This is the website for the OffensEval 2019 shared task organized at SemEval 2019.
Test Data Released for Task A (15-Jan-2019)
The testing period for task A has begun! You can download the test set by going to the 'Participate' tab above, then go to the "Download Datasets" section from the menu on the left. The zip file is available by clicking the "Public Data" button.
You have 72 hours to make your submissions here. Instructions are available in the "Submission Instructions" page.
Offensive language is pervasive in social media. Individuals frequently take advantage of the perceived anonymity of computer-mediated communication, using this to engage in behavior that many of them would not consider in real life. Online communities, social media platforms, and technology companies have been investing heavily in ways to cope with offensive language to prevent abusive behavior in social media.
One of the most effective strategies for tackling this problem is to use computational methods to identify offense, aggression, and hate speech in user-generated content (e.g. posts, comments, microblogs, etc.). This topic has attracted significant attention in recent years as evidenced in recent publications (Waseem et al. 2017; Davidson et al., 2017, Malmasi and Zampieri, 2018, Kumar et al. 2018) and workshops such as ALW and TRAC.
In OffensEval we break down offensive content into three sub-tasks taking the type and target of offenses into account.
The data is retrieved from social media and distributed in tab separated format. The trial and traininga data are available in the "Participate" tab. Please register to the competition to download the files.
Participants are allowed to use external resources and other datasets for this task. Please indicate which resources were used when submitting your results.
The system will be open for 72 hours during each phase. PS: The dataset in CodaLab for sub-task B are earlier. We will release the data on the 22nd.
Davidson, T., Warmsley, D., Macy, M. and Weber, I. (2017) Automated Hate Speech Detection and the Problem of Offensive Language. Proceedings of ICWSM.
Kumar, R., Ojha, A.K., Malmasi, S. and Zampieri, M. (2018) Benchmarking Aggression Identification in Social Media. In Proceedings of the First Workshop on Trolling, Aggression and Cyberbullying (TRAC). pp. 1-11.
Malmasi, S., Zampieri, M. (2018) Challenges in Discriminating Profanity from Hate Speech. Journal of Experimental & Theoretical Artificial Intelligence. Volume 30, Issue 2, pp. 187-202. Taylor & Francis.
Waseem, Z., Davidson, T., Warmsley, D. and Weber, I. (2017) Understanding Abuse: A Typology of Abusive Language Detection Subtasks. Proceedings of the Abusive Language Online Workshop.
Classification systems in all tasks will be evaluated using the macro-averaged F1-score.
Submission format information is available from the 'Participate' tab above.
Some participants ask us to provide links to other datasets that could be used together with OffensEval's training set.
OffensEval's annotation is different than previously released offensive language, agression, and hate speech datasets. Nevertheless, some datasets may be used to provide more training material in sub-task A (offensive vs. not offensive). We recommend the TRAC shared task dataset on aggression identification.
The links to the training set are listed below and more information can be found in the shared task report.
Instructions for downloading the training and test data are available on the competition's "Overview" page.
Please read these instructions carefully.
You are required to submit a zip archive containing two files: (1) your predictions; and (2) a brief system description. These two files are described below. You can download a sample submission zip file from here.
The prediction format is a comma-delimited text file with a *.csv extension which should contain two columns: (1) the sample ID, and (2) the sample label. You can name the file anything you like, so long as it has a .csv extension. The order of the entries is not important, but there must be one prediction for each ID. The CSV file should not have a header row.
To help us report and summarize the results we ask that each submission include a description of the system that was used to generate it.
This should be a plain text file called "description.txt" and it should contain a minimum of 50 words.
Include information about the model, features, and data used in the system. Please try to include a sufficiently detailed description of your system. These files will not be made public.
NOTE: If you use any external training data in addition to what was provided, you must mention it in the description.
You can make submissions from the 'Submit / View Results' tab to the left.
Remember: The online system requires a zip file with your entry in it. If you do not follow the above instructions your submission will fail and you will receive an error message.
NOTE: You must submit a zip file containing just the two files described above. It should not contain folders or any other files.
The submission files should be in the root of the zip archive, not inside any sub-directories. Zip files containing any directories will be rejected!
We have included a Practice track where you can test the system by submitting predictions on the training set of each task. You can also submit this sample submission to see how it works.
You can make up to 3 submissions for each task. Submissions that are rejected by the system due to errors are not counted towards this limit.
Submission order is NOT important. You will be ranked by your best performing submission.
Results and rankings will be available after the competition ends. We will email everyone once the results are ready.
Start: Jan. 15, 2019, midnight
Description: Submit up to 3 predictions on the test set for Task A.
Start: Jan. 18, 2019, midnight
Description: Submit up to 3 predictions on the test set for Task B.
Start: Jan. 25, 2019, midnight
Description: Submit up to 3 predictions on the test set for Task C.
Feb. 1, 2019, midnight
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