Related papers: HoaxItaly: a collection of Italian disinformation and fact-checking stories shared on Twitter in 2019

HoaxItaly: a collection of Italian disinformation and fact-checking stories shared on Twitter in 2019

URL: http://arxiv.org/abs/2001.10926v1
Date: Wed, 29 Jan 2020 16:14:47 GMT
Title: HoaxItaly: a collection of Italian disinformation and fact-checking stories shared on Twitter in 2019
Authors: Francesco Pierri, Alessandro Artoni, Stefano Ceri
Abstract summary: The dataset includes also title and body for approximately 37k news articles. It is publicly available at https://doi.org/10.79DVN/ PGVDHX.
Score: 72.96986027203377
License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Abstract: We released over 1 million tweets shared during 2019 and containing links to thousands of news articles published on two classes of Italian outlets: (1) disinformation websites, i.e. outlets which have been repeatedly flagged by journalists and fact-checkers for producing low-credibility content such as false news, hoaxes, click-bait, misleading and hyper-partisan stories; (2) fact-checking websites which notably debunk and verify online news and claims. The dataset, which includes also title and body for approximately 37k news articles, is publicly available at https://doi.org/10.7910/DVN/ PGVDHX.

Related papers

3DLNews: A Three-decade Dataset of US Local News Articles [49.1574468325115]
3DLNews is a novel dataset with local news articles from the United States spanning the period from 1996 to 2024. It contains almost 1 million URLs (with HTML text) from over 14,000 local newspapers, TV, and radio stations across all 50 states.
arXiv Detail & Related papers (2024-08-08T18:33:37Z)
Machine-Made Media: Monitoring the Mobilization of Machine-Generated Articles on Misinformation and Mainstream News Websites [5.161088104035108]
We train a DeBERTa-based synthetic news detector and classify over 15.46 million articles from 3,074 misinformation and mainstream news websites. We find that between January 1, 2022, and May 1, 2023, the relative number of synthetic news articles increased by 57.3% on mainstream websites while increasing by 474% on misinformation sites.
arXiv Detail & Related papers (2023-05-16T21:51:01Z)
Fake News Detection Tools and Methods -- A Review [0.0]
We discuss the recent literature about different approaches to detect fake news over the Internet. We highlight the various publicly available datasets and various online tools that are available and can debunk Fake News in real-time.
arXiv Detail & Related papers (2021-11-21T13:19:23Z)
Cross-lingual COVID-19 Fake News Detection [54.125563009333995]
We make the first attempt to detect COVID-19 misinformation in a low-resource language (Chinese) only using the fact-checked news in a high-resource language (English) We propose a deep learning framework named CrossFake to jointly encode the cross-lingual news body texts and capture the news content. Empirical results on our dataset demonstrate the effectiveness of CrossFake under the cross-lingual setting.
arXiv Detail & Related papers (2021-10-13T04:44:02Z)
Misinfo Belief Frames: A Case Study on Covid & Climate News [49.979419711713795]
We propose a formalism for understanding how readers perceive the reliability of news and the impact of misinformation. We introduce the Misinfo Belief Frames (MBF) corpus, a dataset of 66k inferences over 23.5k headlines. Our results using large-scale language modeling to predict misinformation frames show that machine-generated inferences can influence readers' trust in news headlines.
arXiv Detail & Related papers (2021-04-18T09:50:11Z)
The Rise and Fall of Fake News sites: A Traffic Analysis [62.51737815926007]
We investigate the online presence of fake news websites and characterize their behavior in comparison to real news websites. Based on our findings, we build a content-agnostic ML for automatic detection of fake news websites.
arXiv Detail & Related papers (2021-03-16T18:10:22Z)
Where Are the Facts? Searching for Fact-checked Information to Alleviate the Spread of Fake News [9.68145635795782]
We propose a novel framework to search for fact-checking articles, which address the content of an original tweet (that may contain misinformation) posted by online users. The search can directly warn fake news posters and online users about misinformation, discourage them from spreading fake news, and scale up verified content on social media. Our framework uses both text and images to search for fact-checking articles, and achieves promising results on real-world datasets.
arXiv Detail & Related papers (2020-10-07T04:55:34Z)
Fake News Spreader Detection on Twitter using Character N-Grams. Notebook for PAN at CLEF 2020 [0.0]
This notebook describes our profiling system for the fake news detection task on Twitter. We conduct different feature extraction techniques and learning experiments from a multilingual perspective. Our models achieve an overall accuracy of 73% and 79% on the English and Spanish official test set.
arXiv Detail & Related papers (2020-09-29T08:32:32Z)
Can We Spot the "Fake News" Before It Was Even Written? [25.536546272915427]
A number of fact-checking initiatives have been launched so far, both manual and automatic. An arguably more promising direction is to focus on fact-checking entire news outlets, which can be done in advance. We describe how we do this in the Tanbih news aggregator, which makes readers aware of what they are reading.
arXiv Detail & Related papers (2020-08-10T19:21:06Z)
365 Dots in 2019: Quantifying Attention of News Sources [69.50862982117125]
We measure the overlap of topics of online news articles from a variety of sources. We score news stories according to the degree of attention in near-real time. This can enable multiple studies, including identifying topics that receive the most attention.
arXiv Detail & Related papers (2020-03-22T20:32:47Z)

This list is automatically generated from the titles and abstracts of the papers in this site.