Question: In what way is the offensive dataset not biased by topic, dialect or target?

Evidence:
raw_evidence: Disclaimer: Due to the nature of the paper, some examples contain highly offensive language and hate speech. They don't reflect the views of the authors in any way, and the point of the paper is to help fight such speech. Much recent interest has focused on the detection of offensive language and hate speech in online social media. Such language is often associated with undesirable online behaviors such as trolling, cyberbullying, online extremism, political polarization, and propaganda. Thus, offensive language detection is instrumental for a variety of application such as: quantifying polarization BIBREF0, BIBREF1, trolls and propaganda account detection BIBREF2, detecting the likelihood of hate crimes BIBREF3; and predicting conflict BIBREF4. In this paper, we describe our methodology for building a large dataset of Arabic offensive tweets. Given that roughly 1-2% of all Arabic tweets are offensive BIBREF5, targeted annotation is essential for efficiently building a large dataset. Since our methodology does not use a seed list of offensive words, it is not biased by topic, target, or dialect. Using our methodology, we tagged 10,000 Arabic tweet dataset for offensiveness, where offensive tweets account for roughly 19% of the tweets. Further, we labeled tweets as vulgar or hate speech. To date, this is the largest available dataset, which we plan to make publicly available along with annotation guidelines. We use this dataset to characterize Arabic offensive language to ascertain the topics, dialects, and users' gender that are most associated with the use of offensive language. Though we suspect that there are common features that span different languages and cultures, some characteristics of Arabic offensive language is language and culture specific. Thus, we conduct a thorough analysis of how Arabic users use offensive language. Next, we use the dataset to train strong Arabic offensive language classifiers using state-of-the-art representations and classification techniques. Specifically, we experiment with static and contextualized embeddings for representation along with a variety of classifiers such as a deep neural network classifier and Support Vector Machine (SVM).
raw_evidence: Disclaimer: Due to the nature of the paper, some examples contain highly offensive language and hate speech. They don't reflect the views of the authors in any way, and the point of the paper is to help fight such speech. Much recent interest has focused on the detection of offensive language and hate speech in online social media. Such language is often associated with undesirable online behaviors such as trolling, cyberbullying, online extremism, political polarization, and propaganda. Thus, offensive language detection is instrumental for a variety of application such as: quantifying polarization BIBREF0, BIBREF1, trolls and propaganda account detection BIBREF2, detecting the likelihood of hate crimes BIBREF3; and predicting conflict BIBREF4. In this paper, we describe our methodology for building a large dataset of Arabic offensive tweets. Given that roughly 1-2% of all Arabic tweets are offensive BIBREF5, targeted annotation is essential for efficiently building a large dataset. Since our methodology does not use a seed list of offensive words, it is not biased by topic, target, or dialect. Using our methodology, we tagged 10,000 Arabic tweet dataset for offensiveness, where offensive tweets account for roughly 19% of the tweets. Further, we labeled tweets as vulgar or hate speech. To date, this is the largest available dataset, which we plan to make publicly available along with annotation guidelines. We use this dataset to characterize Arabic offensive language to ascertain the topics, dialects, and users' gender that are most associated with the use of offensive language. Though we suspect that there are common features that span different languages and cultures, some characteristics of Arabic offensive language is language and culture specific. Thus, we conduct a thorough analysis of how Arabic users use offensive language. Next, we use the dataset to train strong Arabic offensive language classifiers using state-of-the-art representations and classification techniques. Specifically, we experiment with static and contextualized embeddings for representation along with a variety of classifiers such as a deep neural network classifier and Support Vector Machine (SVM).

Answers:
It does not use a seed list to gather tweets so the dataset does not skew to specific topics, dialect, targets.
our methodology does not use a seed list of offensive words