state-of-the-art additive manufacturing processes. However, conventional microfluidic channel based circuitry suffers from limited driving force, while macroscopic pneumatic logic lacks timely responsivity and desirable accuracy. Producing heavy duty, highly responsive and integrated fluidic soft robotic circuitry for control and actuation purposes for biomedical applications has yet to be accomplished in a hydraulic manner. Here, we present a 3D printed hydraulic fluidic half-adder system, composing of three basic hydraulic fluidic logic building blocks: AND, OR, and NOT gates. Furthermore, a hydraulic soft robotic half-adder system is implemented using an XOR operation and modified dual NOT gate system based on an electrical oscillator structure. This half-adder system possesses binary arithmetic capability as a key component of arithmetic logic unit in modern computers. With slight modifications, it can realize the control over three different directions of deformation of a three degree-of-freedom soft actuation mechanism solely by changing the states of the two fluidic inputs. This hydraulic fluidic system utilizing a small number of inputs to control multiple distinct outputs, can alter the internal state of the circuit solely based on external inputs, holding significant promises for the development of microfluidics, fluidic logic, and intricate internal systems of untethered soft robots with machine intelligence.

ChatGPT is a Potential Zero-Shot Dependency Parser

Pre-trained language models have been widely used in dependency parsing task and have achieved significant improvements in parser performance. However, it remains an understudied question whether pre-trained language models can spontaneously exhibit the ability of dependency parsing without introducing additional parser structure in the zero-shot scenario. In this paper, we propose to explore the dependency parsing ability of large language models such as ChatGPT and conduct linguistic analysis. The experimental results demonstrate that ChatGPT is a potential zero-shot dependency parser, and the linguistic analysis also shows some unique preferences in parsing outputs.

Temporal Interest Network for Click-Through Rate Prediction

The history of user behaviors constitutes one of the most significant characteristics in predicting the click-through rate (CTR), owing to their strong semantic and temporal correlation with the target item. While the literature has individually examined each of these correlations, research has yet to analyze them in combination, that is, the quadruple correlation of (behavior semantics, target semantics, behavior temporal, and target temporal). The effect of this correlation on performance and the extent to which existing methods learn it remain unknown. To address this gap, we empirically measure the quadruple correlation and observe intuitive yet robust quadruple patterns. We measure the learned correlation of several representative user behavior methods, but to our surprise, none of them learn such a pattern, especially the temporal one. In this paper, we propose the Temporal Interest Network (TIN) to capture the quadruple semantic and temporal correlation between behaviors and the target. We achieve this by incorporating target-aware temporal encoding, in addition to semantic embedding, to represent behaviors and the target. Furthermore, we deploy target-aware attention, along with target-aware representation, to explicitly conduct the 4-way interaction. We performed comprehensive evaluations on the Amazon and Alibaba datasets. Our proposed TIN outperforms the best-performing baselines by 0.43\% and 0.29\% on two datasets, respectively. Comprehensive analysis and visualization show that TIN is indeed capable of learning the quadruple correlation effectively, while all existing methods fail to do so. We provide our implementation of TIN in Tensorflow.

Linguistic-style-aware Neural Networks for Fake News Detection

We propose the hierarchical recursive neural network (HERO) to predict fake news by learning its linguistic style, which is distinguishable from the truth, as psychological theories reveal. We first generate the hierarchical linguistic tree of news documents; by doing so, we translate each news document's linguistic style into its writer's usage of words and how these words are recursively structured as phrases, sentences, paragraphs, and, ultimately, the document. By integrating the hierarchical linguistic tree with the neural network, the proposed method learns and classifies the representation of news documents by capturing their locally sequential and globally recursive structures that are linguistically meaningful. It is the first work offering the hierarchical linguistic tree and the neural network preserving the tree information to our best knowledge. Experimental results based on public real-world datasets demonstrate the proposed method's effectiveness, which can outperform state-of-the-art techniques in classifying short and long news documents. We also examine the differential linguistic style of fake news and the truth and observe some patterns of fake news. The code and data have been publicly available.

From Fake News to #FakeNews: Mining Direct and Indirect Relationships among Hashtags for Fake News Detection

The COVID-19 pandemic has gained worldwide attention and allowed fake news, such as ``COVID-19 is the flu,'' to spread quickly and widely on social media. Combating this coronavirus infodemic demands effective methods to detect fake news. To this end, we propose a method to infer news credibility from hashtags involved in news dissemination on social media, motivated by the tight connection between hashtags and news credibility observed in our empirical analyses. We first introduce a new graph that captures all (direct and \textit{indirect}) relationships among hashtags. Then, a language-independent semi-supervised algorithm is developed to predict fake news based on this constructed graph. This study first investigates the indirect relationship among hashtags; the proposed approach can be extended to any homogeneous graph to capture a comprehensive relationship among nodes. Language independence opens the proposed method to multilingual fake news detection. Experiments conducted on two real-world datasets demonstrate the effectiveness of our approach in identifying fake news, especially at an \textit{early} stage of propagation.

"This is Fake! Shared it by Mistake": Assessing the Intent of Fake News Spreaders

Individuals can be misled by fake news and spread it unintentionally without knowing it is false. This phenomenon has been frequently observed but has not been investigated. Our aim in this work is to assess the intent of fake news spreaders. To distinguish between intentional versus unintentional spreading, we study the psychological explanations of unintentional spreading. With this foundation, we then propose an influence graph, using which we assess the intent of fake news spreaders. Our extensive experiments show that the assessed intent can help significantly differentiate between intentional and unintentional fake news spreaders. Furthermore, the estimated intent can significantly improve the current techniques that detect fake news. To our best knowledge, this is the first work to model individuals' intent in fake news spreading.

SAFE: Similarity-Aware Multi-Modal Fake News Detection

Effective detection of fake news has recently attracted significant attention. Current studies have made significant contributions to predicting fake news with less focus on exploiting the relationship (similarity) between the textual and visual information in news articles. Attaching importance to such similarity helps identify fake news stories that, for example, attempt to use irrelevant images to attract readers' attention. In this work, we propose a $\mathsf{S}$imilarity-$\mathsf{A}$ware $\mathsf{F}$ak$\mathsf{E}$ news detection method ($\mathsf{SAFE}$) which investigates multi-modal (textual and visual) information of news articles. First, neural networks are adopted to separately extract textual and visual features for news representation. We further investigate the relationship between the extracted features across modalities. Such representations of news textual and visual information along with their relationship are jointly learned and used to predict fake news. The proposed method facilitates recognizing the falsity of news articles based on their text, images, or their "mismatches." We conduct extensive experiments on large-scale real-world data, which demonstrate the effectiveness of the proposed method.

Credibility-based Fake News Detection

Fake news can significantly misinform people who often rely on online sources and social media for their information. Current research on fake news detection has mostly focused on analyzing fake news content and how it propagates on a network of users. In this paper, we emphasize the detection of fake news by assessing its credibility. By analyzing public fake news data, we show that information on news sources (and authors) can be a strong indicator of credibility. Our findings suggest that an author's history of association with fake news, and the number of authors of a news article, can play a significant role in detecting fake news. Our approach can help improve traditional fake news detection methods, wherein content features are often used to detect fake news.

Fake News Early Detection: A Theory-driven Model

The explosive growth of fake news and its erosion of democracy, justice, and public trust has significantly increased the demand for accurate fake news detection. Recent advancements in this area have proposed novel techniques that aim to detect fake news by exploring how it propagates on social networks. However, to achieve fake news early detection, one is only provided with limited to no information on news propagation; hence, motivating the need to develop approaches that can detect fake news by focusing mainly on news content. In this paper, a theory-driven model is proposed for fake news detection. The method investigates news content at various levels: lexicon-level, syntax-level, semantic-level and discourse-level. We represent news at each level, relying on well-established theories in social and forensic psychology. Fake news detection is then conducted within a supervised machine learning framework. As an interdisciplinary research, our work explores potential fake news patterns, enhances the interpretability in fake news feature engineering, and studies the relationships among fake news, deception/disinformation, and clickbaits. Experiments conducted on two real-world datasets indicate that the proposed method can outperform the state-of-the-art and enable fake news early detection, even when there is limited content information.