2,776 research outputs found
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.
We grant you the right to access the collection's content as described in this agreement. You may not otherwise make unauthorised commercial use of, reproduce, prepare derivative works, distribute copies, perform, or publicly display the collection or parts of it. You are responsible for keeping and storing the data in a way that others cannot access. The data is provided free of charge.
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
Multilingual log analysis: LogCLEF
No full textThe current lack of recent and long-term query logs makes
the verifiability and repeatability of log analysis experiments very limited. A first attempt in this direction has been made within the Cross-Language Evaluation Forum in 2009 in a track named LogCLEF which aims to stimulate research on user behaviour in multilingual environments and promote standard evaluation collections of log data. We report on similarities and differences of the most recent activities for LogCLEF
Data Repository for: On Reducing the Amount of Samples Required for Training of QNNs
No full text
Simulation experiment data for training Quantum Neural Networks (QNNs) using entangled datasets.
The experiments investigate the validity of the lower bounds for the expected risk after training QNNs given by the extensions to the Quantum No-Free-Lunch theorem presented in the related publication. The QNNs are trained with (i) samples of varying Schmidt rank, (ii) orthogonal samples of fixed Schmidt rank and (iii) linearly dependent samples of fixed Schmidt rank.
The dataset contains raw experiment data (directory "raw_data"), analyzed mean risks and errors (directory "plot_data") and the resulting plots (directory "plots").
Experiments:
The experiments train QNNs using various compositions of training samples on a simulator and extract the risk after training to compute average risks.
Experiment 1: Trains QNNs using entangled training samples of varying Schmidt rank. The average Schmidt rank and the number of training samples are controlled.
Raw data: average_rank_results.zip; Computed average risks: avg_rank_risks.npy; Computed average losses: avg_rank_losses.npy; Plotted average risks: avg_rank_experiments.pdf; Plotted average losses: avg_rank_losses.pdf.
Experiment 2: Trains QNNs using entangled orthogonal training samples. The number of training samples is controlled and the Schmidt rank is fixed such that d=r*t for the dimension d of the Hilbert space.
Raw data: orthogonal_results.zip; Computed average risks: orthogonal_exp_points.npy; Plotted average risks: orthogonal_experiments.pdf.
Experiment 3: Trains QNNs using entangled linearly dependent training samples. The number of training samples is controlled and the Schmidt rank is fixed such that d=r*t for the dimension d of the Hilbert space.
Raw data: not_linearly_independent_results.zip; Computed average risks: nlihx_exp_points.npy; Plotted average risks: nlihx_experiments.pdf
Additionally, this repository contains the reproduction data for Figure 1 (phases_in_orthogonal_training.zip). This file contains the training data, the target unitary and the resulting hypothesis unitary for orthogonal training samples of (i) high risk and (ii) low risk.
For the code to reproduce and analyze the experiments see the Code repository.
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Griesbaum, J.; Mandl, T.; Womser-Hacker, C. (Hrsg.) Information und Wissen: global, sozial und frei?: Proceedings des 12. Internationalen Symposiums für Informationswissenschaft (ISI 2011) Hildesheim, 9.–11. März 2011. Boizenburg: Hülsbusch, 2011. 532 S. ISBN 978-3-940317-91-9.
No full textReview of the proceedings volume "Information
und Wissen: global, sozial und frei? Proceedings des 12. Internationalen
Symposiums für Informationswissenschaft (ISI 2011) Hildesheim, 9.–11. März 2011", edited by Griesbaum, J., Mandl, T. and Womser-Hacker, C
Distant Viewing-Forschung mit digitalisierten Kinderbüchern. Voraussetzungen, Herausforderungen und Ansätze
No full textHelm W, Mandl T, Putjenter S, Schmideler S, Zellhöfer D. Distant Viewing-Forschung mit digitalisierten Kinderbüchern. Voraussetzungen, Herausforderungen und Ansätze. b.i.t.online: Bibliothek, Information, Technologie. 2019;22(2):127-134
Latency is the major determinant of UDP-glucuronosyltransferase activity in isolated hepatocytes
No full textThe glucuronidation of p-nitrophenol was measured in intact, saponin- and alamethicin-treated isolated mouse hepatocytes. In saponin-permeabilized cells the elevation of extrareticular UDP-glucuronic acid concentration enhanced the rate of glucuronidation threefold. When intracellular membranes were also permeabilized by alamethicin, a further tenfold increase in the glucuronidation of p-nitrophenol was present. Parallel measurements of the ER mannose 6-phosphatase activity revealed that saponin selectively permeabilized the plasma membrane, whereas alamethicin permeabilized both plasma membrane and ER membranes. The inhibition of p-nitrophenol glucuronidation by dbcAMP in intact hepatocytes was still present in saponin-treated cells and disappeared in alamethicin-permeabilized hepatocytes. It is suggested that the permeability of the endoplasmic reticulum membrane is a major determinant of glucuronidation not only in microsomes but in isolated hepatocytes as well. © 1993
Using TF-IDF n-gram and word embedding cluster ensembles for author profiling: Notebook for PAN at CLEF 2017
No full textThis paper presents our approach and results for the 2017 PAN Author Profiling Shared Task. Language-specific corpora were provided for four langauges: Spanish, English, Portuguese, and Arabic. Each corpus consisted of tweets authored by a number of Twitter users labeled with their gender and the specific variant of their language which was used in the documents (e.g. Brazilian or European Portuguese). The task was to develop a system to infer the same attributes for unseen Twitter users. Our system employs an ensemble of two probabilistic classifiers: a Logistic regression classifier trained on TF-IDF transformed n-grams and a Gaussian Process classifier trained on word embedding clusters derived for an additional, external corpus of tweets
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