Predicting the Type and Target of Offensive Posts in Social Media

Abstract

As offensive content has become pervasive in social media, there has been much research in identifying potentially offensive messages. However, previous work on this topic did not consider the problem as a whole, but rather focused on detecting very specific types of offensive content, e.g., hate speech, cyberbulling, or cyber-aggression. In contrast, here we target several different kinds of offensive content.In particular, we model the task hierarchically, identifying the type and the target of offensive messages in social media. For this purpose, we complied the Offensive Language Identification Dataset (OLID), a new dataset with tweets annotated for offensive content using a fine-grained three-layer annotation scheme, which we make publicly available. We discuss the main similarities and differences between OLID and pre-existing datasets for hate speech identification, aggression detection, and similar tasks. We further experiment with and we compare the performance of different machine learning models on OLID.

UM-IU@LING at SemEval-2019 Task 6: Identifying Offensive Tweets Using BERT and SVMs

“…The data collection method used to compile the dataset in OffensEval is described by Zampieri et al (2019a). We used the official training data and trial data provided by the shared task to train the classifier.…”

SemEval-2019 Task 6: Identifying and Categorizing Offensive Language in Social Media (OffensEval)

“…Below, we briefly describe OLID, the dataset used for our SemEval-2019 task 6. A detailed description of the data collection process and annotation is presented in Zampieri et al (2019). OLID is a large collection of English tweets annotated using a hierarchical three-layer annotation model.…”

“…Section 3 presents the shared task description and the subtasks included in OffensEval. Section 4 includes a brief description of OLID based on (Zampieri et al, 2019). Section 5 discusses the participating systems and their results in the shared task.…”

UTFPR at SemEval-2019 Task 5: Hate Speech Identification with Recurrent Neural Networks

“…In HatEval, posts are labeled as as to whether they contain hate speech or not and in OffensEval, posts are labeled as being offensive or not. As OffensEval considers multiple types of offensive contents, the hierarchical annotation model used to annotate OLID (Zampieri et al, 2019a), the dataset used in Offen-sEval, includes an annotation level distinguishing between the type of offensive content that posts include with two classes: insults and threats, and general profanity. This type annotation is used in OffensEval's sub-task B. ten in both English and Spanish.…”