A major concern with text-based news veracity detection methods is that they may not generalize across countries and cultures. In this short paper, we explicitly test news veracity models across news data from the United States and the United Kingdom, demonstrating there is reason for concern of generalizabilty. Through a series of testing scenarios, we show that text-based classifiers perform poorly when trained on one country's news data and tested on another. Furthermore, these same models have trouble classifying unseen, unreliable news sources. In conclusion, we discuss implications of these results and avenues for future work.
State University of New York at Albany（纽约州立大学奥尔巴尼分校）；College of Emergency Preparedness, Homeland Security and Cybersecurity（应急准备、国土安全与网络安全学院）
Over the past couple of years, the topic of "fake news" and its influence over people's opinions has become a growing cause for concern. Although the spread of disinformation on the Internet is not a new phenomenon, the widespread use of social media has exacerbated its effects, providing more channels for dissemination and the potential to "go viral." Nowhere was this more evident than during the 2016 United States Presidential Election. Although the current of disinformation spread via trolls, bots, and hyperpartisan media outlets likely reinforced existing biases rather than sway undecided …
From Fragments to Facts: A Curriculum-Driven DPO Approach for Generating Hindi News Veracity Explanations
TCS Research（TCS研究）；Department of Computer Science and Engineering, Indian Institute of Technology Patna（印度理工学院帕纳布计算机科学与工程系）；Indian Institute of Technology Patna（印度理工学院帕纳布）；University of Innsbruck（因斯布鲁克大学）
A Graph-Enhanced Defense Framework for Explainable Fake News Detection with LLM
School of Artificial Intelligence, Jilin University（吉林大学人工智能学院）；Hong Kong Baptist University（香港浸会大学）；Guangdong Institute of Smart Education, Jinan University（暨南大学广东智慧教育研究院）；University of Science and Technology Beijing（北京科技大学）；Engineering Research Center of Knowledge-Driven Human-Machine Intelligence（知识驱动人机智能教育部工程研究中心）；International Center of Future Science, Jilin University（吉林大学未来科学国际合作中心）
Explainable Fake News Detection With Large Language Model via Defense Among Competing Wisdom
School of Artificial Intelligence, Jilin University（吉林大学人工智能学院）；Hong Kong Baptist University（香港浸会大学）；Guangdong Institute of Smart Education, Jinan University（暨南大学广东智慧教育研究院）
Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所）；University of Chinese Academy of Sciences（中国科学院大学）；Beijing Institute of Technology（北京理工大学）
The prevalence and perniciousness of fake news has been a critical issue on the Internet, which stimulates the development of automatic fake news detection in turn. In this paper, we focus on the evidence-based fake news detection, where several evidences are utilized to probe the veracity of news (i.e., a claim). Most previous methods first employ sequential models to embed the semantic information and then capture the claim-evidence interaction based on different attention mechanisms. Despite their effectiveness, they still suffer from two main weaknesses. Firstly, due to the inherent drawba…
Event-Centric Human Value Understanding in News-Domain Texts: An Actor-Conditioned, Multi-Granularity Benchmark
Graduate School of Comprehensive Human Sciences, University of Tsukuba, Tsukuba, Japan（茨口大学综合人类科学研究生院）；Intelligent Platforms Research Institute, National Institute of Advanced Industrial Science and Technology, Tokyo, Japan（国家先进工业科学与技术研究院智能平台研究所）；Digital Science Center, University of Innsbruck, Innsbruck, Austria（因斯布鲁克大学数字科学中心）；National Institute of Informatics, Tokyo, Japan（日本信息处理学会）；Institute of Library, Information and Media Science, University of Tsukuba, Tsukuba, Japan（茨口大学图书馆、信息与媒体科学研究所）
Fair Play in the Newsroom: Actor-Based Filtering Gender Discrimination in Text Corpora
Faculty for Computer Science and Mathematics, Hochschule München University of Applied Sciences（慕尼黑应用科学大学计算机科学与数学学院）；Department of Statistics, LMU Munich（慕尼黑大学统计学系）；Munich Center for Machine Learning (MCML), LMU Munich（慕尼黑大学机器学习中心）
Most fact checking models for automatic fake news detection are based on reasoning: given a claim with associated evidence, the models aim to estimate the claim veracity based on the supporting or refuting content within the evidence. When these models perform well, it is generally assumed to be due to the models having learned to reason over the evidence with regards to the claim. In this paper, we investigate this assumption of reasoning, by exploring the relationship and importance of both claim and evidence. Surprisingly, we find on political fact checking datasets that most often the high…
University College London（伦敦大学学院）；University of Copenhagen（哥本哈根大学）
Identifying public misinformation is a complicated and challenging task. An important part of checking the veracity of a specific claim is to evaluate the stance different news sources take towards the assertion. Automatic stance evaluation, i.e. stance detection, would arguably facilitate the process of fact checking. In this paper, we present our stance detection system which claimed third place in Stage 1 of the Fake News Challenge. Despite our straightforward approach, our system performs at a competitive level with the complex ensembles of the top two winning teams. We therefore propose o…
The University of British Columbia（不列颠哥伦比亚大学）；Columbia University（哥伦比亚大学）
Fake news and deceptive machine-generated text are serious problems threatening modern societies, including in the Arab world. This motivates work on detecting false and manipulated stories online. However, a bottleneck for this research is lack of sufficient data to train detection models. We present a novel method for automatically generating Arabic manipulated (and potentially fake) news stories. Our method is simple and only depends on availability of true stories, which are abundant online, and a part of speech tagger (POS). To facilitate future work, we dispense with both of these requir…
Bad Actor, Good Advisor: Exploring the Role of Large Language Models in Fake News Detection
Key Lab of Intelligent Information Processing of Chinese Academy of Sciences（中国科学院智能信息处理重点实验室）；Institute of Computing Technology, Chinese Academy of Sciences（中国科学院计算技术研究所）；University of Chinese Academy of Sciences（中国科学院大学）；National University of Singapore（新加坡国立大学）
Jilin University（吉林大学）；Hong Kong Baptist University（香港浸会大学）
Existing fake news detection methods aim to classify a piece of news as true or false and provide veracity explanations, achieving remarkable performances. However, they often tailor automated solutions on manual fact-checked reports, suffering from limited news coverage and debunking delays. When a piece of news has not yet been fact-checked or debunked, certain amounts of relevant raw reports are usually disseminated on various media outlets, containing the wisdom of crowds to verify the news claim and explain its verdict. In this paper, we propose a novel Coarse-to-fine Cascaded Evidence-Di…
University of Chinese Academy of Sciences（中国科学院大学）；Beihang University（北京航空航天大学）
The wide dissemination of fake news is increasingly threatening both individuals and society. Fake news detection aims to train a model on the past news and detect fake news of the future. Though great efforts have been made, existing fake news detection methods overlooked the unintended entity bias in the real-world data, which seriously influences models' generalization ability to future data. For example, 97\% of news pieces in 2010-2017 containing the entity `Donald Trump' are real in our data, but the percentage falls down to merely 33\% in 2018. This would lead the model trained on the f…
The distribution of fake news is not a new but a rapidly growing problem. The shift to news consumption via social media has been one of the drivers for the spread of misleading and deliberately wrong information, as in addition to it of easy use there is rarely any veracity monitoring. Due to the harmful effects of such fake news on society, the detection of these has become increasingly important. We present an approach to the problem that combines the power of transformer-based language models while simultaneously addressing one of their inherent problems. Our framework, CMTR-BERT, combines…
Indian Institute of Technology, Roorkee（印度理工学院鲁尔基分校）；Graphic Era University（格拉菲克时代大学）
Identifying the veracity of a news article is an interesting problem while automating this process can be a challenging task. Detection of a news article as fake is still an open question as it is contingent on many factors which the current state-of-the-art models fail to incorporate. In this paper, we explore a subtask to fake news identification, and that is stance detection. Given a news article, the task is to determine the relevance of the body and its claim. We present a novel idea that combines the neural, statistical and external features to provide an efficient solution to this probl…
Breaking news leads to situations of fast-paced reporting in social media, producing all kinds of updates related to news stories, albeit with the caveat that some of those early updates tend to be rumours, i.e., information with an unverified status at the time of posting. Flagging information that is unverified can be helpful to avoid the spread of information that may turn out to be false. Detection of rumours can also feed a rumour tracking system that ultimately determines their veracity. In this paper we introduce a novel approach to rumour detection that learns from the sequential dynam…
Experiments in News Bias Detection with Pre-Trained Neural Transformers
Information Access Research Group, Coburg University of Applied Sciences（信息获取研究组、科堡应用科学大学）；University of Sheffield, Department of Computer Science（谢菲尔德大学计算机科学系）
Institute of Computing Technology, Chinese Academy of Sciences（中国科学院计算技术研究所）；University of Chinese Academy of Sciences（中国科学院大学）
To defend against fake news, researchers have developed various methods based on texts. These methods can be grouped as 1) pattern-based methods, which focus on shared patterns among fake news posts rather than the claim itself; and 2) fact-based methods, which retrieve from external sources to verify the claim's veracity without considering patterns. The two groups of methods, which have different preferences of textual clues, actually play complementary roles in detecting fake news. However, few works consider their integration. In this paper, we study the problem of integrating pattern- and…
Debunk and Infer: Multimodal Fake News Detection via Diffusion-Generated Evidence and LLM Reasoning
Sun Yat-sen University（中山大学）；Beihang University（北航）；University of Chinese Academy of Sciences（中国科学院大学）；Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所）；Beijing National Research Center for Information Science and Technology, Tsinghua University（北京信息科学与技术国家研究中心，清华大学）；Department of Automation, Tsinghua University（清华大学自动化系）
Reputed by their low-cost, easy-access, real-time and valuable information, social media also wildly spread unverified or fake news. Rumors can notably cause severe damage on individuals and the society. Therefore, rumor detection on social media has recently attracted tremendous attention. Most rumor detection approaches focus on rumor feature analysis and social features, i.e., metadata in social media. Unfortunately, these features are data-specific and may not always be available, e.g., when the rumor has just popped up and not yet propagated. In contrast, post contents (including images o…
University of Warwick（华威大学）；University of Sheffield（谢菲尔德大学）；University of Duisburg-Essen（杜伊斯堡-埃森大学）；Alan Turing Institute（艾伦·图灵研究所）
Despite the increasing use of social media platforms for information and news gathering, its unmoderated nature often leads to the emergence and spread of rumours, i.e. pieces of information that are unverified at the time of posting. At the same time, the openness of social media platforms provides opportunities to study how users share and discuss rumours, and to explore how natural language processing and data mining techniques may be used to find ways of determining their veracity. In this survey we introduce and discuss two types of rumours that circulate on social media; long-standing ru…
Netaji Subhas University of Technology（尼塔吉·苏巴斯技术大学）
Fake News Detection (FND) is an essential field in natural language processing that aims to identify and check the truthfulness of major claims in a news article to decide the news veracity. FND finds its uses in preventing social, political and national damage caused due to misrepresentation of facts which may harm a certain section of society. Further, with the explosive rise in fake news dissemination over social media, including images and text, it has become imperative to identify fake news faster and more accurately. To solve this problem, this work investigates a novel multimodal stacke…
Robust Fake News Detection using Large Language Models under Adversarial Sentiment Attacks
TIB – Leibniz Information Centre for Science；TIB -- Leibniz Information Centre for Science；Marburg University \& Hessian Center for Artificial Intelligence (hessian.AI) Marburg/Darmstadt；\ TIB \& L3S Research Center Hannover Germany
Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所）；School of Artificial Intelligence, University of Chinese Academy of Sciences（中国科学院大学人工智能学院）
The prevalence and perniciousness of fake news have been a critical issue on the Internet, which stimulates the development of automatic fake news detection in turn. In this paper, we focus on evidence-based fake news detection, where several evidences are utilized to probe the veracity of news (i.e., a claim). Most previous methods first employ sequential models to embed the semantic information and then capture the claim-evidence interaction based on attention mechanisms. Despite their effectiveness, they still suffer from three weaknesses. Firstly, sequential models fail to integrate the re…
The Jheronimus Academy of Data Science（杰罗尼莫斯数据科学学院）；University of Sheffield（谢菲尔德大学）
Stance detection in fake news is an important component in news veracity assessment because this process helps fact-checking by understanding stance to a central claim from different information sources. The Fake News Challenge Stage 1 (FNC-1) held in 2017 was setup for this purpose, which involves estimating the stance of a news article body relative to a given headline. This thesis starts from the error analysis for the three top-performing systems in FNC-1. Based on the analysis, a simple but tough-to-beat Multilayer Perceptron system is chosen as the baseline. Afterwards, three approaches …
School of ECE, College of Engineering, University of Tehran（德黑兰大学工程学院电气与计算机工程学院）
Data is published on the web over time in great volumes, but majority of the data is unstructured, making it hard to understand and difficult to interpret. Information Extraction (IE) methods obtain structured information from unstructured data. One of the challenging IE tasks is Event Extraction (EE) which seeks to derive information about specific incidents and their actors from the text. EE is useful in many domains such as building a knowledge base, information retrieval and summarization. In the past decades, some event ontologies like ACE, CAMEO and ICEWS were developed to define event f…
Rhetorical Frames in AI can be thought of as expressions that describe AI development as a competition between two or more actors, such as governments or companies. Examples of such Frames include robotic arms race, AI rivalry, technological supremacy, cyberwarfare dominance and 5G race. Detection of Rhetorical Frames from open sources can help us track the attitudes of governments or companies towards AI, specifically whether attitudes are becoming more cooperative or competitive over time. Given the rapidly increasing volumes of open sources (online news media, twitter, blogs), it is difficu…
