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CT-FAN: A Multilingual dataset for Fake News Detection
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Data Access: The data in the research collection 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 them only for research purposes.
Summaries, analyses and interpretations of the linguistic properties of the information may be derived and published, provided it is impossible to reconstruct the information from these summaries. You may not try identifying the individuals whose texts are included in this dataset. You may not try to identify the original entry on the fact-checking site. You are not permitted to publish any portion of the dataset besides summary statistics or share it with anyone else.
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Citation
Please cite our work as
@InProceedings{clef-checkthat:2022:task3,
author = {K{\"o}hler, Juliane and Shahi, Gautam Kishore and Stru{\ss}, Julia Maria and Wiegand, Michael and Siegel, Melanie and Mandl, Thomas},
title = "Overview of the {CLEF}-2022 {CheckThat}! Lab Task 3 on Fake News Detection",
year = {2022},
booktitle = "Working Notes of CLEF 2022---Conference and Labs of the Evaluation Forum",
series = {CLEF~'2022},
address = {Bologna, Italy},}
@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 and German.
Task 3: Multi-class fake news detection of news articles (English) Sub-task A would detect fake news designed as a four-class classification problem. Given the text of a news article, determine whether the main claim made in the article is true, partially true, false, or other. The training data will be released in batches and roughly about 1264 articles with the respective label in English language. 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 a lack of evidence about its claims. This category includes articles in dispute and unproven articles.
Cross-Lingual Task (German)
Along with the multi-class task for the English language, we have introduced a task for low-resourced language. We will provide the data for the test in the German language. The idea of the task is to use the English data and the concept of transfer to build a classification model for the German language.
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:
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
Output data format
public_id- Unique identifier of the news article
predicted_rating- predicted class
Sample File
public_id, predicted_rating
1, false
2, true
IMPORTANT!
We have used the data from 2010 to 2022, and the content of fake news is mixed up with several topics like elections, COVID-19 etc.
Baseline: For this task, we have created a baseline system. The baseline system can be found at https://zenodo.org/record/6362498
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 multilingual cross-domain fact check news dataset for covid-19,” in workshop Proceedings of the 14th International AAAI Conference 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
Shahi, G. K., Struß, J. M., & Mandl, T. (2021). Overview of the CLEF-2021 CheckThat! lab task 3 on fake news detection. Working Notes of CLEF.
Nakov, P., Da San Martino, G., Elsayed, T., Barrón-Cedeno, 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 European Conference on Information Retrieval (pp. 639-649). Springer, Cham.
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 International Conference of the Cross-Language Evaluation Forum for European Languages (pp. 264-291). Springer, Cham
CT-FAN-22 corpus: A Multilingual dataset for Fake News Detection
No full textData 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 them only for research purposes. Due to these restrictions, the collection is not open data. Please fill out the form and upload the Data Sharing Agreement at Google Form.
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 and German.
Task 3: 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 1264 articles with the respective label in English language. 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 a lack of evidence about its claims. This category includes articles in dispute and unproven articles.
Cross-Lingual Task (German)
Along with the multi-class task for the English language, we have introduced a task for low resourced language. We will provide the data for test in the German language. The idea of the task is to use the English data and the concept of transfer to build a classification model for the German language.
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:
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
Output data format
public_id- Unique identifier of the news article
predicted_rating- predicted class
Sample File
public_id, predicted_rating
1, false
2, true
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 sources:
Fakenews Classification Datasets
Fake News Detection Challenge KDD 2020
FakeNewsNet
IMPORTANT!
We have used the data from 2010 to 2022, and the content of fake news is mixed up with several topics like elections, 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. here is no limit to the number of submissions, we will evaluate the last submission from each team. Please mention your team name in each submission.
Baseline: For this task, we have created a baseline system. The baseline system can be found at https://zenodo.org/record/6362498
Submission Link: Codalab Page
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 multilingual cross-domain fact check news dataset for covid-19,” in workshop Proceedings of the 14th International AAAI Conference 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
Shahi, G. K., Struß, J. M., & Mandl, T. (2021). Overview of the CLEF-2021 CheckThat! lab task 3 on fake news detection. Working Notes of CLEF.
Nakov, P., Da San Martino, G., Elsayed, T., Barrón-Cedeno, 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 European Conference on Information Retrieval (pp. 639-649). Springer, Cham.
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 International Conference of the Cross-Language Evaluation Forum for European Languages (pp. 264-291). Springer, Cham
Thomas Grisell letter to Thomas Rotch, 2nd mo 19th 1823
No full textThomas Grisell's letter reached the Rotch household several months before the unexpected death of Thomas Rotch in August, 1823. This is the last letter of the series and presumably the author learned of his friend's death before another letter was penned. 7.95" x 10" (20.2 by 25.5 cm
Information Retrieval an der Universität Hildesheim: Optimierung, Evaluierung und Informationsverhalten
No full textDie Arbeitsgruppe Information Retrieval an der Universität Hildesheim besteht seit 1998 und wird von Frau Prof. Dr. Womser-Hacker und Prof. Dr. Thomas Mandl geleitet. Derzeit arbeiten dort sieben wissenschaftliche Mitarbeiter an Promotionen zum Information Retrieval (IR). Darüber hinaus sind am Institut für Informationswissenschaft und Sprachtechnologie (IWiST) noch zwei weitere Professoren und drei promovierte Mitarbeiter beschäftigt.Die Arbeitsgruppe von Prof. Womser-Hacker und Prof. Mandl nimmt eine ganzheitliche Perspektive auf Suchprozesse ein, wobei, neben der Technologie der Benutzer, seine Bedürfnisse und sein Informationsverhalten immer eine zentrale Rolle spielen. Dies spiegelt sich in der Einbettung in die Informationswissenschaft wider, welche schon seit langem aus einer empirischen und benutzerorientierten Sicht die Bedingungen für das Gelingen von Informationsprozessen untersucht. Daraus ergibt sich als zentrales Forschungsthema für das IR in Hildesheim die benutzerorientierte Optimierung von IR-Systemen und deren adäquate Evaluierung. Im Einzelnen arbeiten die Mitglieder der Arbeitsgruppe an Cross-Language Retrieval, mehrsprachigem Opinion Mining, Benchmark-Gestaltung, Evaluierungsmaßen und Analyse der Benutzerzufriedenheit, spezifischen Anforderungen der Fachinformation im Patentwesen und im Bildungsbereich, der Visualisierung in Benutzungsoberflächen für IR sowie in der Analyse von Informationsverhalten im Sinne der Information-Seeking-Forschung
Failed Censures: Ecclesiastical Regulation of Women’s Clothing in Late Medieval Italy
No full textChurchmen in the late thirteenth and early fourteenth centuries tried to regulate the costume of Italian women. These efforts failed, and regulation was largely left thereafter to civic authorities.The published version was published as Chapter 3 in Medieval Clothing and Textiles 5Izbicki, Thomas M. (2009), "Failed Censures: Ecclesiastical Regulation of Women’s Clothing in Late Medieval Italy" in Netherton, Robin and Owen-Crocker, Gale R., eds., Medieval Clothing and Textiles 5 (Boydell Press), 37-53ISBN: 9781843834519 (published book)Peer reviewe
Western medieval legal manuscripts in the collections of the University of Pennsylvania
No full textWestern legal manuscripts of the Middle Ages in North American collections are among the least known to scholars. The University of Pennsylvania has a rich collection of these texts, several of which were in the collection of the historian Henry Charles Lea. Included are works of civil law and canon law, as well as collections of papal letters and guides to pastoral care. The descriptions of most of these manuscripts in the catalog of Norman P. Zacour and Rudolf Hirsch are perfunctory, sometimes erring or omitting valuable information. Other manuscripts were added in recent years in the Lawrence J. Schoenberg Collection. Much of this material is being added to the Franklin online catalog of the University’s libraries, but researchers frequently do not search these digital resources. This article provides more complete guidance to the University’s medieval legal manuscripts than any of the existing catalogs offers, whether in print or online. It also provides updated bibliographic information in print or online. Every manuscript has been examined by the author in situ. Among the important works represented in the collection is the Panormia (a work of canon law often attributed to Ivo of Chartres). Authors present include the curialist Thomas of Capua, canonists Petrus de Braco, William of Pagula, Bernardus Raimundi, Adam of Aldersbach, Raymond of Peñafort, and civil lawyers Baldus de Ubaldis, and Bartolus de Saxoferrato. Three of these manuscripts were owned in the past by Sir Thomas Phillipps
Forbidden Colors in the Regulation of Clerical Dress from the Fourth Lateran Council (1215) to the Time of Nicholas of Cusa (d. 1464)
No full textMedieval canon law attempted to distinguish clergy from the laity by restricting their dress choices. The article focuses on prohibition of wearing red or green on the street. Both colors were identified with the nobility.The published version was published as Chapter 7 in Medieval Clothing and Textiles 1Izbicki, Thomas M. (2005), "Forbidden Colors in the Regulation of Clerical Dress from the Fourth Lateran Council (1215) to the Time of Nicholas of Cusa (d. 1464)" in Netherton, Robin and Owen-Crocker, Gale R., eds., Medieval Clothing and Textiles 1 (Boydell Press),105-114ISBN: 9781843831235 (published book
Thomas Crutchfield account book, 1848-1861
No full textA book containing business accounts, including details about travel expenses and the purchase and sale of lumber as well as other goods and services. The author also catalogs personal spending, the dates and pricing of properties offered for rent, and the purchase and leasing of enslaved people. Many entries are consistent with the business activities of Thomas Crutchfield Sr., who died in 1850. Someone continued to make entries in the book for activities dated up to 1861
Thomas Crutchfield account book, 1848-1861
No full textA book containing business accounts, including details about travel expenses and the purchase and sale of lumber as well as other goods and services. The author also catalogs personal spending, the dates and pricing of properties offered for rent, and the purchase and leasing of enslaved people. Many entries are consistent with the business activities of Thomas Crutchfield Sr., who died in 1850. Someone continued to make entries in the book for activities dated up to 1861
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