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Offensive Language Identification Dataset (OLID) |
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v 1.0: March 15 2018 |
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https://scholar.harvard.edu/malmasi/olid |
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1) DESCRIPTION |
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This is the README file for OLID described in: https://arxiv.org/abs/1902.09666 |
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OLID contains 14,100 annotate tweets. It has been used as the official dataset for OffensEval: Identifying and Categorizing Offensive Language in Social Media (SemEval 2019 - Task 6): https://competitions.codalab.org/competitions/20011 |
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The files included are: |
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- olid-training-v1.tsv contains 13,240 annotated tweets. |
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- olid-annotation.txt contains a short summary of the annotation guidelines. |
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- testset-levela.tsv contains the test set instances of level a. |
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- testset-levelb.tsv contains the test set instances of level b. |
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- testset-levelc.tsv contains the test set instances of level c. |
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- labels-levela.csv contains the gold labels and IDs of the instances in test set layer a. |
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- labels-levelb.csv contains the gold labels and IDs of the instances test set layer b. |
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- labels-levelc.csv contains the gold labels and IDs of the instances test set layer c. |
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The dataset was annotated using crowdsourcing. The gold labels were assigned taking the agreement of three annotators into consideration. No correction has been carried out on the crowdsourcing annotations. |
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Twitter user mentions were substituted by @USER and URLs have been substitute by URL. |
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OLID is annotated using a hierarchical annotation. Each instance contains up to 3 labels each corresponding to one of the following levels: |
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- Level (or sub-task) A: Offensive language identification; |
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- Level (or sub-task) B: Automatic categorization of offense types; |
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- Level (or sub-task) C: Offense target identification. |
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2) FORMAT |
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Instances are included in TSV format as follows: |
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ID INSTANCE SUBA SUBB SUBC |
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Whenever a label is not given, a value NULL is inserted (e.g. INSTANCE NOT NULL NULL) |
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The column names in the file are the following: |
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id tweet subtask_a subtask_b subtask_c |
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The labels used in the annotation are listed below. |
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3) TASKS AND LABELS |
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(A) Level A: Offensive language identification |
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- (NOT) Not Offensive - This post does not contain offense or profanity. |
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- (OFF) Offensive - This post contains offensive language or a targeted (veiled or direct) offense |
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In our annotation, we label a post as offensive (OFF) if it contains any form of non-acceptable language (profanity) or a targeted offense, which can be veiled or direct. |
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(B) Level B: Automatic categorization of offense types |
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- (TIN) Targeted Insult and Threats - A post containing an insult or threat to an individual, a group, or others (see categories in sub-task C). |
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- (UNT) Untargeted - A post containing non-targeted profanity and swearing. |
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Posts containing general profanity are not targeted, but they contain non-acceptable language. |
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(C) Level C: Offense target identification |
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- (IND) Individual - The target of the offensive post is an individual: a famous person, a named individual or an unnamed person interacting in the conversation. |
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- (GRP) Group - The target of the offensive post is a group of people considered as a unity due to the same ethnicity, gender or sexual orientation, political affiliation, religious belief, or something else. |
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- (OTH) Other – The target of the offensive post does not belong to any of the previous two categories (e.g., an organization, a situation, an event, or an issue) |
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Label Combinations |
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Here are the possible label combinations in the OLID annotation. |
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- NOT NULL NULL |
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- OFF UNT NULL |
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- OFF TIN (IND|GRP|OTH) |
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4) CITING THE DATASET |
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If you use this dataset we kindly ask you to include a reference to the paper below. |
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@inproceedings{zampierietal2019, |
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title={{Predicting the Type and Target of Offensive Posts in Social Media}}, |
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author={Zampieri, Marcos and Malmasi, Shervin and Nakov, Preslav and Rosenthal, Sara and Farra, Noura and Kumar, Ritesh}, |
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booktitle={Proceedings of NAACL}, |
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year={2019}, |
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} |
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The paper will be presented at NAACL and the pre-print is available on arXiv.org: https://arxiv.org/abs/1902.09666 |
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If you would like to refer to the OffensEval competition, here is the bib entry to the report: |
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@inproceedings{offenseval, |
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title={{SemEval-2019 Task 6: Identifying and Categorizing Offensive Language in Social Media (OffensEval)}}, |
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author={Zampieri, Marcos and Malmasi, Shervin and Nakov, Preslav and Rosenthal, Sara and Farra, Noura and Kumar, Ritesh}, |
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booktitle={Proceedings of The 13th International Workshop on Semantic Evaluation (SemEval)}, |
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year={2019} |
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} |
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