You will be shown sections from a scientific research paper, together with a question about the paper. Paragraphs in the paper are separated by newlines. Your task is to answer the question based on the contents of the paper.

Paper:
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Arabic 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 effective Arabic offensive tweet detection. We introduce a method for building an offensive dataset that is not biased by topic, dialect, or target. We produce the largest Arabic dataset to date with special tags for vulgarity and hate speech. Next, we analyze the dataset to determine which topics, dialects, and gender are most associated with offensive tweets and how Arabic speakers use offensive language. Lastly, we conduct a large battery of experiments to produce strong results (F1 = 79.7) on the dataset using Support Vector Machine techniques.

Introduction
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).
The contributions of this paper are as follows:
We built the largest Arabic offensive language dataset to date that includes special tags for vulgar language and hate speech. We describe the methodology for building it along with annotation guidelines.
We performed thorough analysis of the dataset and described the peculiarities of Arabic offensive language.
We experimented with Support Vector Machine classifiers on character and word ngrams classification techniques to provide strong results on Arabic offensive language classification.

Experiments ::: Representations ::: Static Embeddings
We experimented with various static embeddings that were pre-trained on different corpora with different vector dimensionality. We compared pre-trained embeddings to embeddings that were trained on our dataset. For pre-trained embeddings, we used: fastText Egyptian Arabic pre-trained embeddings BIBREF27 with vector dimensionality of 300; AraVec skip-gram embeddings BIBREF28, trained on 66.9M Arabic tweets with 100-dimensional vectors; and Mazajak skip-gram embeddings BIBREF29, trained on 250M Arabic tweets with 300-dimensional vectors.
Sentence embeddings were calculated by taking the mean of the embeddings of their tokens. The importance of testing a character level n-gram model like fastText lies in the agglutinative nature of the Arabic language. We trained a new fastText text classification model BIBREF30 on our dataset with vectors of 40 dimensions, 0.5 learning rate, 2$-$10 character n-grams as features, for 30 epochs. These hyper-parameters were tuned using a 5-fold cross-validated grid-search.

Experiments ::: Representations ::: Deep Contextualized Embeddings
We also experimented with pre-trained contextualized embeddings with fine-tuning for down-stream tasks. Recently, deep contextualized language models such as BERT (Bidirectional Encoder Representations from Transformers) BIBREF31, UMLFIT BIBREF32, and OpenAI GPT BIBREF33, to name but a few, have achieved ground-breaking results in many NLP classification and language understanding tasks. In this paper, we used BERTbase-multilingual (hereafter as simply BERT) fine-tuning method to classify Arabic offensive language on Twitter as it eliminates the need of heavily engineered task-specific architectures. Although Robustly Optimized BERT (RoBERTa) embeddings perform better than (BERTlarge) on GLUE BIBREF34, RACE BIBREF35, and SQuAD BIBREF36 tasks, pre-trained multilingual RoBERTa models are not available. BERT is pre-trained on Wikipedia text from 104 languages and comes with hundreds of millions of parameters. It contains an encoder with 12 Transformer blocks, hidden size of 768, and 12 self-attention heads. Though the training data for the BERT embeddings don't match our genre, these embedding use BP sub-word segments. Following devlin-2019-bert, the classification consists of introducing a dense layer over the final hidden state $h$ corresponding to first token of the sequence, [CLS], adding a softmax activation on the top of BERT to predict the probability of the $l$ label:
where $W$ is the task-specific weight matrix. During fine-tuning, all BERT parameters together with $W$ are optimized end-to-end to maximize the log-probability of the correct labels.

Experiments ::: Error Analysis ::: False Positives
had four main types:
[leftmargin=*]
Gloating: ex. يا هبيده> (“yA hbydp” - “O you delusional”) referring to fans of rival sports team for thinking they could win.
Quoting: ex. لما حد يسب ويقول يا كلب> (“lmA Hd ysb wyqwl yA klb” – “when someone swears and says: O dog”).
Idioms: ex. يا فاطر رمضان يا خاسر دينك> (“yA fATr rmDAn yA xAsr dynk” – “o you who does not fast Ramadan, you who have lost your religion”), which is a colloquial idiom.
Implicit Sarcasm: ex. يا خاين انت عايز تشكك>
في حب الشعب للريس> (“yA KAyn Ant EAwz t$kk fy Hb Al$Eb llrys” – “O traitor, (you) want to question the love of the people to the president ”) where the author is mocking the president's popularity.

Conclusion and Future Work
In this paper we presented a systematic method for building an Arabic offensive language tweet dataset that does not favor specific dialects, topics, or genres. We developed detailed guidelines for tagging the tweets as clean or offensive, including special tags for vulgar tweets and hate speech. We tagged 10,000 tweets, which we plan to release publicly and would constitute the largest available Arabic offensive language dataset. We characterized the offensive tweets in the dataset to determine the topics that illicit such language, the dialects that are most often used, the common modes of offensiveness, and the gender distribution of their authors. We performed this breakdown for offensive tweets in general and for vulgar and hate speech tweets separately. We believe that this is the first detailed analysis of its kind. Lastly, we conducted a large battery of experiments on the dataset, using cross-validation, to establish a strong system for Arabic offensive language detection. We showed that using static embeddings produced a competitive results on the dataset.
For future work, we plan to pursue several directions. First, we want explore target specific offensive language, where attacks against an entity or a group may employ certain expressions that are only offensive within the context of that target and completely innocuous otherwise. Second, we plan to examine the effectiveness of cross dialectal and cross lingual learning of offensive language.

Figure 1: Topic distribution for offensive language and its sub-categories

Figure 4: Distribution of Hate Speech Types. Note: A tweet may have more than one type.

Figure 5: Tag cloud for words with top valence score among offensive class
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Question: In what way is the offensive dataset not biased by topic, dialect or target?

To answer the question, format your response as a `json` object with two fields:

"answer": A string providing a succinct answer to the question, in your own words.
"evidence": An array of strings. Each entry should be a full paragraph from the paper. Together, the evidence should serve as a justification for the answer.

For instance, for the question "What baselines did the authors compare against?", a sample response might be:

{
  "answer": "BERT and RoBERTa."
  "evidence": ["We compare our approach against two baselines. In Table 1, we compare against BERT. In Table 2, we compare against RoBERTa. Our findings indicate that our approach improves over both baeslines..."]
}

The "answer" field should be roughly 111 characters in length.

Do not include any text in your response other than the json. If the question is unanswerable given the provided excerpts, respond with the single word "null".

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

Answer JSON object:
{"answer": "It does not use a seed list to gather tweets so the dataset does not skew to specific topics, dialect, targets.", "evidence": ["Disclaimer: Due to the nature of the paper, some examples contain highly offensive language and hate speech. They don\u0027t 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\u0027 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)."]}