Arabic Dialect Identification in the Wild

We present QADI, an automatically collected dataset of tweets belonging to a wide range of country-level Arabic dialects -covering 18 different countries in the Middle East and North Africa region. Our method for building this dataset relies on applying multiple filters to identify users who belong to different countries based on their account descriptions and to eliminate tweets that are either written in Modern Standard Arabic or contain inappropriate language. The resultant dataset contains 540k tweets from 2,525 users who are evenly distributed across 18 Arab countries. Using intrinsic evaluation, we show that the labels of a set of randomly selected tweets are 91.5% accurate. For extrinsic evaluation, we are able to build effective country-level dialect identification on tweets with a macro-averaged F1-score of 60.6% across 18 classes.

Fighting the COVID-19 Infodemic: Modeling the Perspective of Journalists, Fact-Checkers, Social Media Platforms, Policy Makers, and the Society

Disinformation, i.e., information that is both false and means harm, thrives in social media. Most often, it is used for political purposes, e.g., to influence elections or simply to cause distrust in society. It can also target medical issues, most notably the use of vaccines. With the emergence of the COVID-19 pandemic, the political and the medical aspects merged as disinformation got elevated to a whole new level to become the first global infodemic. Fighting this infodemic is now ranked second on the list of the most important focus areas of the World Health Organization, with dangers ranging from promoting fake cures, rumors, and conspiracy theories to spreading xenophobia and panic. The fight requires solving a number of problems such as identifying tweets containing claims, determining their check-worthiness and factuality, and their potential to do harm as well as the nature of that harm, to mention just a few. These are challenging problems, and some of them have been studied previously, but typically in isolation. Here, we design, annotate, and release to the research community a new dataset for fine-grained disinformation analysis that (i) focuses on COVID-19, (ii) combines the perspectives and the interests of journalists, fact-checkers, social media platforms, policy makers, and society as a whole, and (iii) covers both English and Arabic.

A Few Topical Tweets are Enough for Effective User-Level Stance Detection

Stance detection entails ascertaining the position of a user towards a target, such as an entity, topic, or claim. Recent work that employs unsupervised classification has shown that performing stance detection on vocal Twitter users, who have many tweets on a target, can yield very high accuracy (+98%). However, such methods perform poorly or fail completely for less vocal users, who may have authored only a few tweets about a target. In this paper, we tackle stance detection for such users using two approaches. In the first approach, we improve user-level stance detection by representing tweets using contextualized embeddings, which capture latent meanings of words in context. We show that this approach outperforms two strong baselines and achieves 89.6% accuracy and 91.3% macro F-measure on eight controversial topics. In the second approach, we expand the tweets of a given user using their Twitter timeline tweets, and then we perform unsupervised classification of the user, which entails clustering a user with other users in the training set. This approach achieves 95.6% accuracy and 93.1% macro F-measure.

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.

Arabic Diacritic Recovery Using a Feature-Rich biLSTM Model

Diacritics (short vowels) are typically omitted when writing Arabic text, and readers have to reintroduce them to correctly pronounce words. There are two types of Arabic diacritics: the first are core-word diacritics (CW), which specify the lexical selection, and the second are case endings (CE), which typically appear at the end of the word stem and generally specify their syntactic roles. Recovering CEs is relatively harder than recovering core-word diacritics due to inter-word dependencies, which are often distant. In this paper, we use a feature-rich recurrent neural network model that uses a variety of linguistic and surface-level features to recover both core word diacritics and case endings. Our model surpasses all previous state-of-the-art systems with a CW error rate (CWER) of 2.86\% and a CE error rate (CEER) of 3.7% for Modern Standard Arabic (MSA) and CWER of 2.2% and CEER of 2.5% for Classical Arabic (CA). When combining diacritized word cores with case endings, the resultant word error rate is 6.0% and 4.3% for MSA and CA respectively. This highlights the effectiveness of feature engineering for such deep neural models.

Tanbih: Get To Know What You Are Reading

We introduce Tanbih, a news aggregator with intelligent analysis tools to help readers understanding what's behind a news story. Our system displays news grouped into events and generates media profiles that show the general factuality of reporting, the degree of propagandistic content, hyper-partisanship, leading political ideology, general frame of reporting, and stance with respect to various claims and topics of a news outlet. In addition, we automatically analyse each article to detect whether it is propagandistic and to determine its stance with respect to a number of controversial topics.

Diacritization of Maghrebi Arabic Sub-Dialects

Diacritization process attempt to restore the short vowels in Arabic written text; which typically are omitted. This process is essential for applications such as Text-to-Speech (TTS). While diacritization of Modern Standard Arabic (MSA) still holds the lion share, research on dialectal Arabic (DA) diacritization is very limited. In this paper, we present our contribution and results on the automatic diacritization of two sub-dialects of Maghrebi Arabic, namely Tunisian and Moroccan, using a character-level deep neural network architecture that stacks two bi-LSTM layers over a CRF output layer. The model achieves word error rate of 2.7% and 3.6% for Moroccan and Tunisian respectively and is capable of implicitly identifying the sub-dialect of the input.

Arabic Multi-Dialect Segmentation: bi-LSTM-CRF vs. SVM

Arabic word segmentation is essential for a variety of NLP applications such as machine translation and information retrieval. Segmentation entails breaking words into their constituent stems, affixes and clitics. In this paper, we compare two approaches for segmenting four major Arabic dialects using only several thousand training examples for each dialect. The two approaches involve posing the problem as a ranking problem, where an SVM ranker picks the best segmentation, and as a sequence labeling problem, where a bi-LSTM RNN coupled with CRF determines where best to segment words. We are able to achieve solid segmentation results for all dialects using rather limited training data. We also show that employing Modern Standard Arabic data for domain adaptation and assuming context independence improve overall results.

Arabizi Detection and Conversion to Arabic

Arabizi is Arabic text that is written using Latin characters. Arabizi is used to present both Modern Standard Arabic (MSA) or Arabic dialects. It is commonly used in informal settings such as social networking sites and is often with mixed with English. In this paper we address the problems of: identifying Arabizi in text and converting it to Arabic characters. We used word and sequence-level features to identify Arabizi that is mixed with English. We achieved an identification accuracy of 98.5%. As for conversion, we used transliteration mining with language modeling to generate equivalent Arabic text. We achieved 88.7% conversion accuracy, with roughly a third of errors being spelling and morphological variants of the forms in ground truth.