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.

According to a digital news consumption survey conducted in India in **********, more than ** percent of the respondents claimed that they sometimes encountered potentially fake news online. In contrast, ***** percent of the surveyed consumers stated that they never encountered potentially fake news online. In recent years, the number of fake news-related incidents in India has been on the rise.

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

Perhaps unsurprisingly, the main traffic source for false information online is social media, which generates 42 percent of fake news traffic. The nature of social networks, most notably the ease of sharing content, allows fake news to spread at a rapid rate – an issue further exacerbated by the fact that many U.S. adults sometimes believe fake news to be real.

Fake news: an ongoing problem

The presence of fake news would be less of an issue if users were more aware of how to identify it and were aware of the risks of sharing such content. Many U.S. news consumers have shared fake news online, and worryingly, ten percent did so deliberately. Adults who are part of that ten percent are just a small portion of people in the United States, and elsewhere in the world, who are responsible for spreading false information. More than 30 percent of U.S. children and teenagers have shared a fake news story online, and over 50 percent of adults in selected countries worldwide have wrongly believed a fake news story.

The result of adults and young consumers alike not only believing fake news, but actively sharing it, is that small, illegitimate websites producing such content are able to grow more successful. Such websites have the potential to tarnish or seriously damage the reputation of any persons mentioned within a fake news article, promote events or policies which do not exist, and mislead readers about important topics they are trying to keep up with. A 2019 survey revealed that most adults believe that fake news and misinformation will get worse in the next five years, and the sad truth is that this will likely be the case unless news consumers grow more discerning about what they post and share online.

In a survey carried out in the United Kingdom in September 2021, five percent of respondents said that they had encountered news or information about the coronavirus that they believed to be false or misleading ** times or more per day in the last week. This marked an increase of *** percent from the share who said the same in the survey wave held in September 2020. Meanwhile, ** percent of respondents believed they had seen fake news about COVID-19 a few times a week in September 2021.

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

According to a global study conducted in 2019, ** percent of respondents felt that there was a fair extent or great deal of fake news on online websites and platforms. By comparison, ** percent less said the same about TV, radio, newspapers, and magazines. Traditional media in general is still considered more trustworthy than online formats, despite social networks being the preferred choice for many.

Meanwhile, as some consumers around the world now turn to influencers for news instead of journalists, the risk of them being exposed to inaccurate, incorrect, or deliberately false information continues to grow, and journalists face pressure to battle fake content whilst finding new ways to keep audiences engaged.

Fake news and journalism

More than ** percent of journalists responding to a global survey believed that the public had lost trust in the media over the past year. Whilst the reasons for this are many, the role of fake news cannot be undermined, particularly given the speed with which false content can spread and reach vulnerable or misinformed audiences. Either unintentionally or deliberately, fake news is often shared by those who encounter it, which only serves to worsen the problem. Indeed, journalists consider regular citizens to be the main source of disinformation, followed by political leaders and internet trolls.

Despite the threats fake news poses, journalists themselves feel that concerns about disinformation could positively impact the quality of journalism. There are also growing expectations from the public and journalists alike for governments and companies to do more to help boost quality journalism and curb the dissemination and influence of fake news. News industry leaders rated Google as being the best platform for supporting journalism, but the likes of Amazon and Snapchat have a long way to go before organizations consider them reliable in this respect.

Dataset

This dataset was created by Akash_Sandhu4x4

Released under MIT

Contents

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

Introduction

This describes two fake news datasets covering seven different news domains. One of the datasets is collected by combining manual and crowdsourced annotation approaches (FakeNewsAMT), while the second is collected directly from the web (Celebrity).

Data collection

The FakeNewsDatabase dataset contains news in six different domains: technology, education, business, sports, politics, and entertainment. The legitimate news included in the dataset were collected from a variety of mainstream news websites predominantly in the US such as the ABCNews, CNN, USAToday, NewYorkTimes, FoxNews, Bloomberg, and CNET among others. The fake news included in this dataset consist of fake versions of the legitimate news in the dataset, written using Mechanical Turk. More details on the data collection are provided in section 3 of the paper.

The Celebrity dataset contain news about celebrities (actors, singers, socialites, and politicians). The legitimate news in the dataset were obtained from entertainment, fashion and style news sections in mainstream news websites and from entertainment magazines websites. The fake news were obtained from gossip websites such as Entertainment Weekly, People Magazine, RadarOnline, and other tabloid and entertainment-oriented publications. The news articles were collected in pairs, with one article being legitimate and the other fake (rumors and false reports). The articles were manually verified using gossip-checking sites such as "GossipCop.com", and also cross-referenced with information from other entertainment news sources on the web.

The data directory contains two fake news datasets:

• Celebrity The fake and legitimate news are provided in two separate folders. The fake and legitimate labels are also provided as part of the filename.

• FakeNewsAMT The fake and legitimate news are provided in two separate folders. Each folder contains 40 news from six different domains: technology, education, business, sports, politics, and entertainment. The file names indicate the news domain: business (biz), education (edu), entertainment (entmt), politics (polit), sports (sports) and technology (tech). The fake and legitimate labels are also provided as part of the filename.

Dataset citation :

@article{Perez-Rosas18Automatic, author = {Ver\’{o}nica P\'{e}rez-Rosas, Bennett Kleinberg, Alexandra Lefevre, Rada Mihalcea}, title = {Automatic Detection of Fake News}, journal = {International Conference on Computational Linguistics (COLING)}, year = {2018} }

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.

According a survey held in April 2023, the share of people aged 18 years and above in the United States who were very confident in their ability to distinguish real news from false information amounted to 23 percent. A further 52 percent were somewhat confident that they were able to identify misinformation, whereas just five percent had little faith in themselves to determine facts from fake content.

Dataset Card for Election-Related Fake News Classification

  Dataset Summary

This dataset is designed for the task of fake news classification in the context of elections. It consists of news articles, social media posts, and other text sources related to various elections worldwide. Each entry in the dataset is labeled as 'fake' or 'real' based on its content and the veracity of the information presented. https://arxiv.org/abs/2312.03750

  Languages

English… See the full description on the dataset page: https://huggingface.co/datasets/newsmediabias/fake_news_elections_labelled_data.

In this dataset have to part combined namely fake news and true news. fake news collected from Kaggle and some true news collected form IEEE Data port. Therefor some true news data required to optimize with the fake news. After that i have collect some true news from different trusted online site. Finally i have concat the Fake and True news as a single dataset for the purpose to help the Researchers further if they want to research by taken this topic.

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.

Online-Plattformen und andere Internetdienste bieten den Menschen neue Möglichkeiten, sich zu verbinden, zu diskutieren und Informationen zu sammeln. Die Verbreitung von Nachrichten, die die Leser absichtlich irreführen, ist jedoch zu einem zunehmenden Problem für das Funktionieren unserer Demokratien geworden, was sich auf das Verständnis der Realität auswirkt. Im Juni 2017 nahm das Europäische Parlament eine Entschließung an, in der die Europäische Kommission aufgefordert wird, die derzeitige Situation und den Rechtsrahmen in Bezug auf Falschmeldungen eingehend zu analysieren und zu prüfen, ob legislative Maßnahmen ergriffen werden können, um die Verbreitung und Verbreitung gefälschter Inhalte zu begrenzen. Mit diesem Flash-Eurobarometer soll das Bewusstsein und die Einstellung der EU-Bürger zur Existenz von Falschmeldungen und Desinformation im Internet untersucht werden. Er befasst sich mit folgenden Fragen: — Grad des Vertrauens in Nachrichten und Informationen, auf die über verschiedene Kanäle zugegriffen wird; — Die Wahrnehmung der Menschen, wie oft sie auf Nachrichten oder Informationen stoßen, die irreführend oder falsch sind; — Das Vertrauen der Öffentlichkeit in die Identifizierung irreführender oder falscher Nachrichten oder Informationen; — Die Ansichten der Menschen über das Ausmaß des Problems sowohl in ihrem eigenen Land als auch für die Demokratie im Allgemeinen; — Ansichten, welche Institutionen und Medienakteure handeln sollten, um die Verbreitung von Falschmeldungen zu stoppen.

Die Ergebnisse nach Volumes werden wie folgt verteilt:

• Band A: Länder
• Volumen AA: Ländergruppen
• Band A' (AP): Trends
• Volumen AA' (AAP): Trends von Gruppen von Ländern
• Band B: EU/Soziodemografie
• Band B' (BP): Trends in der EU/soziodemografischen
• Band C: Land/Soziodemografie — Forscher können sich auch an GESIS – Leibniz-Institut für Sozialwissenschaften wenden: https://www.gesis.org/eurobarometer

The chart shows that Americans over 65 were more likely to share fake news to their Facebook friends, regardless of their education, ideology, and partisanship. The oldest age group was likely to share nearly seven times as many articles from fake news domains on Facebook as those in the youngest age group, or about 2.3 times as many as those in the next-oldest age group. The data regarding the age group 18-29 and 30-44 are not displayed in the source, therefore the value of data in this chart are approximate, determined with pixel count.

Systematic literature review data (2011-2020) on "fake news", "disinformation", "misinformation" and "literacy". Wos, Scopus and ERIC databases.

Dataset

This dataset was created by Abdul Manan

Contents

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.

A study held in early 2024 found that more than a third of surveyed consumers in selected countries worldwide had witnessed false news about politics in the week running to the survey. Suspicious or false COVID-19 news was also a problem. False news False news is often at its most insidious when it distorts or misrepresents information about key topics, such as public health, global conflicts, and elections. With 2024 set to be a significant year of political change, with elections taking place worldwide, trustworthy and verifiable information will be crucial. In the U.S., trust in news sources for information about the 2024 presidential election is patchy. Republicans and Independents are notably less trusting of news about the topic than their Democrat-voting peers, with only around 40 percent expressing trust in most news sources in the survey. Social media fared the least well in this respect with just a third of surveyed adults saying that they had faith in such sites to deliver trustworthy updates on the 2024 election. A separate survey revealed that older adults were the least likely to trust the news media for election news. This is something that publishers can bear in mind when targeting audiences with updates and campaign information. Distorting the truth: the impact of false news Aside from reading (and potentially believing) false information, consumers are also at risk of accidentally sharing false news and therefore contributing to its spread. One way in which the dissemination of false news could be stemmed is by consumers educating themselves on how to identify suspicious content, however government intervention has also been tabled. Consumers are split on whether or not governments should take steps to restrict false news, partly due to concerns about the need to protect freedom of information.