DOI: 10.5281/zenodo.4714517
title: CT-FAN-21 corpus: A dataset for Fake News Detection

description: <strong>Data Access: </strong>The data in the research collection provided may only be used for research purposes. Portions of the data are copyrighted and have commercial value as data, so you must be careful to use it only for research purposes. Due to these restrictions, the collection is not open data. Please download the Agreement at Data Sharing Agreement and send the signed form to fakenewstask@gmail.com . <strong>Citation</strong> Please cite our work as <pre>@article{shahi2021overview, title={Overview of the CLEF-2021 CheckThat! lab task 3 on fake news detection}, author={Shahi, Gautam Kishore and Stru{\ss}, Julia Maria and Mandl, Thomas}, journal={Working Notes of CLEF}, year={2021} }</pre> <strong>Problem Definition:</strong> Given the text of a news article, determine whether the main claim made in the article is true, partially true, false, or other (e.g., claims in dispute) and detect the topical domain of the article. This task will run in <strong>English.</strong> <strong>Subtask 3A:</strong> <strong>Multi-class fake news detection of news articles (English)</strong> Sub-task A would detect fake news designed as a four-class classification problem. The training data will be released in batches and roughly about 900 articles with the respective label. Given the text of a news article, determine whether the main claim made in the article is true, partially true, false, or other. Our definitions for the categories are as follows: False - The main claim made in an article is untrue. Partially False - The main claim of an article is a mixture of true and false information. The article contains partially true and partially false information but cannot be considered 100% true. It includes all articles in categories like partially false, partially true, mostly true, miscaptioned, misleading etc., as defined by different fact-checking services. True - This rating indicates that the primary elements of the main claim are demonstrably true. Other- An article that cannot be categorised as true, false, or partially false due to lack of evidence about its claims. This category includes articles in dispute and unproven articles. <strong>Subtask 3B: Topical Domain Classification of News Articles (English) </strong>Fact-checkers require background expertise to identify the truthfulness of an article. The categorisation will help to automate the sampling process from a stream of data. Given the text of a news article, determine the topical domain of the article (English). This is a classification problem. The task is to categorise fake news articles into six topical categories like health, election, crime, climate, election, education. This task will be offered for a subset of the data of Subtask 3A. <strong>Input Data</strong> The data will be provided in the format of Id, title, text, rating, the domain; the description of the columns is as follows: <strong>Task 3a</strong> ID- Unique identifier of the news article Title- Title of the news article text- Text mentioned inside the news article our rating - class of the news article as false, partially false, true, other <strong>Task 3b</strong> public_id- Unique identifier of the news article Title- Title of the news article text- Text mentioned inside the news article domain - domain of the given news article(applicable only for task B) <strong>Output data format</strong> <strong>Task 3a</strong> public_id- Unique identifier of the news article predicted_rating- predicted class Sample File <pre><code>public_id, predicted_rating 1, false 2, true</code></pre> <strong>Task 3b</strong> public_id- Unique identifier of the news article predicted_domain- predicted domain Sample file <pre><code>public_id, predicted_domain 1, health 2, crime</code></pre> <strong>Additional data for Training</strong> To train your model, the participant can use additional data with a similar format; some datasets are available over the web. We don't provide the background truth for those datasets. For testing, we will not use any articles from other datasets. Some of the possible source: Fakenews Classification Datasets Fake News Detection Challenge KDD 2020 FakeNewsNet <strong>IMPORTANT! </strong> Fake news article used for task 3b is a subset of task 3a. We have used the data from 2010 to 2021, and the content of fake news is mixed up with several topics like election, COVID-19 etc. <strong>Evaluation Metrics</strong> This task is evaluated as a classification task. We will use the F1-macro measure for the ranking of teams. There is a limit of 5 runs (total and not per day), and only one person from a team is allowed to submit runs. <strong>Submission Link:</strong> https://competitions.codalab.org/competitions/31238 <strong>Related Work</strong> Shahi GK. AMUSED: An Annotation Framework of Multi-modal Social Media Data. arXiv preprint arXiv:2010.00502. 2020 Oct 1.https://arxiv.org/pdf/2010.00502.pdf G. K. Shahi and D. Nandini, “FakeCovid – a multilingualcross-domain fact check news dataset for covid-19,” inWorkshop Proceedings of the 14th International AAAIConference on Web and Social Media, 2020. http://workshop-proceedings.icwsm.org/abstract?id=2020_14 Shahi, G. K., Dirkson, A., & Majchrzak, T. A. (2021). An exploratory study of covid-19 misinformation on twitter. <em>Online Social Networks and Media</em>, <em>22</em>, 100104. doi: 10.1016/j.osnem.2020.100104