A survey conducted in December 2020 assessing if news consumers in the United States had ever unknowingly shared fake news or information on social media found that 38.2 percent had done so. A similar share had not, whereas seven percent were unsure if they had accidentally disseminated misinformation on social networks.

Fake news in the U.S.

Fake news, or news that contains misinformation, has become a prevalent issue within the American media landscape. Fake news can be circulated online as news stories with deliberately misleading headings, or clickbait, but the rise of misinformation cannot be solely accredited to online social media. Forms of fake news are also found in print media, with 47 percent of Americans witnessing fake news in newspapers and magazines as of January 2019.

News consumers in the United States are aware of the spread of misinformation, with many Americans believing online news websites regularly report fake news stories. With such a high volume of online news websites publishing false information, it can be difficult to assess the credibility of a story. This can have damaging effects on society in that the public struggled to keep informed, creating a great deal of confusion about even basic facts and contributing to incivility.

A 2024 study on news consumption among children in the United Kingdom found that ** percent of respondents aged 12 to 15 years old had come across deliberately untrue or misleading news online or on social media in the year before the survey was conducted. ** percent said they had not seen any false news.

This study investigated the cognitive processing of true and false political information. Specifically, it examined the impact of source credibility on the assessment of veracity when information comes from a polarizing source (Experiment 1), and effectiveness of explanations when they come from one's own political party or an opposition party (Experiment 2). These experiments were conducted prior to the 2016 Presidential election. Participants rated their belief in factual and incorrect statements that President Trump made on the campaign trail; facts were subsequently affirmed and misinformation retracted. Participants then re-rated their belief immediately or after a delay. Experiment 1 found that (i) if information was attributed to Trump, Republican supporters of Trump believed it more than if it was presented without attribution, whereas the opposite was true for Democrats and (ii) although Trump supporters reduced their belief in misinformation items following a correction, they did not change their voting preferences. Experiment 2 revealed that the explanation's source had relatively little impact, and belief updating was more influenced by perceived credibility of the individual initially purporting the information. These findings suggest that people use political figures as a heuristic to guide evaluation of what is true or false, yet do not necessarily insist on veracity as a prerequisite for supporting political candidates.

A report investigating media literacy and news consumption revealed that consumers in Brazil found telling the difference between misinformation and facts most difficult, with 34 percent saying that they found it very or somewhat difficult to differentiate between false and real content. By contrast, Indian and Nigerian audiences were the least likely to have problems in this regard and reported finding it relatively easy to identify misinformation.

From September to November 2020, 57.7 percent of misinformation recommended by Instagram contained content about the coronavirus. Overall, 21.2 percent of misinformation posts contained content about vaccines, and 12.5 percent of recommended misinformation was surrounding elections. Overall, over 37 percent of misinformation came from Instagram's suggested posts feature.

FakeNewsNet is a multi-dimensional data repository that currently contains two datasets with news content, social context, and spatiotemporal information. The dataset is constructed using an end-to-end system, FakeNewsTracker. The constructed FakeNewsNet repository has the potential to boost the study of various open research problems related to fake news study. Because of the Twitter data sharing policy, we only share the news articles and tweet ids as part of this dataset and provide code along with repo to download complete tweet details, social engagements, and social networks. We describe and compare FakeNewsNet with other existing datasets in FakeNewsNet: A Data Repository with News Content, Social Context and Spatialtemporal Information for Studying Fake News on Social Media (https://arxiv.org/abs/1809.01286). A more readable version of the dataset is available at https://github.com/KaiDMML/FakeNewsNet

Data for Experiments 1 & 2 for Cook, Lewandowsky & Ecker (2017). Neutralizing Misinformation Through Inoculation: Exposing Misleading Argumentation Techniques Reduces Their Influence. PLOS ONE.

The Health Misinformation track aims to (1) provide a venue for research on retrieval methods that promote better decision making with search engines, and (2) develop new online and offline evaluation methods to predict the decision making quality induced by search results. Consumer health information is used as the domain of interest in the track.

This dataset contains metadata about all Covid-related YouTube videos which circulated on public social media, but which YouTube eventually removed because they contained false information. It describes 8,122 videos that were shared between November 2019 and June 2020. The dataset contains unique identifiers for the videos and social media accounts that shared the videos, statistics on social media engagement and metadata such as video titles and view counts where they were recoverable. We publish the data alongside the code used to produce on Github. The dataset has reuse potential for research studying narratives related to the coronavirus, the impact of social media on knowledge about health and the politics of social media platforms.

Introduction

Fake News Statistics: Fake news refers to information that is untrue and circulated deliberately intending to deceive the reader. The dissemination of fake news statistics has increased tremendously over the past few years with the development of social media and other online platforms.

It has become a serious concern in various countries as of the year 2024 for aspects such as trust among the citizens, politics, and the social conduct of the people. There are concerted efforts by both the authorities and technology industries to contain the menace of false information. This article will show the fake news statistics and facts below, showing how prevalent this modern issue is today.

Curated database of fact checked claims (fake and real news), with close to 70.000 URLs, classified by topic.

This dataset provides a labeled fake news data

Data Access: 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 .

Citation

Please cite our work as

@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.

Subtask 3A: Multi-class fake news detection of news articles (English) 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.

Subtask 3B: Topical Domain Classification of News Articles (English) 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.

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:

Task 3a

• 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

Task 3b

• 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)

Output data format

Task 3a

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

Sample File

public_id, predicted_rating
1, false
2, true

Task 3b

• public_id- Unique identifier of the news article
• predicted_domain- predicted domain

Sample file

public_id, predicted_domain
1, health
2, crime

Additional data for Training

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

IMPORTANT!

1. Fake news article used for task 3b is a subset of task 3a.
2. 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.

Evaluation Metrics

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.

Submission Link: https://competitions.codalab.org/competitions/31238

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 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. Online Social Networks and Media, 22, 100104. doi: 10.1016/j.osnem.2020.100104

A July 2021 survey of online adults in the United States found that 34 percent of respondents felt that the user who originally posted the false information are most responsible for the spread of coronavirus misinformation on social media. Additionally, 27 percent of respondents stated that social media companies were most responsible.

daviddaubner/misinformation-detection dataset hosted on Hugging Face and contributed by the HF Datasets community

The datasets are for my master's thesis. We conducted 2 experiments to examine the effect of our self-designed reflective questions which induce critical thinking on participants' fake news discernment ability. The dataset contains participants' demographic information, critical thinking ability, reflective question scoring, fake news discerning and sharing ability etc.

Materials, scripts, and data are available at: https://osf.io/na9qt/ and https://osf.io/96p7g/

Survey data collected in 2019 in Canada (n=1539). Seeing misinformation online, trust in federal government, political ideology and Facebook use.

A sample of TikTok videos associated with the hashtag #coronavirus were downloaded on September 20, 2020. Misinformation was evaluated on a scale (low, medium, high) using a codebook developed by experts in infectious diseases. Multivariable modeling was used to evaluate factors associated with number of views and presence of user comments indicating intention to change behavior. Videos and related metadata were downloaded using a third-party TikTok Scraper using the search term #coronavirus. Videos were reviewed for content and data were entered on a spreadsheet.

Governments may have the capacity to flood social media with fake news, but little is known about the use of flooding by ordinary voters. In this work, we identify 2107 registered US voters that account for 80% of the fake news shared on Twitter during the 2020 US presidential election by an entire panel of 664,391 voters. We find that supersharers are important members of the network, reaching a sizable 5.2% of registered voters on the platform. Supersharers have a significant overrepresentation of women, older adults, and registered Republicans. Supersharers' massive volume does not seem automated but is rather generated through manual and persistent retweeting. These findings highlight a vulnerability of social media for democracy, where a small group of people distort the political reality for many.

Replication data and code for "Real Solutions for Fake News? Measuring the Effectiveness of General Warnings and Fact-Check Tags in Reducing Belief in False Stories on Social Media" by Katherine Clayton, Spencer Blair, Jonathan A. Busam, Samuel Forstner, John Glance, Guy Green, Anna Kawata, Akhila Kovvuri, Jonathan Martin, Evan Morgan, Morgan Sandhu, Rachel Sang, Rachel Scholz-Bright, Austin T. Welch, Andrew G. Wolff, Amanda Zhou, and Brendan Nyhan.