In this task, you will be presented with a context from an academic paper and you have to write an answerable question based on the context. Your questions can be extractive, abstractive, or yes-no questions.

[EX Q]: Next, we analyzed all tweets labeled as offensive to better understand how Arabic speakers use offensive language. Here is a breakdown of usage:

Direct name calling: The most frequent attack is to call a person an animal name, and the most used animals were كلب> (“klb” – “dog”), حمار> (“HmAr” – “donkey”), and بهيم> (“bhym” – “beast”). The second most common was insulting mental abilities using words such as غبي> (“gby” – “stupid”) and عبيط> (“EbyT” –“idiot”). Some culture-specific differences should be considered. Not all animal names are used as insults. For example, animals such as أسد> (“Asd” – “lion”), صقر> (“Sqr” – “falcon”), and غزال> (“gzAl” – “gazelle”) are typically used for praise. For other insults, people use: some bird names such as دجاجة> (“djAjp” – “chicken”), بومة> (“bwmp” – “owl”), and غراب> (“grAb” – “crow”); insects such as ذبابة> (“*bAbp” – “fly”), صرصور> (“SrSwr” – “cockroach”), and حشرة> (“H$rp” – “insect”); microorganisms such as جرثومة> (“jrvwmp” – “microbe”) and طحالب> (“THAlb” – “algae”); inanimate objects such as جزمة> (“jzmp” – “shoes”) and سطل> (“sTl” – “bucket”) among other usages.

Simile and metaphor: Users use simile and metaphor were they would compare a person to: an animal as in زي الثور> (“zy Alvwr” – “like a bull”), سمعني نهيقك> (“smEny nhyqk” – “let me hear your braying”), and هز ديلك> (“hz dylk” – “wag your tail”); a person with mental or physical disability such as منغولي> (“mngwly” – “Mongolian (down-syndrome)”), معوق> (“mEwq” – “disabled”), and قزم> (“qzm” – “dwarf”); and to the opposite gender such as جيش نوال> (“jy$ nwAl” – “Nawal's army (Nawal is female name)”) and نادي زيزي> (“nAdy zyzy” – “Zizi's club (Zizi is a female pet name)”).

Indirect speech: This type of offensive language includes: sarcasm such as أذكى إخواتك> (“A*kY AxwAtk” – “smartest one of your siblings”) and فيلسوف الحمير> (“fylswf AlHmyr” – “the donkeys' philosopher”); questions such as ايه كل الغباء ده> (“Ayh kl AlgbA dh” – “what is all this stupidity”); and indirect speech such as النقاش مع البهايم غير مثمر> (“AlnqA$ mE AlbhAym gyr mvmr” – “no use talking to cattle”).

Wishing Evil: This entails wishing death or major harm to befall someone such as ربنا ياخدك> (“rbnA yAxdk” – “May God take (kill) you”), الله يلعنك> (“Allh ylEnk” – “may Allah/God curse you”), and روح في داهية> (“rwH fy dAhyp” – equivalent to “go to hell”).

Name alteration: One common way to insult others is to change a letter or two in their names to produce new offensive words that rhyme with the original names. Some examples of such include changing الجزيرة> (“Aljzyrp” – “Aljazeera (channel)”) to الخنزيرة> (“Alxnzyrp” – “the pig”) and خلفان> (“xlfAn” – “Khalfan (person name)”) to خرفان> (“xrfAn” – “crazed”).

Societal stratification: Some insults are associated with: certain jobs such as بواب> (“bwAb” – “doorman”) or خادم> (“xAdm” – “servant”); and specific societal components such بدوي> (“bdwy” – “bedouin”) and فلاح> (“flAH” – “farmer”).

Immoral behavior: These insults are associated with negative moral traits or behaviors such as حقير> (“Hqyr” – “vile”), خاين> (“xAyn” – “traitor”), and منافق> (“mnAfq” – “hypocrite”).

Sexually related: They include expressions such as خول> (“xwl” – “gay”), وسخة> (“wsxp” – “prostitute”), and عرص> (“ErS” – “pimp”).
[EX A]: What are the distinctive characteristics of how Arabic speakers use offensive language?

[EX Q]: Our analysis found that gang members have a penchant for using just a small set of emoji symbols that convey their anger and violent behavior through their tweets. Figure FIGREF24 illustrates the emoji distribution for the top 20 most frequent emojis used by gang member profiles in our dataset. The fuel pump emoji was the most frequently used emoji by the gang members, which is often used in the context of selling or consuming marijuana. The pistol emoji is the second most frequent in our dataset, which is often used with the guardsman emoji or the police cop emoji in an `emoji chain'. Figure FIGREF28 presents some prototypical `chaining' of emojis used by gang members. The chains may reflect their anger at law enforcement officers, as a cop emoji is often followed up with the emoji of a weapon, bomb, or explosion. We found that 32.25% of gang members in our dataset have chained together the police and the pistol emoji, compared to just 1.14% of non-gang members. Moreover, only 1.71% of non-gang members have used the hundred points emoji and pistol emoji together in tweets while 53% of gang members have used them. A variety of the angry face emoji such as devil face emoji and imp emoji were also common in gang member tweets.
[EX A]: What are the differences in the use of emojis between gang member and the rest of the Twitter population?

[EX Q]: In order to obtain insights about why the more fine-grained bi-sense emoji embedding helps in understanding the complexed sentiments behind tweets, we visualize the attention weights for ATT-E-LSTM and MATT-BiE-LSTM for comparison. Therefore, we construct the new input INLINEFORM0 to each LSTM unit by concatenating the original word embedding and the attention vector in Equation EQREF21 to distribute the senti-emoji information to each step. This model is called Multi-level Attention-based LSTM with Bi-sense Emoji Embedding (MATT-BiE-LSTM) Attention-based LSTM with emojis:We also use the word-emoji embedding to calculate the emoji-word attention following Equation EQREF20 and EQREF21 , and the only difference is that we replace the attention-derived senti-emoji embedding with the pre-trained word-emoji embedding by fasttext, denoted as ATT-E-LSTM. In Figure FIGREF27 (a), the ATT-E-LSTM model (baseline) assigns relatively more weights on the word “no” and “pressure”, while MATT-BiE-LSTM attends mostly on the word “happy” and “lovely”. The different attention distributions suggest that the proposed senti-emoji embedding is capable of recognizing words with strong sentiments that are closely related to the true sentiment even with the presence of words with conflicting sentiments, such as “pressure” and “happy”. while ATT-E-LSTM tends to pick up all sentimental words which could raise confusions. The senti-emoji embedding is capable of extracting representations of complexed semantics and sentiments which help guide the attentions even in cases when the word sentiment and emoji sentiment are somewhat contradictory to each other. From Figure FIGREF27 (b) and (c) we can observe that the ATT-E-LSTM assigns more weights on the sentiment-irrelevant words than the MATT-BiE-LSTM such as “hoodies”, “wait” and “after”, indicating that the proposed model is more robust to irrelevant words and concentrates better on important words. Because of the senti-emoji embedding obtained through bi-sense emoji embedding and the sentence-level LSTM encoding on the text input (described in Section SECREF13 ), we are able to construct a more robust embedding based on the semantic and sentiment information from the whole context compared to the word-emoji embedding used in ATT-E-LSTM which takes only word-level information into account.
[EX A]:
What evidence does visualizing the attention give to show that it helps to obtain a more robust understanding of semantics and sentiments?