Data and Evaluation

For both tracks, we split the data into training and testing partitions. For developing their methods, participants will use the training partition, and subsequently the test partition will be used to evaluate the participant methods and to determine the winner of the challenge. For ranking participants, we will use the F1 measure over the class of interest.

Training corpus

The training data file is password-protected; to obtain the password you first need to be registered as participant.

Evaluation rules

The performance of your fake news detection solution will be ranked by the F1 measure on the positive class.

The performance of your aggressive detection solution will the ranked by the F1 measure on the aggressive class.

Runs for Track 1 will be received from 14th April 0:01 until 30th April 23:59 (-0600 UTC)Runs for Track 2 will be received from 14th April 0:01 until 30th April, 23:59 (-0600 UTC)

Participants are allowed to submit up to two runs for each track: one primary and one secondary. The participants must clearly flag each of the two.

Output submission

Submissions formatted as described below and sent via email to the account: mex.a3t@gmail.com

Your software has to output for each task of the dataset a corresponding txt file. The file must contain one line per classified instance. Each line looks like this:

"TaskName"\t"IdentifierOfAnInstance"\t"Class"\n

It's important to respect the format with the " character, \t (tabulator) and \n (linux enter). The naming of the output files is up to you, we recommend to use the author and a run's identifier as filename with "txt" as extension.

For the fake news track the possible labels are:

TaskName: fakenews
IdentifierOfAnInstance: NumberOfDocument
Class: {True, Fake}
Output example:

"fakenews" "1" "True" "fakenews" "2" "Fake" "fakenews" "3" "Fake" "fakenews" "4" "True""fakenews" "5" "True"

For the aggressiveness track the possible labels are:

TaskName: aggressiveness
IdentifierOfAnInstance: tweet-NumberOfLine
- where NumberOfLine is the number line of the each tweet in the test file.
Class: {0, 1}
Output example:

"aggressiveness" "tweet-1" "1" "aggressiveness" "tweet-2" "0" "aggressiveness" "tweet-3" "0" "aggressiveness" "tweet-4" "1" "aggressiveness" "tweet-5" "0"

A submission failing the format checking will be considered null.

Paper submission

Participants of the tasks will be given the opportunity to write a paper that describes their system, resources used, results, and analysis that will be part of the official IberLEF-2020 proceedings. The paper is to be FOUR pages long plus two pages at most for references, and are required to be formatted in the Springer LNCS format (see http://www.springer.de/comp/lncs/authors.html).

Papers must be written in English.