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31 results for “fake news”
Dataset-COVID-19 Fake News: A Systematic Literature Review using "SmartLitReview"
<p>The final dataset from the systematic literature review of COVID-19 fake news using the tool <strong>"SmartLitReview"</strong>.</p>
Supersharers of fake news on Twitter
<p>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.</p>
Exploring agent interaction patterns in the comment sections of fake and real news
<p>The code and dataset provided for the paper <em>"Exploring agent interaction patterns in the comment sections of fake and real news"</em> contain all information about fake and real news, as well as the complete code for constructing comment networks and user network experiments.</p>
Exploring agent interaction patterns in the comment sections of fake and real news
<p>The code and dataset provided for the paper <em>"Exploring agent interaction patterns in the comment sections of fake and real news"</em> contain all information about fake and real news, as well as the complete code for constructing comment networks and user network experiments.</p>
The Effect of a Protective Message on Critical Evaluation of Fake News
ClinicalTrials.gov study NCT05030883. IPD Sharing: UNDECIDED. Countries: 1. Publications: 1.
Supersharers of fake news on Twitter
Open the record for dataset details and reuse information.
FactDrill: A Data Repository of Fact-checked Social Media Content to Study Fake News Incidents in India
<p>A dataset containing 22,435 fact-checked social media content to study fake news incidents in India. The dataset comprises news stories from 2013 to the year 2020, covering 13 different languages spoken in the country. There are 14 different attributes present in the dataset.</p>
Profiling Fake News Spreaders on Twitter
<p><strong>Task</strong></p> <p>Fake news has become one of the main threats of our society. Although fake news is not a new phenomenon, the exponential growth of social media has offered an easy platform for their fast propagation. A great amount of fake news, and rumors are propagated in online social networks with the aim, usually, to deceive users and formulate specific opinions. Users play a critical role in the creation and propagation of fake news online by consuming and sharing articles with inaccurate information either intentionally or unintentionally. To this end, in this task, we aim at identifying possible fake news spreaders on social media as a first step towards preventing fake news from being propagated among online users.</p> <p>After having addressed several aspects of author profiling in social media from 2013 to 2019 (bot detection, age and gender, also together with personality, gender and language variety, and gender from a multimodality perspective), this year we aim at investigating if it is possbile to discriminate authors that have shared some fake news in the past from those that, to the best of our knowledge, have never done it.</p> <p>As in previous years, we propose the task from a <strong>multilingual</strong> perspective:</p> <ul> <li>English</li> <li>Spanish</li> </ul> <p><strong>NOTE:</strong> Although we recommend to participate in both languages (English and Spanish), it is possible to address the problem just for one language.</p> <p><strong>Data</strong></p> <p><em>Input</em></p> <p>The uncompressed dataset consists in a folder per language (en, es). Each folder contains:</p> <ul> <li>A XML file per author (Twitter user) with 100 tweets. The name of the XML file correspond to the unique author id.</li> <li>A truth.txt file with the list of authors and the ground truth.</li> </ul> <p>The format of the XML files is:</p> <pre> <author lang="en"> <documents> <document>Tweet 1 textual contents</document> <document>Tweet 2 textual contents</document> ... </documents> </author> </pre> <p>The format of the truth.txt file is as follows. The first column corresponds to the author id. The second column contains the truth label.</p> <pre> b2d5748083d6fdffec6c2d68d4d4442d:::0 2bed15d46872169dc7deaf8d2b43a56:::0 8234ac5cca1aed3f9029277b2cb851b:::1 5ccd228e21485568016b4ee82deb0d28:::0 60d068f9cafb656431e62a6542de2dc0:::1 ... </pre> <p><em>Output</em></p> <p>Your software must take as input the absolute path to an unpacked dataset, and has to output for each document of the dataset a corresponding XML file that looks like this:</p> <pre> <author id="author-id" lang="en|es" type="0|1" /> </pre> <p>The naming of the output files is up to you. However, we recommend to use the author-id as filename and "xml" as extension.</p> <p><strong>IMPORTANT!</strong> Languages should not be mixed. A folder should be created for each language and place inside only the files with the prediction for this language.</p> <p><strong>Evaluation</strong></p> <p>The performance of your system will be ranked by accuracy. For each language, we will calculate individual accuracies in discriminating between the two classes. Finally, we will average the accuracy values per language to obtain the final ranking.</p> <p><strong>Submission</strong></p> <p>Once you finished tuning your approach on the validation set, your software will be tested on the test set. During the competition, the test set will not be released publicly. Instead, we ask you to submit your software for evaluation at our site as described below.</p> <p>We ask you to prepare your software so that it can be executed via command line calls. The command shall take as input (i) an absolute path to the directory of the test corpus and (ii) an absolute path to an empty output directory:</p> <pre><code>mySoftware -i INPUT-DIRECTORY -o OUTPUT-DIRECTORY</code></pre> <p>Within <code>OUTPUT-DIRECTORY</code>, we require two subfolders: <code>en</code> and <code>es</code>, one folder per language, respectively. As the provided output directory is guaranteed to be empty, your software needs to create those subfolders. Within each of these subfolders, you need to create one xml file per author. The xml file looks like this: </p> <pre> <author id="author-id" lang="en|es" type="0|1" /> </pre> <p>The naming of the output files is up to you. However, we recommend to use the author-id as filename and "xml" as extension.</p> <p>Note: By submitting your software you retain full copyrights. You agree to grant us usage rights only for the purpose of the PAN competition. We agree not to share your software with a third party or use it for other purposes than the PAN competition.</p> <p><strong>Related Work</strong></p> <ul> <li>Bilal Ghanem, Paolo Rosso, Francisco Rangel. <a href="https://arxiv.org/abs/1908.09951">An Emotional Analysis of False Information in Social Media and News Articles. </a>arXiv preprint arXiv:1908.09951 (2019). ACM Transactions on Internet Technology (TOIT). In Press.</li> <li>Anastasia Giachanou, Paolo Rosso, Fabio Crestani. <a href="https://dl.acm.org/doi/10.1145/3331184.3331285">Leveraging Emotional Signals for Credibility Detection. </a>Proceedings of the 42nd International ACM Conference on Research and Development in Information Retrieval (SIGIR). pp 877–880. (2019)</li> <li>Andre Guess, Jonathan Nagler, and Joshua Tucker. <a href="http://advances.sciencemag.org/content/5/1/eaau4586.abstract">Less than you think: Prevalence and predictors of fake news dissemination on Facebook.</a> Science Advances vol. 5 (2019)</li> <li>Andrew Hall, Loren Terveen, Aaron Halfaker. <a href="https://dl.acm.org/citation.cfm?id=3274333">Bot Detection in Wikidata Using Behavioral and Other Informal Cues. </a>Proceedings of the ACM on Human-Computer Interaction. 2018 Nov 1;2(CSCW):64.</li> <li>Kashyap Popat, Subhabrata Mukherjee, Andrew Yates, Gerhard Weikum. <a href="https://www.aclweb.org/anthology/D18-1003/">DeClarE: Debunking Fake News and False Claims using Evidence-Aware Deep Learning. </a>Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing (EMNLP). pp 22-32. (2018)</li> <li>Francisco Rangel and Paolo Rosso. <a href="http://ceur-ws.org/Vol-2380/paper_263.pdf">Overview of the 7th Author Profiling Task at PAN 2019: Bots and Gender Profiling in Twitter. </a>In: L. Cappellato, N. Ferro, D. E. Losada and H. Müller (eds.) CLEF 2019 Labs and Workshops, Notebook Papers. CEUR Workshop Proceedings.CEUR-WS.org, vol. 2380</li> <li>Francisco Rangel, Paolo Rosso, Martin Potthast, Benno Stein. <a href="http://ceur-ws.org/Vol-2125/invited_paper_15.pdf">Overview of the 6th author profiling task at pan 2018: multimodal gender identification in Twitter.</a> In: CLEF 2018 Labs and Workshops, Notebook Papers. CEUR Workshop Proceedings. CEUR-WS.org, vol. 2125.</li> <li>Francisco Rangel, Paolo Rosso, Martin Potthast, Benno Stein. <a href="http://ceur-ws.org/Vol-1866/invited_paper_11.pdf">Overview of the 5th Author Profiling Task at PAN 2017: Gender and Language Variety Identification in Twitter.</a> In: Cappellato L., Ferro N., Goeuriot L, Mandl T. (Eds.) CLEF 2017 Labs and Workshops, Notebook Papers. CEUR Workshop Proceedings. CEUR-WS.org, vol. 1866.</li> <li>Francisco Rangel, Paolo Rosso, Ben Verhoeven, Walter Daelemans, Martin Pottast, Benno Stein. <a href="http://ceur-ws.org/Vol-1609/16090750.pdf">Overview of the 4th Author Profiling Task at PAN 2016: Cross-Genre Evaluations.</a> In: Balog K., Capellato L., Ferro N., Macdonald C. (Eds.) CLEF 2016 Labs and Workshops, Notebook Papers. CEUR Workshop Proceedings. CEUR-WS.org, vol. 1609, pp. 750-784</li> <li>Francisco Rangel, Fabio Celli, Paolo Rosso, Martin Pottast, Benno Stein, Walter Daelemans. <a href="https://github.com/autoritas/RD-Lab/blob/master/doc/papers/2015%20-%20PAN/pan15-ap-overview.pdf">Overview of the 3rd Author Profiling Task at PAN 2015.</a>In: Linda Cappelato and Nicola Ferro and Gareth Jones and Eric San Juan (Eds.): CLEF 2015 Labs and Workshops, Notebook Papers, 8-11 September, Toulouse, France. CEUR Workshop Proceedings. ISSN 1613-0073, http://ceur-ws.org/Vol-1391/,2015.</li> <li>Francisco Rangel, Paolo Rosso, Irina Chugur, Martin Potthast, Martin Trenkmann, Benno Stein, Ben Verhoeven, Walter Daelemans. <a href="http://ceur-ws.org/Vol-1180/CLEF2014wn-Pan-RangelEt2014.pdf">Overview of the 2nd Author Profiling Task at PAN 2014.</a> In: Cappellato L., Ferro N., Halvey M., Kraaij W. (Eds.) CLEF 2014 Labs and Workshops, Notebook Papers. CEUR-WS.org, vol. 1180, pp. 898-827.</li> <li>Francisco Rangel, Paolo Rosso, Moshe Koppel, Efstatios Stamatatos, Giacomo Inches. <a href="http://ceur-ws.org/Vol-1179/CLEF2013wn-PAN-RangelEt2013.pdf">Overview of the Author Profiling Task at PAN 2013.</a> In: Forner P., Navigli R., Tufis D. (Eds.)Notebook Papers of CLEF 2013 LABs and Workshops. CEUR-WS.org, vol. 1179</li> <li>Francisco Rangel and Paolo Rosso <a href="https://ojs.letras.up.pt/ojs/index.php/LLLD/article/download/6119/5761">On the Implications of the General Data Protection Regulation on the Organisation of Evaluation Tasks. </a>In: Language and Law / Linguagem e Direito, Vol. 5(2), pp. 80-102</li> <li>Kai Shu, Suhang Wang, and Huan Liu. <a href="http://www.public.asu.edu/~skai2/papers/fake_news_user.pdf">Understanding user profiles on social media for fake news detection. </a>Proceedings of the IEEE Conference on Multimedia Information Processing and Retrieval (MIPR), pp. 430--435 (2018)</li> <li>Kai Shu, Amy Sliva, Suhang Wang, Jiliang Tang, and Huan Liu. <a href="https://dl.acm.org/doi/10.1145/3137597.3137600">Fake News Detection on Social Media: A Data Mining Perspective. </a>ACM SIGKDD Explorations Newsletter. (2017)</li> </ul>
Turkish Political Fake News Dataset (TPFND)
<div> <div> <div> <div> <div> <div> <div> <div> <p>The Turkish Political Fake News Dataset (TPFND) presented in this repository was developed as part of the doctoral dissertation.</p> <p>This repository contains the following components:</p> <ol> <li> <p><strong>Original TPFND</strong>: The Turkish Political Fake News Dataset (TPFND) is a meticulously curated dataset developed to address the scarcity of high-quality Turkish political fake news data. The dataset reflects the real-world class imbalance, with 2,308 instances (25%) of verified political fake news and 6,922 instances (75%) of factual political news. The classification of fake news is grounded in evidence from reputable Turkish fact-checking organizations, ensuring the dataset's credibility and reliability.</p> </li> <li> <p><strong>Augmented TPFND</strong>: To mitigate the inherent class imbalance in the original TPFND, this repository includes an augmented version of the dataset. The augmentation process employed a large language model (LLM), specifically the Turkish LLaMA-3 8B model, to generate synthetic samples of Turkish political fake news. This approach effectively increased the representation of the minority class, raising the proportion of fake news instances from 25% to 40% within the augmented dataset.</p> </li> <li> <p><strong>Synthetic Data</strong>: The synthetic data generated by the LLM is also provided in this repository. This synthetic data was carefully calibrated to closely mimic the characteristics and nuances of the original fake news samples, ensuring the integrity and authenticity of the augmented dataset.</p> </li> </ol> <p>The TPFND and its augmented version, along with the synthetic data, serve as valuable resources for researchers and practitioners working on fake news detection, particularly in the Turkish context. The dataset's rigorous development, the incorporation of LLM-generated synthetic data, and the addressing of class imbalance issues make this repository a significant contribution to the field of Turkish political fake news research.</p> <p>Researchers can leverage this dataset and the accompanying synthetic data to develop and evaluate advanced machine learning models for fake news detection. The availability of a controlled, high-quality dataset, coupled with the synthetic data, enables the exploration of novel techniques to enhance the accuracy and robustness of fake news detection, ultimately supporting the fight against the spread of fake news in the digital age.</p> </div> </div> </div> </div> </div> </div> </div> </div> <div> <div> <div> <div> <div> <div> <div> <div> </div> </div> </div> </div> </div> </div> </div> </div>
CT-FAN-22 corpus: A Multilingual dataset for Fake News Detection
<p><strong>Data Access: </strong>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 <a href="https://drive.google.com/file/d/1QU-rw4D26r3F04FB63hTvToxOvDKaKdv/view?usp=sharing">Data Sharing Agreement</a> and send the signed form to <a href="mailto:fakenewstask@gmail.com">fakenewstask@gmail.com</a> .</p> <p><strong>Citation</strong></p> <p>Please cite our work as</p> <pre>@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} }</pre> <p><strong>Problem Definition:</strong> 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 <strong>English and German.</strong></p> <p><strong>Subtask 3:</strong> <strong>Multi-class fake news detection of news articles (English)</strong> 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:</p> <ul> <li> <p>False - The main claim made in an article is untrue.</p> </li> <li> <p>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.</p> </li> <li> <p>True - This rating indicates that the primary elements of the main claim are demonstrably true.</p> </li> <li> <p>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.</p> </li> </ul> <p><strong>Input Data</strong></p> <p>The data will be provided in the format of Id, title, text, rating, the domain; the description of the columns is as follows:</p> <p><strong>Task 3</strong></p> <ul> <li>ID- Unique identifier of the news article</li> <li>Title- Title of the news article</li> <li>text- Text mentioned inside the news article</li> <li>our rating - class of the news article as false, partially false, true, other</li> </ul> <p><strong>Output data format</strong></p> <p><strong>Task 3</strong></p> <ul> <li>public_id- Unique identifier of the news article</li> <li>predicted_rating- predicted class</li> </ul> <p>Sample File</p> <pre><code>public_id, predicted_rating 1, false 2, true</code></pre> <p>Sample file</p> <pre><code>public_id, predicted_domain 1, health 2, crime</code></pre> <p><strong>Additional data for Training</strong></p> <p>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 sources:</p> <ul> <li><a href="https://www.kaggle.com/liberoliber/onion-notonion-datasets">Fakenews Classification Datasets</a></li> <li><a href="https://www.kaggle.com/c/fakenewskdd2020/overview">Fake News Detection Challenge KDD 2020</a></li> <li><a href="https://www.kaggle.com/mdepak/fakenewsnet?select=PolitiFact_real_news_content.csv">FakeNewsNet</a></li> </ul> <p><strong>IMPORTANT! </strong></p> <ol> <li>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.</li> </ol> <p><strong>Evaluation Metrics</strong></p> <p>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.</p> <p><strong>Submission Link: </strong><a href="https://codalab.org/">Coming soon</a></p> <p><strong>Related Work</strong></p> <ul> <li>Shahi, G. K., Struß, J. M., & Mandl, T. (2021). Overview of the CLEF-2021 CheckThat! lab task 3 on fake news detection. <em>Working Notes of CLEF</em>.</li> <li>Nakov, P., Da San Martino, G., Elsayed, T., Barrón-Cedeño, 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 <em>European Conference on Information Retrieval</em> (pp. 639-649). Springer, Cham.</li> <li>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 <em>International Conference of the Cross-Language Evaluation Forum for European Languages</em> (pp. 264-291). Springer, Cham.</li> <li>Shahi GK. AMUSED: An Annotation Framework of Multi-modal Social Media Data. arXiv preprint arXiv:2010.00502. 2020 Oct 1.<a href="https://arxiv.org/pdf/2010.00502.pdf">https://arxiv.org/pdf/2010.00502.pdf</a></li> <li>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. <a href="http://workshop-proceedings.icwsm.org/abstract?id=2020_14">http://workshop-proceedings.icwsm.org/abstract?id=2020_14</a></li> <li>Shahi, G. K., Dirkson, A., & Majchrzak, T. A. (2021). An exploratory study of covid-19 misinformation on twitter. <em>Online Social Networks and Media</em>, <em>22</em>, 100104. doi: <a href="https://dx.doi.org/10.1016%2Fj.osnem.2020.100104">10.1016/j.osnem.2020.100104</a></li> </ul>
Restricted access data for the paper Fake News on Twitter During the 2016 U.S. Presidential Election
<p>Restricted access data for replicating results in the paper Fake News on Twitter During the 2016 U.S. Presidential Election.</p>
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