Data on trust in the news reported by selected social media platforms in the United States revealed that as of April 2023, news found on TikTok was considered to be the least trustworthy overall, with 50 percent of respondents saying they did not trust news they encountered on the platform. Facebook, X, and YouTube fared the best in terms of trustworthy news content, with over 20 percent of respondents saying they felt the reporting they saw on these sites was reliable.

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 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.

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.

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.

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.

Following Twitter’s and Facebook’s data sharing policy, we only share Tweet IDs and public Face-book post IDs (collected from public Facebook pages and public Facebook groups via CrowdTangle) for academic research purposes. Regarding mainstream news media data, TV news transcripts are not shared due to copyright restrictions.

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

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.

Social media platforms have been found to be the primary gateway through which individuals are exposed to fake news. The algorithmic filter bubbles and echo chambers that have popularized these platforms may also increase exposure to fake news. Because of this, scholars have suggested disrupting the stream of congruent information that filter bubbles and echo chambers produce, as this may reduce the impact and circulation of misinformation. To test this, a survey experiment was conducted via Amazon MTurk. Participants read ten short stories that were either all fake or half real and half fake. These treatment conditions were made up of stories agreeable to the perspective of Democrats, Republicans, or a mix of both. The results show that participants assigned to conditions that were agreeable to their political world view found fake stories more believable compared to participants who received a heterogeneous mix of news stories complementary to both world views. However, this "break up" effect appears confined to Democratic participants; findings indicate that Republicans assigned to filter bubble treatment conditions believed fake news stories at approximately the same rate as their fellow partisans receiving a heterogeneous mix of news items. This suggests that a potential "break up" may only influence more progressive users.Data included in this deposit:1. Stata .dta file2. Stata .do file used to generate tables and figures featured in paper3. Treatment .mp4 video used on primed participants

This dataset contains the text of official policies, blog posts and press releases from websites of 12 social media platforms, outlining their responses to COVID-19 misinformation between February 1, 2020 and April 1, 2021 (and updated on November 23, 2021).

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

This study investigates the features of fake news networks and how they spread during the 2020 South Korean election. Using Actor-Network Theory (ANT), we assessed the network's central players and how they are connected. Results reveal the characteristics of the videoclips and channel networks responsible for the propagation of fake news. Analysis of the videoclip network reveals a high number of detected fake news videos and a high density of connections among users. Assessment of news videoclips on both actual and fake news networks reveals that the real news network is more concentrated. However, the scale of the network may play a role in these variations. Statistics for network centralization reveal that users are spread out over the network, pointing to its decentralized character. A closer look at the real and fake news networks inside videos and channels reveals similar trends. We find that the density of the real news videoclip network is higher than that of the fake news network, whereas the fake news channel networks are denser than their real news counterparts, which may indicate greater activity and interconnectedness in their transmission. We also found that fake news videoclips had more likes than real news videoclips, whereas real news videoclips had more dislikes than fake news videoclips. These findings strongly suggest that fake news videoclips are more accepted when people watch them on YouTube. In addition, we used semantic networks and automated content analysis to uncover common language patterns in fake news which helps us better understand the structure and dynamics of the networks involved in the dissemination of fake news. The findings reported here provide important insights on how fake news spread via social networks during the South Korean election of 2020. The results of this study have important implications for the campaign against fake news and ensuring factual coverage.

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} }

During a 2025 survey, ** percent of respondents from Nigeria stated that they used social media as a source of news. In comparison, just ** percent of Japanese respondents said the same. Large portions of social media users around the world admit that they do not trust social platforms either as media sources or as a way to get news, and yet they continue to access such networks on a daily basis. Social media: trust and consumption Despite the majority of adults surveyed in each country reporting that they used social networks to keep up to date with news and current affairs, a 2018 study showed that social media is the least trusted news source in the world. Less than ** percent of adults in Europe considered social networks to be trustworthy in this respect, yet more than ** percent of adults in Portugal, Poland, Romania, Hungary, Bulgaria, Slovakia and Croatia said that they got their news on social media. What is clear is that we live in an era where social media is such an enormous part of daily life that consumers will still use it in spite of their doubts or reservations. Concerns about fake news and propaganda on social media have not stopped billions of users accessing their favorite networks on a daily basis. Most Millennials in the United States use social media for news every day, and younger consumers in European countries are much more likely to use social networks for national political news than their older peers. Like it or not, reading news on social is fast becoming the norm for younger generations, and this form of news consumption will likely increase further regardless of whether consumers fully trust their chosen network or not.

Abstract: The fine line separating fact from fiction is increasingly hidden, creating parallel realities that cloud the view of society. The current essay on saramaguian blindness is reintroduced with the aid of the speed of a simple touch on the screen in social media. In this sense, the present article explores the comprehension of which elements influence the credibility of scientific fake news. The main concepts to elucidate this question are perception and persuasion. The study is qualitative in nature, with the participation of 232 subjects through an online questionnaire. The results show that family income, schooling, and the articulation of persuasive discourse are essential elements for the credibility of fake news.

The current data from 537 Pakistanis tells us about their trust in different information sources, use of news media, and perception of misinformation regarding COVID-19 in Pakistan. The dataset includes variables such as age, marital status, gender, social class, residential area, trust in source of information, use of news media for coronavirus information, and perception of misinformation regarding COVID-19 in Pakistan. We used Qualtrics to collect data via an online survey that was conducted between 24 April and 12 May 2020. This data will help future research to understand national and international scholarship relating to COVID-19.

The data set contains posts from social media networks popular among Russian-speaking communities. Information was searched based on pre-defined keywords ("war", "special military operation", etc.) and is mainly related to the ongoing war in Ukraine with Russia. After a thorough review and analysis of the data, both propaganda and fake news were identified. The collected data is anonymized. Feature engineering and text preprocessing can be applied to obtain new insights and knowledge from this data set. The data set is useful for the study of information wars and propaganda identification.

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.

Past research has attributed the online circulation of misinformation to two main factors - individual characteristics (e.g., a person's information literacy) and social media effects (e.g., algorithm-mediated information diffusion) - and has overlooked a third one: the critical mass created by the offline self-segregation of Americans into like-minded geographical regions such as states (a phenomenon called "The Big Sort"). We hypothesized that this latter factor matters for the online spreading of misinformation not least because online interactions, despite having the potential of being global, end up being localized: interaction probability is known to rapidly decay with distance. Upon analysis of more than 8M Reddit comments containing news links spanning four years, from January 2016 to December 2019, we found that Reddit did not work as an "hype machine" for misinformation (as opposed to what previous work reported for other platforms, circulation was not mainly caused by platform-facilitated network effects) but worked as a supply-and-demand system: misinformation news items scaled linearly with the number of users in each state (with a scaling exponent beta=1, and a goodness of fit R2 = 0.95). Furthermore, deviations from such a universal pattern were best explained by state-level personality and cultural factors (R2 = {0.12, 0.39}), rather than socioeconomic conditions (R2 = {0.15, 0.29}) or, as one would expect, political characteristics (R2 ={0.06, 0.21}). Higher-than-expected circulation of any type of news (including reputable news) was found in states characterised by residents who tend to be less diligent in terms of their personality (low in conscientiousness) and by loose cultures understating the importance of adherence to norms (low in cultural tightness).