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.

Introduction

Fake News Statistics: Fake news has become a major problem in today's digital age in recent years. It spreads quickly through social media and other online platforms, often misleading people. Fake news spreads faster than real news, thus creating confusion and mistrust among global people. In 2024, current statistics and trends reveal that many people have encountered fake news online, and many have shared it unknowingly.

Fake news affects public opinion, political decisions, and even relationships. This article helps us understand how widespread it is and helps us address several issues more effectively. Raising awareness and encouraging critical thinking can reduce its impact, in which reliable statistics and research are essential for uncovering the truth and stopping the spread of false information. Everyone plays a role in combating fake news.

A study held in early 2023 found that Indonesian adults were the most concerned about the spread of false information on social media, with over 80 percent saying that they were very or somewhat worried about the matter. Whilst Swedish and Danish respondents were less concerned about misinformation on social media, the global average among all countries was 68 percent, highlighting the growing awareness and worry about false information worldwide.

The term ‘fake news’ is used in multiple different contexts, but officially refers simply to false information presented as legitimate news. Adults in the United States believe social media platforms and online news sites to be the most likely sources of fake news – 58 percent of respondents to a survey believed that Facebook was the most likely place in which they would encounter false news stories, and 49 percent said the same about Twitter.

A separate study revealed that 66 percent of U.S. adults believed that 76 percent or more of the news they saw on social media was biased. Social networks are generally not seen as credible or trustworthy news platforms – on a global level, social media was the least trusted source of general news and information.

Why does social media fuel or help to spread fake news?

Sadly, the main way in which fake news can be so quickly disseminated throughout not only one, but multiple social media platforms, is by users sharing such news with others (either knowingly or unknowingly). The ability to share content with friends and family is one of the key appeals of social networks, but the ease of doing so becomes somewhat sinister when it comes to the spread of false information.

Ten percent of U.S. adults admitted to knowingly sharing fake news or information online, 49 percent said that they shared such content and later found out it was inaccurate or made up, and 52 percent admitted to having done either of these things. This is a serious cause for concern. Sharing news in good faith and later discovering it was fake is one thing, but deliberately and knowingly passing such content on to others is another. Many social media users blame the networks themselves for the spread of fake news. Whilst social platforms do make attempts to regulate the content shared on their sites, the more users who actively take responsibility for the content they choose to share and hold themselves accountable, the greater the overall impact.

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.

A 2024 study on news consumption among children in the United Kingdom found that 37 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. 35 percent said they had not seen any false news.

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

As of March of 2018, around 52 percent of Americans felt that online news websites regularly report fake news stories in the United States. Another 34 percent of respondents stated that they believed that online news websites occasionally report fake news stories. Just nine percent of adults said that they did not believe that fake news stories were being reported online.

Fake news

Coined by Donald Trump, the term ‘fake news’ is used to describe news stories or even entire networks believed to be spreading false information. Increasingly used by members of government and citizens on both sides of the political spectrum, the term is now a staple in debates regarding freedom of the press, corruption, and media bias. People of all ages now believe that over 60 percent of the news that they see on social media is fake and express similar concern over the accuracy of traditional news sources. While a cynical perspective regarding news and reporting may be positive in terms of holding guilty outlets accountable and ensuring responsible reporting, the fake news phenomenon has extended much farther than pure skepticism. As of 2018, around 35 percent of Republicans and 18 percent of Independents perceived the media to be an enemy of the American people.

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

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:The Stata .dta fileThe Stata .do file used to generate tables and figures featured in the paperA .pdf file containing the text of the fake and real news items used in the paperA .pdf file containing the complete survey text

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.

The statistic presents results of a survey on whether social media sites are currently doing enough to stop the spread of fake news United States as of March 2018. During the survey, 69 percent of respondents stated that they believed social media sites were not doing enough to stop the spread of fake news.

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. Methods This dataset contains aggregated information necessary to replicate the results reported in our work on Supersharers of Fake News on Twitter while respecting and preserving the privacy expectations of individuals included in the analysis. No individual-level data is provided as part of this dataset. The data collection process that enabled the creation of this dataset leveraged a large-scale panel of registered U.S. voters matched to Twitter accounts. We examined the activity of 664,391 panel members who were active on Twitter during the months of the 2020 U.S. presidential election (August to November 2020, inclusive), and identified a subset of 2,107 supersharers, which are the most prolific sharers of fake news in the panel that together account for 80% of fake news content shared on the platform. We rely on a source-level definition of fake news, that uses the manually-labeled list of fake news sites by Grinberg et al. 2019 and an updated list based on NewsGuard ratings (commercially available, but not provided as part of this dataset), although the results were robust to different operationalizations of fake news sources. We restrict the analysis to tweets with external links that were identified as political by a machine learning classifier that we trained and validated against human coders, similar to the approach used in prior work. We address our research questions by contrasting supersharers with three reference groups: people who are the most prolific sharers of non-fake political tweets (supersharers non-fake group; SS-NF), a group of average fake news sharers, and a random sample of panel members. In particular, we identify the distinct sociodemographic characteristics of supersharers using a series of multilevel regressions, examine their use of Twitter through existing tools and additional statistical analysis, and study supersharers' reach by examining the consumption patterns of voters that follow supersharers.

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.

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} }

This dataset contains multimodal content—images and text—from two sources:Fakeddit Subset: A collection of social media posts (primarily from Reddit) that often include misleading or questionable content.Snopes Crawled Data (Medical Fake News Only): Fact-checking information focused solely on medical misinformation, as curated and verified by Snopes.

Fake news as one of a normal phenomenon all over the world, it exertsa great impact on both individuals and our society. With the popularity of social media, fake news increasingly invades our lives, even brings disadvantages to institutions or countries. According to the analysis of several examples, the study aims to improve the phenomenon of fake news in social media so as to promise the authenticity of news in the field of media and communication. Even though it is difficult to achieve nowadays, especially in the current information era, in fact, it calls for the collective effort by the government, information producers and social media platform, as well as audiences. In addition, this study also expects to offer a reference to future journalism practitioners and related academic researchers.

This is a multimodal dataset used in the paper "On the Role of Images for Analyzing Claims in Social Media", accepted at CLEOPATRA-2021 (2nd International Workshop on Cross-lingual Event-centric Open Analytics), co-located with The Web Conference 2021.

The four datasets are curated for two different tasks that broadly come under fake news detection. Originally, the datasets were released as part of challenges or papers for text-based NLP tasks and are further extended here with corresponding images.

1. clef_en and clef_ar are English and Arabic Twitter datasets for claim check-worthiness detection released in CLEF CheckThat! 2020 Barrón-Cedeno et al. [1].
2. lesa is an English Twitter dataset for claim detection released by Gupta et al.[2]
3. mediaeval is an English Twitter dataset for conspiracy detection released in MediaEval 2020 Workshop by Pogorelov et al.[3]

The dataset details like data curation and annotation process can be found in the cited papers.

Datasets released here with corresponding images are relatively smaller than the original text-based tweets. The data statistics are as follows: 1. clef_en: 281 2. clef_ar: 2571 3. lesa: 1395 4. mediaeval: 1724

Each folder has two sub-folders and a json file data.json that consists of crawled tweets. Two sub-folders are: 1. images: This Contains crawled images with the same name as tweet-id in data.json. 2. splits: This contains 5-fold splits used for training and evaluation in our paper. Each file in this folder is a csv with two columns .

Code for the paper: https://github.com/cleopatra-itn/image_text_claim_detection

If you find the dataset and the paper useful, please cite our paper and the corresponding dataset papers[1,2,3] Cheema, Gullal S., et al. "On the Role of Images for Analyzing Claims in Social Media" 2nd International Workshop on Cross-lingual Event-centric Open Analytics (CLEOPATRA) co-located with The Web Conf 2021.

[1] Barrón-Cedeno, Alberto, et al. "Overview of CheckThat! 2020: Automatic identification and verification of claims in social media." International Conference of the Cross-Language Evaluation Forum for European Languages. Springer, Cham, 2020. [2] Gupta, Shreya, et al. "LESA: Linguistic Encapsulation and Semantic Amalgamation Based Generalised Claim Detection from Online Content." arXiv preprint arXiv:2101.11891 (2021). [3] Pogorelov, Konstantin, et al. "FakeNews: Corona Virus and 5G Conspiracy Task at MediaEval 2020." MediaEval 2020 Workshop. 2020.

We designed a larger and more generic Word Embedding over Linguistic Features for Fake News Detection (WELFake) dataset of 72,134 news articles with 35,028 real and 37,106 fake news. For this, we merged four popular news datasets (i.e. Kaggle, McIntire, Reuters, BuzzFeed Political) to prevent over-fitting of classifiers and to provide more text data for better ML training.

Dataset contains four columns: Serial number (starting from 0); Title (about the text news heading); Text (about the news content); and Label (0 = fake and 1 = real).

There are 78098 data entries in csv file out of which only 72134 entries are accessed as per the data frame.

This dataset is a part of our ongoing research on "Fake News Prediction on Social Media Website" as a doctoral degree program of Mr. Pawan Kumar Verma and is partially supported by the ARTICONF project funded by the European Union’s Horizon 2020 research and innovation program.

This data collection focuses on capturing user-generated content from the popular social network Reddit in 2024. The dataset “Fake News” comprises collected data from 3636 users of Reddit. This dataset consists of .csv .xls, and .xlsx files, containing textual data associated with fake news.

Funded by the EU NextGeneration EU through the Recovery and Resilience Plan for Slovakia under the project No. 09I03-03-V01-000153