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  1. Profiling Fake News Spreaders on Twitter

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    Updated Feb 29, 2020
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Profiling Fake News Spreaders on Twitter

Dataset updated Feb 29, 2020
Dataset provided by
Polytechnic University of Valenciahttp://www.upv.es/


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.

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.

As in previous years, we propose the task from a multilingual perspective:

  • English
  • Spanish

NOTE: Although we recommend to participate in both languages (English and Spanish), it is possible to address the problem just for one language.



The uncompressed dataset consists in a folder per language (en, es). Each folder contains:

  • A XML file per author (Twitter user) with 100 tweets. The name of the XML file correspond to the unique author id.
  • A truth.txt file with the list of authors and the ground truth.

The format of the XML files is:

            Tweet 1 textual contents
            Tweet 2 textual contents

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.



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:


The naming of the output files is up to you. However, we recommend to use the author-id as filename and "xml" as extension.

IMPORTANT! 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.


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.


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.

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:


Within OUTPUT-DIRECTORY, we require two subfolders: en and es, 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:


The naming of the output files is up to you. However, we recommend to use the author-id as filename and "xml" as extension.

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.

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