Slovenian Twitter hate speech dataset IMSyPP-sl

  Authors

Kralj Novak, Petra ; Mozetič, Igor and Ljubešić, Nikola

  Item identifier

http://hdl.handle.net/11356/1398

 Project URL

http://imsypp.ijs.si/

 Date issued

2021-02-17

 Type

corpus, text

 Size

120000 items

 Language(s)

Slovenian

 Description

A hand-labeled training (50,000 tweets labeled twice) and evaluation set (10,000 tweets labeled twice) for hate speech on Slovenian Twitter. The data files contain tweet IDs, hate speech type, hate speech target, and annotator ID. For obtaining the full text of the dataset, please contact the first author. Hate speech type: 1. Appropriate - has no target 2. Inappropriate (contains terms that are obscene, vulgar; but the text is not directed at any person specifically) - has no target 3. Offensive (including offensive generalization, contempt, dehumanization, indirect offensive remarks) 4. Violent (author threatens, indulges, desires, or calls for physical violence against a target; it also includes calling for, denying, or glorifying war crimes and crimes against humanity) Hate speech target: 1. Racism (intolerance based on nationality, ethnicity, language, towards foreigners; and based on race, skin color) 2. Migrants (intolerance of refugees or migrants, offensive generalization, call for their exclusion, restriction of rights, non-acceptance, denial of assistance…) 3. Islamophobia (intolerance towards Muslims) 4. Antisemitism (intolerance of Jews; also includes conspiracy theories, Holocaust denial or glorification, offensive stereotypes…) 5. Religion (other than above) 6. Homophobia (intolerance based on sexual orientation and / or identity, calls for restrictions on the rights of LGBTQ persons 7. Sexism (offensive gender-based generalization, misogynistic insults, unjustified gender discrimination) 8. Ideology (intolerance based on political affiliation, political belief, ideology… e.g. “communists”, “leftists”, “home defenders”, “socialists”, “activists for…”) 9. Media (journalists and media, also includes allegations of unprofessional reporting, false news, bias) 10. Politics (intolerance towards individual politicians, authorities, system, political parties) 11. Individual (intolerance toward any other individual due to individual characteristics; like commentator, neighbor, acquaintance ) 12. Other (intolerance towards members of other groups due to belonging to this group; write in the blank column on the right which group it is) Training dataset The training set is sampled from data collected between December 2017 and February 2020. The sampling was intentionally biased to contain as much hate speech as possible. A simple model was used to flag potential hate speech content and additionally, filtering by users and by tweet length (number of characters) was applied. 50,000 tweets were selected for annotation. Evaluation dataset The evaluation set is sampled from data collected between February 2020 and August 2020. Contrary to the training set, the evaluation set is an unbiased random sample. Since the evaluation set is from a later period compared to the training set, the possibility of data linkage is minimized. Furthermore, the estimates of model performance made on the evaluation set are realistic, or even pessimistic, since the evaluation set is characterized by a new topic: Covid-19. 10,000 tweets were selected for the evaluation set. Annotation results Each tweet was annotated twice: In 90% of the cases by two different annotators and in 10% of the cases by the same annotator. Special attention was devoted to evening out the overlap between annotators to get agreement estimates on equally sized sets. Ten annotators were engaged for our annotation campaign. They were given annotation guidelines, a training session, and a test on a small set to evaluate their understanding of the task and their commitment before starting the annotation procedure. Annotator agreement in terms of Krippendorff Alpha is around 0.6. Annotation agreement scores are detailed in the accompanying report files for each dataset separately. The annotation process lasted four months, and it required about 1,200 person-hours for the ten annotators to complete the task.

 Publisher

Jožef Stefan Institute

 Acknowledgement

European Union’s Rights,Equality and Citizenship Programme 875263 "IMSyPP - Innovative Monitoring Systems and PreventionPolicies of Online Hate Speech"

ARRS (Slovenian Research Agency) P2-103 "Knowledge Technologies"

ARRS (Slovenian Research Agency) P6-0411 "Language Resources and Technologies for Slovene"

 Subject(s)

hate speech Twitter offensive language inappropriate language violent language manual annotation

 Collection(s)

CLARIN.SI data & tools

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