The United States of America

Context

The dataset contains the list of COVID Fake News/Claims which is shared all over the internet.

Content

1. Headlines: String attribute consisting of the headlines/fact shared.
2. Outcome: It is binary data where 0 means the headline is fake and 1 means that it is true.

Inspiration

In many research portals, there was this common question in which the combined fake news dataset is available or not. This led to the publication of this dataset.

Dataset

This dataset was created by Inv.Alireza babazadeh zarei

Released under MIT

Contents

Dataset

This dataset was created by NewsDataio

Contents

FakeCovid Fact-Checked News Dataset

International Coverage of COVID-19 in 40 Languages from 105 Countries

By [source]

About this dataset

The FakeCovid dataset is an unparalleled compilation of 7623 fact-checked news articles related to COVID-19. Obtained from 92 fact-checking websites located in 105 countries, this comprehensive collection covers a wide range of sources and languages, including locations across Africa, Europe, Asia, The Americas and Oceania. With data gathered from references on Poynter and Snopes, this unique dataset is an invaluable resource for researching the accuracy of global news related to the pandemic. It offers an invaluable insight into the international nature of COVID information with its column headers covering country's involved; categories such as coronavirus health updates or political interference during coronavirus; URLs for referenced articles; verifiers employed by websites; article classes that can range from true to false or even mixed evaluations; publication dates ; article sources injected with credibility verification as well as article text and language standardization. This one-of-a kind dataset serves as an essential tool in understanding both global information flow around the world concerning COVID 19 while simultaneously offering transparency into whose interests guide it

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How to use the dataset

The FakeCovid dataset is a multilingual cross-domain collection of 7623 fact-checked news articles related to COVID-19. It is collected from 92 fact-checking websites and covers a wide range of sources and countries, including locations in Africa, Asia, Europe, The Americas, and Oceania. This dataset can be used for research related to understanding the truth and accuracy of news sources related to COVID-19 in different countries and languages.

To use this dataset effectively, you will need basic knowledge of data science principles such as data manipulation with pandas or Python libraries such as NumPy or ScikitLearn. The data is in CSV (comma separated values) format that can be read by most spreadsheet applications or text editor like Notepad++. Here are some steps on how to get started: - Access the FakeCovid Fact Checked News Dataset from Kaggle: https://www.kaggle.com/c/fakecovidfactcheckednewsdataset/data - Download the provided CSV file containing all fact checked news articles and place it into your desired folder location - Load the CSV file into your preferred software application like Jupyter Notebook or RStudio 4)Explore your dataset using built-in functions within data science libraries such as Pandas & matplotlib – find meaningful information through statistical analysis &//or create visualizations 5)Modify parameters within the csv file if required & save 6)Share your creative projects through Gitter chatroom #fakecovidauthors 7 )Publish any interesting discoveries you find within open source repositories like GitHub 8 )Engage with our Hangouts group #FakeCoviDFactCheckersClub 9 )Show off fun graphics via Twitter hashtag #FakeCovidiauthors 10 )Reach out if you have further questions via email contactfakecovidadatateam 11 )Stay connected by joining our mailing list#FakeCoviDAuthorsGroup

We hope this guide helps you better understand how to use our FakeCoviD Fact Checked News Dataset for generating meaningful insights relating to COVID-19 news articles worldwide!

Research Ideas

• Developing an automated algorithm to detect fake news related to COVID-19 by leveraging the fact-checking flags and other results included in this dataset for machine learning and natural language processing tasks.
• Training a sentiment analysis model on the data to categorize articles according to their sentiments which can be used for further investigations into why certain news topics or countries have certain outcomes, motivations, or behaviors due to their content relatedness or author biasness(if any).
• Using unsupervised clustering techniques, this dataset could be used as a tool for identifying any discrepancies between news circulated in different populations in different countries (langauge and regions) so that publicists can focus more on providing factual information rather than spreading false rumors or misinformation about the pandemic

Acknowledgements

If you use this dataset in your research, please credit the original authors. Data Source

License

**License: [CC0 1.0 Universal (CC0 1.0) - Public Do...

Coronavirus disease 2019 (COVID19) time series that lists confirmed cases, reported deaths, and reported recoveries. Data is broken down by country (and sometimes by sub-region).

Coronavirus disease (COVID19) is caused by severe acute respiratory syndrome Coronavirus 2 (SARSCoV2) and has had an effect worldwide. On March 11, 2020, the World Health Organization (WHO) declared it a pandemic, currently indicating more than 118,000 cases of coronavirus disease in more than 110 countries and territories around the world.

This dataset contains the latest news related to Covid-19 and it was fetched with the help of Newsdata.io news API.

Track and monitor Covid-19 related news from the world.

A COVID-19 misinformation / fake news / rumor / disinformation dataset collected from online social media and news websites. Usage note:Misinformation detection, classification, tracking, prediction.Misinformation sentiment analysis.Rumor veracity classification, comment stance classification.Rumor tracking, social network analysis.Data pre-processing and data analysis codes available at https://github.com/MickeysClubhouse/COVID-19-rumor-datasetPlease see full info in our GitHub link.Cite us:Cheng, Mingxi, et al. "A COVID-19 Rumor Dataset." Frontiers in Psychology 12 (2021): 1566.@article{cheng2021covid, title={A COVID-19 Rumor Dataset}, author={Cheng, Mingxi and Wang, Songli and Yan, Xiaofeng and Yang, Tianqi and Wang, Wenshuo and Huang, Zehao and Xiao, Xiongye and Nazarian, Shahin and Bogdan, Paul}, journal={Frontiers in Psychology}, volume={12}, pages={1566}, year={2021}, publisher={Frontiers} }

The FakeCovid dataset contains 5182 fact-checked news articles for COVID-19 collected from January to May 2020.

WWFND: World Wide Fake News Dataset 1. Introduction The World Wide Fake News Dataset (WWFND) has been developed with the objective of facilitating research in the domain of fake news detection. This dataset has been created using Python’s web scraping library – BeautifulSoup, and comprises news articles collected from multiple globally recognised fact-checking and media organisations. The data has been carefully compiled from reputable news and fact-verification platforms identified by the Pew Research Center, including but not limited to:

BBC News

CNN

Al Jazeera

Times of India

The Hindu

PolitiFact

NBC News

CBS News

ABC News

NDTV

The Wire

These sources have been selected for their credibility and global or national reach. News articles were collected only after ensuring that they had been clearly classified as either true or fake by these organisations.

2. Dataset Summary The dataset comprises a total of 30,616 records, which include:

15,027 records identified as true news articles

15,589 records identified as fake news articles

To further enhance the robustness and applicability of the dataset, it has been combined with another dataset titled COVID19_FNIR, available through the IEEE Dataport at the following link: https://ieee-dataport.org/open-access/covid-19-fake-news-infodemic-research-dataset-covid19-fnir-dataset

This integration was undertaken to provide a more comprehensive dataset, especially for training machine learning models in detecting misinformation during global crises such as the COVID-19 pandemic.

3. Contents of the Dataset The WWFND dataset includes the following files:

• Final_Preprocessed_WWFND_BothFakeAndTrueNews.csv

This file contains the cleaned and preprocessed version of the dataset, combining both fake and true news articles.

• WWFND_FakeNews_RawData.csv

This file contains the raw, unprocessed fake news articles collected from the sources mentioned above.

• WWFND_TrueNews_RawData.csv

This file contains the raw, unprocessed true news articles obtained from the verified sources.

4. Applications This dataset is suitable for various applications, including:

Training and testing models for fake news detection

Text classification and content analysis using Natural Language Processing (NLP) techniques

Research in media literacy, misinformation tracking, and credibility assessment

Academic projects and data science competitions focused on information verification

5. Acknowledgements The dataset creators acknowledge the use of publicly available content solely for academic and research purposes. The COVID19_FNIR dataset has been used with reference to its source on IEEE Dataport.

6. Licensing and Usage This dataset is intended for educational and research use only. Users are advised to cite the original sources and the IEEE dataset if the WWFND dataset is used in any publication or project.

The New York Times is releasing a series of data files with cumulative counts of coronavirus cases in the United States, at the state and county level, over time. We are compiling this time series data from state and local governments and health departments in an attempt to provide a complete record of the ongoing outbreak.

Since late January, The Times has tracked cases of coronavirus in real time as they were identified after testing. Because of the widespread shortage of testing, however, the data is necessarily limited in the picture it presents of the outbreak.

We have used this data to power our maps and reporting tracking the outbreak, and it is now being made available to the public in response to requests from researchers, scientists and government officials who would like access to the data to better understand the outbreak.

The data begins with the first reported coronavirus case in Washington State on Jan. 21, 2020. We will publish regular updates to the data in this repository.

People are often exposed to fake news. Such an exposure to misleading information might lead to false memory creation. We examined whether people can form false memories for COVID-19-related fake news. Furthermore, we investigated which individual factors might predict false memory formation for fake news. In two experiments, we provided participants with two pieces of COVID-19-related fake news along with a non-probative photograph. In Experiment 1, 41% (n = 66/161) of our sample reported at least one false memory for COVID-19-related fake news. In Experiment 2, even a higher percentage emerged (54.9%; n = 185/337). Moreover, in Experiment 2, participants with conspiracy beliefs were more likely to report false memories for fake news than those without such beliefs, irrespective of the conspiratorial nature of the materials. Finally, while well-being was found to be positively associated with both true and false memories (Experiment 1), only analytical thinking was negatively linked to the vulnerability to form false memories for COVID-19-related fake news (Experiment 2). Overall, our data demonstrated that false memories can occur following exposure to fake news about COVID-19, and that governmental and social media interventions are needed to increase individuals’ discriminability between true and false COVID-19-related news.

In May 2020, up to six percent of all online news and posts related to the coronavirus (COVID-19) and released in Italy were false or not accurate. The percentage was calculated on the average volume of posts and articles published by the Italian media outlets, including posts on social media. The peak in the release of fake news was registered in the early stage of the pandemic at the end of January 2020, with 7.3 percent of the coronavirus-related information.

For further information about the coronavirus (COVID-19) pandemic, please visit our dedicated Fact and Figures page.

Introduction

There are several works based on Natural Language Processing on newspaper reports. Mining opinions from headlines [ 1 ] using Standford NLP and SVM by Rameshbhaiet. Al.compared several algorithms on a small and large dataset. Rubinet. al., in their paper [ 2 ], created a mechanism to differentiate fake news from real ones by building a set of characteristics of news according to their types. The purpose was to contribute to the low resource data available for training machine learning algorithms. Doumitet. al.in [ 3 ] have implemented LDA, a topic modeling approach to study bias present in online news media.

However, there are not many NLP research invested in studying COVID-19. Most applications include classification of chest X-rays and CT-scans to detect presence of pneumonia in lungs [ 4 ], a consequence of the virus. Other research areas include studying the genome sequence of the virus[ 5 ][ 6 ][ 7 ] and replicating its structure to fight and find a vaccine. This research is crucial in battling the pandemic. The few NLP based research publications are sentiment classification of online tweets by Samuel et el [ 8 ] to understand fear persisting in people due to the virus. Similar work has been done using the LSTM network to classify sentiments from online discussion forums by Jelodaret. al.[ 9 ]. NKK dataset is the first study on a comparatively larger dataset of a newspaper report on COVID-19, which contributed to the virus’s awareness to the best of our knowledge.

2 Data-set Introduction

2.1 Data Collection

We accumulated 1000 online newspaper report from United States of America (USA) on COVID-19. The newspaper includes The Washington Post (USA) and StarTribune (USA). We have named it as “Covid-News-USA-NNK”. We also accumulated 50 online newspaper report from Bangladesh on the issue and named it “Covid-News-BD-NNK”. The newspaper includes The Daily Star (BD) and Prothom Alo (BD). All these newspapers are from the top provider and top read in the respective countries. The collection was done manually by 10 human data-collectors of age group 23- with university degrees. This approach was suitable compared to automation to ensure the news were highly relevant to the subject. The newspaper online sites had dynamic content with advertisements in no particular order. Therefore there were high chances of online scrappers to collect inaccurate news reports. One of the challenges while collecting the data is the requirement of subscription. Each newspaper required $1 per subscriptions. Some criteria in collecting the news reports provided as guideline to the human data-collectors were as follows:

The headline must have one or more words directly or indirectly related to COVID-19.

The content of each news must have 5 or more keywords directly or indirectly related to COVID-19.

The genre of the news can be anything as long as it is relevant to the topic. Political, social, economical genres are to be more prioritized.

Avoid taking duplicate reports.

Maintain a time frame for the above mentioned newspapers.

To collect these data we used a google form for USA and BD. We have two human editor to go through each entry to check any spam or troll entry.

2.2 Data Pre-processing and Statistics

Some pre-processing steps performed on the newspaper report dataset are as follows:

Remove hyperlinks.

Remove non-English alphanumeric characters.

Remove stop words.

Lemmatize text.

While more pre-processing could have been applied, we tried to keep the data as much unchanged as possible since changing sentence structures could result us in valuable information loss. While this was done with help of a script, we also assigned same human collectors to cross check for any presence of the above mentioned criteria.

The primary data statistics of the two dataset are shown in Table 1 and 2.

Table 1: Covid-News-USA-NNK data statistics

No of words per headline

7 to 20

No of words per body content

150 to 2100

Table 2: Covid-News-BD-NNK data statistics No of words per headline

10 to 20

No of words per body content

100 to 1500

2.3 Dataset Repository

We used GitHub as our primary data repository in account name NKK^1. Here, we created two repositories USA-NKK^2 and BD-NNK^3. The dataset is available in both CSV and JSON format. We are regularly updating the CSV files and regenerating JSON using a py script. We provided a python script file for essential operation. We welcome all outside collaboration to enrich the dataset.

3 Literature Review

Natural Language Processing (NLP) deals with text (also known as categorical) data in computer science, utilizing numerous diverse methods like one-hot encoding, word embedding, etc., that transform text to machine language, which can be fed to multiple machine learning and deep learning algorithms.

Some well-known applications of NLP includes fraud detection on online media sites[ 10 ], using authorship attribution in fallback authentication systems[ 11 ], intelligent conversational agents or chatbots[ 12 ] and machine translations used by Google Translate[ 13 ]. While these are all downstream tasks, several exciting developments have been made in the algorithm solely for Natural Language Processing tasks. The two most trending ones are BERT[ 14 ], which uses bidirectional encoder-decoder architecture to create the transformer model, that can do near-perfect classification tasks and next-word predictions for next generations, and GPT-3 models released by OpenAI[ 15 ] that can generate texts almost human-like. However, these are all pre-trained models since they carry huge computation cost. Information Extraction is a generalized concept of retrieving information from a dataset. Information extraction from an image could be retrieving vital feature spaces or targeted portions of an image; information extraction from speech could be retrieving information about names, places, etc[ 16 ]. Information extraction in texts could be identifying named entities and locations or essential data. Topic modeling is a sub-task of NLP and also a process of information extraction. It clusters words and phrases of the same context together into groups. Topic modeling is an unsupervised learning method that gives us a brief idea about a set of text. One commonly used topic modeling is Latent Dirichlet Allocation or LDA[17].

Keyword extraction is a process of information extraction and sub-task of NLP to extract essential words and phrases from a text. TextRank [ 18 ] is an efficient keyword extraction technique that uses graphs to calculate the weight of each word and pick the words with more weight to it.

Word clouds are a great visualization technique to understand the overall ’talk of the topic’. The clustered words give us a quick understanding of the content.

4 Our experiments and Result analysis

We used the wordcloud library^4 to create the word clouds. Figure 1 and 3 presents the word cloud of Covid-News-USA- NNK dataset by month from February to May. From the figures 1,2,3, we can point few information:

In February, both the news paper have talked about China and source of the outbreak.

StarTribune emphasized on Minnesota as the most concerned state. In April, it seemed to have been concerned more.

Both the newspaper talked about the virus impacting the economy, i.e, bank, elections, administrations, markets.

Washington Post discussed global issues more than StarTribune.

StarTribune in February mentioned the first precautionary measurement: wearing masks, and the uncontrollable spread of the virus throughout the nation.

While both the newspaper mentioned the outbreak in China in February, the weight of the spread in the United States are more highlighted through out March till May, displaying the critical impact caused by the virus.

We used a script to extract all numbers related to certain keywords like ’Deaths’, ’Infected’, ’Died’ , ’Infections’, ’Quarantined’, Lock-down’, ’Diagnosed’ etc from the news reports and created a number of cases for both the newspaper. Figure 4 shows the statistics of this series. From this extraction technique, we can observe that April was the peak month for the covid cases as it gradually rose from February. Both the newspaper clearly shows us that the rise in covid cases from February to March was slower than the rise from March to April. This is an important indicator of possible recklessness in preparations to battle the virus. However, the steep fall from April to May also shows the positive response against the attack. We used Vader Sentiment Analysis to extract sentiment of the headlines and the body. On average, the sentiments were from -0.5 to -0.9. Vader Sentiment scale ranges from -1(highly negative to 1(highly positive). There were some cases

where the sentiment scores of the headline and body contradicted each other,i.e., the sentiment of the headline was negative but the sentiment of the body was slightly positive. Overall, sentiment analysis can assist us sort the most concerning (most negative) news from the positive ones, from which we can learn more about the indicators related to COVID-19 and the serious impact caused by it. Moreover, sentiment analysis can also provide us information about how a state or country is reacting to the pandemic. We used PageRank algorithm to extract keywords from headlines as well as the body content. PageRank efficiently highlights important relevant keywords in the text. Some frequently occurring important keywords extracted from both the datasets are: ’China’, Government’, ’Masks’, ’Economy’, ’Crisis’, ’Theft’ , ’Stock market’ , ’Jobs’ , ’Election’, ’Missteps’, ’Health’, ’Response’. Keywords extraction acts as a filter allowing quick searches for indicators in case of locating situations of the economy,

Spanish Fake News Dataset

This dataset contains a structured and annotated collection of false news items in Spanish (Castilian), gathered and processed for academic research on misinformation.

Dataset Scope

The dataset represents most of the recorded false news messages and their variations up to 01.02.2021.

Content Description

The dataset includes samples of false information in various formats:

News articles and headlines

Tweets and Facebook/Instagram/Telegram posts

YouTube video captions

WhatsApp text and voice message transcripts

Transcribed video/audio fragments with false claims

Fake government documents

Captions from photos and memes

Text extracted from images using OCR

Only Spanish (Castilian) texts were used, excluding regional variants (e.g., Catalan, Basque, Galician) for consistency.

Sources

The data was collected from the following verified fact-checking initiatives:

Maldito Bulo

Newtral

AFP Factual

Fact-checkers from these organizations provide detailed articles identifying and explaining falsehoods, often including:

General context of the event

Quotes or links to false claims

Analysis and explanation of why the claims are false

Verified information or corrections

Collection Method

The dataset was built using both manual extraction (e.g., identifying and quoting false statements) and automated parsing:

MyNews service: an archive of Spanish mass media

Custom scripts: for parsing and extracting structured data

OCR tools: for extracting text from images (e.g., memes and screenshots)

Fields Description

Column Name

Description

Topic

The thematic category of the news item (e.g., Politics, Health, COVID-19, Crime). Normalized and translated to English.

Link source

URL to the original news piece, fact-check report, or source of the claim. Invalid links were removed.

Media

The platform or outlet where the false claim appeared (e.g., Facebook, YouTube, WhatsApp). Normalized for consistent spelling and language.

Date

Publication or verification date of the news item, in YYYY-MM-DD format.

Author

(Optional) Author of the news or platform source, if available. May be empty.

Headlines

Title or summary of the news item or article containing the false information.

Fake statement

Quoted false claim or misinformation as cited in the verification article.

⚠️ Notes

The dataset was preprocessed to remove duplicates, invalid links, and non-textual clutter.

Field values were normalized to support multilingual and cross-platform analysis.

Only Castilian Spanish was retained for consistency and clarity.

📚 License & Use

This dataset is intended for non-commercial academic and research purposes. Please cite the original fact-checking organizations and this dataset if used in publications or analysis.

A survey carried out in the United Kingdom in September 2021 found that ** percent of respondents did not take any action after encountering what they believed to be false or misleading information on the COVID-19 outbreak. Whilst this figure was lower than the share who said the same in the 2020 survey, taking no action remained the most common response to fake coronavirus news. Meanwhile, ** percent used a fact checking site or tool to determine whether or not the information they found was true, and ** percent turned to family or friends for help in confirming the legitimacy of news they suspected to be false.

For further information about the coronavirus (COVID-19) pandemic, please visit our dedicated Facts and Figures page.

Context

COVID Fake News Detection Dataset

Content

It is a subtask in the CONSTRAINT-2021 shared task on the hostile post detection. This subtask focuses on the detection of COVID19-related fake news in English. The sources of data are various social-media platforms such as Twitter, Facebook, Instagram, etc. Given a social media post, the objective of the shared task is to classify it into either fake or real news. https://competitions.codalab.org/competitions/26655

Acknowledgements

We wouldn't be here without the help of others. If you owe any attributions or thanks, include them here along with any citations of past research.

Inspiration

Your data will be in front of the world's largest data science community. What questions do you want to see answered?

In March 2020, nearly **** percent of social media users surveyed in Mexico claimed to have received the largest amount of false information regarding COVID-19 via WhatsApp, while **** percent of respondents said Facebook was the platform through which they got the biggest number of fake news on the matter.

Constraint@AAAI2021 - COVID19 Fake News Detection in English @misc{patwa2020fighting, title={Fighting an Infodemic: COVID-19 Fake News Dataset}, author={Parth Patwa and Shivam Sharma and Srinivas PYKL and Vineeth Guptha and Gitanjali Kumari and Md Shad Akhtar and Asif Ekbal and Amitava Das and Tanmoy Chakraborty}, year={2020}, eprint={2011.03327}, archivePrefix={arXiv}, primaryClass={cs.CL} }

By downloading the data, you agree with the terms & conditions mentioned below:

Data Access: The data in the research collection 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 them only for research purposes.

Summaries, analyses and interpretations of the linguistic properties of the information may be derived and published, provided it is impossible to reconstruct the information from these summaries. You may not try identifying the individuals whose texts are included in this dataset. You may not try to identify the original entry on the fact-checking site. You are not permitted to publish any portion of the dataset besides summary statistics or share it with anyone else.

We grant you the right to access the collection's content as described in this agreement. You may not otherwise make unauthorised commercial use of, reproduce, prepare derivative works, distribute copies, perform, or publicly display the collection or parts of it. You are responsible for keeping and storing the data in a way that others cannot access. The data is provided free of charge.

Citation

Please cite our work as

@InProceedings{clef-checkthat:2022:task3,
author = {K{\"o}hler, Juliane and Shahi, Gautam Kishore and Stru{\ss}, Julia Maria and Wiegand, Michael and Siegel, Melanie and Mandl, Thomas},
title = "Overview of the {CLEF}-2022 {CheckThat}! Lab Task 3 on Fake News Detection",
year = {2022},
booktitle = "Working Notes of CLEF 2022---Conference and Labs of the Evaluation Forum",
series = {CLEF~'2022},
address = {Bologna, Italy},}

@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}
}

Problem Definition: 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 English and German.

Task 3: Multi-class fake news detection of news articles (English) Sub-task A would detect fake news designed as a four-class classification problem. Given the text of a news article, determine whether the main claim made in the article is true, partially true, false, or other. The training data will be released in batches and roughly about 1264 articles with the respective label in English language. 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 a lack of evidence about its claims. This category includes articles in dispute and unproven articles.

Cross-Lingual Task (German)

Along with the multi-class task for the English language, we have introduced a task for low-resourced language. We will provide the data for the test in the German language. The idea of the task is to use the English data and the concept of transfer to build a classification model for the German language.

Input Data

The data will be provided in the format of Id, title, text, rating, the domain; the description of the columns is as follows:

• 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

Output data format

• public_id- Unique identifier of the news article
• predicted_rating- predicted class

Sample File

public_id, predicted_rating
1, false
2, true

IMPORTANT!

1. We have used the data from 2010 to 2022, and the content of fake news is mixed up with several topics like elections, COVID-19 etc.

Baseline: For this task, we have created a baseline system. The baseline system can be found at https://zenodo.org/record/6362498

Related Work

• 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 multilingual cross-domain fact check news dataset for covid-19,” in workshop Proceedings of the 14th International AAAI Conference 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. Online Social Networks and Media, 22, 100104. doi: 10.1016/j.osnem.2020.100104
• Shahi, G. K., Struß, J. M., & Mandl, T. (2021). Overview of the CLEF-2021 CheckThat! lab task 3 on fake news detection. Working Notes of CLEF.
• Nakov, P., Da San Martino, G., Elsayed, T., Barrón-Cedeno, A., Míguez, R., Shaar, S., ... & Mandl, T. (2021, March). The CLEF-2021 CheckThat! lab on detecting check-worthy claims, previously fact-checked claims, and fake news. In European Conference on Information Retrieval (pp. 639-649). Springer, Cham.
• Nakov, P., Da San Martino, G., Elsayed, T., Barrón-Cedeño, A., Míguez, R., Shaar, S., ... & Kartal, Y. S. (2021, September). Overview of the CLEF–2021 CheckThat! Lab on Detecting Check-Worthy Claims, Previously Fact-Checked Claims, and Fake News. In International Conference of the Cross-Language Evaluation Forum for European Languages (pp. 264-291). Springer, Cham.