Fake or Real? A Study of Arabic Satirical Fake News

Research Group in Computational Linguistics（计算语言学研究小组）；University of Wolverhampton, UK（英国沃尔夫汉普顿大学）；Centre for Translation Studies（翻译研究中心）；University of Surrey, UK（英国萨里大学）

Hadeel Saadany, Emad Mohamed, Constantin Orasan

备注：: 11 pages
发表信息：: Proceedings of the 3rd International Workshop on Rumours and Deception in Social Media (RDSM) 2020

阅读 PDF ↗

arXiv 页面 ↗HTML 原文 ↗

英文摘要相关论文

英文摘要：

One very common type of fake news is satire which comes in a form of a news website or an online platform that parodies reputable real news agencies to create a sarcastic version of reality. This type of fake news is often disseminated by individuals on their online platforms as it has a much stronger effect in delivering criticism than through a straightforward message. However, when the satirical text is disseminated via social media without mention of its source, it can be mistaken for real news. This study conducts several exploratory analyses to identify the linguistic properties of Arabic fake news with satirical content. We exploit these features to build a number of machine learning models capable of identifying satirical fake news with an accuracy of up to 98.6%.

机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。

阅读原文

阅读 PDF ↗

arXiv 页面 ↗HTML 原文 ↗

本文内容

英文摘要相关论文

机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。

继续探索

相关论文

用于阿拉伯语和英语讽刺新闻检测的多语言与双语模型比较

Comparison of Multilingual and Bilingual Models for Satirical News Detection of Arabic and English

University of New South Wales（新南威尔士大学）

本研究比较了Jais-chat和LLaMA-2-chat模型在阿拉伯语和英语讽刺新闻检测中的表现，发现思维链提示能显著提升Jais-chat的检测效果，其英语F1分数达80%，凸显了结构化推理在复杂任务中的重要性。

2024-11-16cs.CLcs.CR

Arabic Fake News Detection Based on Deep Contextualized Embedding Models

University of Sharjah（沙迦大学）；Western University（韦仕敦大学）

Social media is becoming a source of news for many people due to its ease and freedom of use. As a result, fake news has been spreading quickly and easily regardless of its credibility, especially in the last decade. Fake news publishers take advantage of critical situations such as the Covid-19 pandemic and the American presidential elections to affect societies negatively. Fake news can seriously impact society in many fields including politics, finance, sports, etc. Many studies have been conducted to help detect fake news in English, but research conducted on fake news detection in the Ara…

2022-05-06cs.AIcs.CL

Machine Generation and Detection of Arabic Manipulated and Fake News

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…

2020-11-05cs.CLcs.LG

AraCOVID19-MFH: Arabic COVID-19 Multi-label Fake News and Hate Speech Detection Dataset

Research Centre on Scientific and Technical Information (CERIST)（科学技术信息研究中心）

Along with the COVID-19 pandemic, an "infodemic" of false and misleading information has emerged and has complicated the COVID-19 response efforts. Social networking sites such as Facebook and Twitter have contributed largely to the spread of rumors, conspiracy theories, hate, xenophobia, racism, and prejudice. To combat the spread of fake news, researchers around the world have and are still making considerable efforts to build and share COVID-19 related research articles, models, and datasets. This paper releases "AraCOVID19-MFH" a manually annotated multi-label Arabic COVID-19 fake news and…

2021-05-07cs.AIcs.CL

A Second Pandemic? Analysis of Fake News About COVID-19 Vaccines in Qatar

Qatar Computing Research Institute, HBKU（卡塔尔计算研究所（哈马德·本·哈利法大学））；University of Padova（帕多瓦大学）

While COVID-19 vaccines are finally becoming widely available, a second pandemic that revolves around the circulation of anti-vaxxer fake news may hinder efforts to recover from the first one. With this in mind, we performed an extensive analysis of Arabic and English tweets about COVID-19 vaccines, with focus on messages originating from Qatar. We found that Arabic tweets contain a lot of false information and rumors, while English tweets are mostly factual. However, English tweets are much more propagandistic than Arabic ones. In terms of propaganda techniques, about half of the Arabic tweet…

2021-09-22cs.CLcs.SI

Overview of the CLEF--2021 CheckThat! Lab on Detecting Check-Worthy Claims, Previously Fact-Checked Claims, and Fake News

Qatar Computing Research Institute HBKU（卡塔尔计算研究所（HBKU））；University of Padova（帕多瓦大学）；Qatar University（卡塔尔大学）；DIT Università di Bologna（博洛尼亚大学DIT学院）；Newtral Media Audiovisual；Sofia University（索非亚大学）；University of Duisburg-Essen（杜伊斯堡-埃森大学）；University of Applied Sciences Potsdam（波茨坦应用科学大学）；University of Hildesheim（希尔德斯海姆大学）；TOBB University of Economics and Technology（托普卡帕经济与技术大学）

We describe the fourth edition of the CheckThat! Lab, part of the 2021 Conference and Labs of the Evaluation Forum (CLEF). The lab evaluates technology supporting tasks related to factuality, and covers Arabic, Bulgarian, English, Spanish, and Turkish. Task 1 asks to predict which posts in a Twitter stream are worth fact-checking, focusing on COVID-19 and politics (in all five languages). Task 2 asks to determine whether a claim in a tweet can be verified using a set of previously fact-checked claims (in Arabic and English). Task 3 asks to predict the veracity of a news article and its topical…

2021-09-23cs.CLcs.IRcs.LG

更多相关论文收起更多论文

LLM 是否足以检测极端党派、虚假、两极分化和有害内容？评估上下文学习与微调

Are LLMs Enough for Hyperpartisan, Fake, Polarized and Harmful Content Detection? Evaluating In-Context Learning vs. Fine-Tuning

本研究系统比较了微调与上下文学习在检测虚假、极端党派和有害内容上的效果，发现微调优于上下文学习，即使模型更小，也胜过大型模型的上下文学习。

2025-09-09cs.AIcs.CL