ROFF - A Romanian Twitter Dataset for Offensive Language
This paper describes the annotation process of an offensive language data set for Romanian on social media. To facilitate comparable multi-lingual research on offensive language, the annotation guidelines follow some of the recent annotation efforts for other languages. The final corpus contains 5000 micro-blogging posts annotated by a large number of volunteer annotators. The inter-annotator agreement and the initial automatic discrimination results we present are in line with earlier annotation efforts.
Code (0)
등록된 구현이 없습니다.
Similar Papers 제목 키워드 기반
RED: A Novel Dataset for Romanian Emotion Detection from Tweets
In Romanian language there are some resources for automatic text comprehension, but for Emotion Detection, not lexicon-based, there are none. To cover this gap, we extracted data from Twitter and created the first datase…
BIG-bench Machine LearningClassificationOpinion MiningReading Comprehension+2Arabic Offensive Language on Twitter: Analysis and Experiments
Detecting offensive language on Twitter has many applications ranging from detecting/predicting bullying to measuring polarization. In this paper, we focus on building a large Arabic offensive tweet dataset. We introduce…
CoRoSeOf - An Annotated Corpus of Romanian Sexist and Offensive Tweets
This paper introduces CoRoSeOf, a large corpus of Romanian social media manually annotated for sexist and offensive language. We describe the annotation process of the corpus, provide initial analyses, and baseline class…
Binary ClassificationClassificationEmojis as Anchors to Detect Arabic Offensive Language and Hate Speech
We introduce a generic, language-independent method to collect a large percentage of offensive and hate tweets regardless of their topics or genres. We harness the extralinguistic information embedded in the emojis to co…
Cultural Vocal Bursts Intensity PredictionDetecting Hate Speech and Offensive Language on Twitter using Machine Learning: An N-gram and TFIDF based Approach
Toxic online content has become a major issue in today's world due to an exponential increase in the use of internet by people of different cultures and educational background. Differentiating hate speech and offensive l…
Hate Speech Detection