---
title: "Qatar — research institutions, scientists and papers"
description: "Qatar: 5 institutions with papers in our library in 1 agglomerations, 43 people with a page, 79 works, 14,524 citations."
canonical: "https://research.elamur.ai/free/en/countries/qatar/"
---
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# Qatar

Country 5 institutions in 1 agglomeration · [all countries](https://md.research.elamur.ai/free/en/countries/)

[**5** institutions with a page](https://md.research.elamur.ai/free/en/countries/qatar/#institutions) · [**1** agglomeration](https://md.research.elamur.ai/free/en/countries/qatar/#agglomerations) · [**43** people with a page](https://md.research.elamur.ai/free/en/countries/qatar/#authors) · [**79** works in our library](https://md.research.elamur.ai/free/en/countries/qatar/#works) · [**14,524** citations of their works](https://md.research.elamur.ai/free/en/countries/qatar/#works)

## About

### Profile

Qatar appears on this site as a compact research country. All of its institutions with a presence here are in one city, [Doha](https://md.research.elamur.ai/free/en/agglomerations/doha/). Five bodies have pages: four universities and one nonprofit. They are [Hamad bin Khalifa University](https://md.research.elamur.ai/free/en/universities/hamad-bin-khalifa-university/), [Qatar Foundation](https://md.research.elamur.ai/free/en/ngos/qatar-foundation/), [Carnegie Mellon University Qatar](https://md.research.elamur.ai/free/en/universities/carnegie-mellon-university-qatar/), [Qatar University](https://md.research.elamur.ai/free/en/universities/qatar-university/) and [Northwestern University in Qatar](https://md.research.elamur.ai/free/en/universities/northwestern-university-in-qatar/). The country entry sits alongside [other countries](https://md.research.elamur.ai/free/en/countries/) and within the general [directory of institutions](https://md.research.elamur.ai/free/en/orgs/), which can be narrowed to research institutions in Qatar.

Among the works here, the country's research is almost entirely about language. Its strongest areas are computational work on Arabic and the study of social media text. That second line covers sentiment, emotion, offensive language, propaganda and misinformation. One university, Hamad bin Khalifa University, accounts for most of the cited output. The other four institutions add distinct strands. Carnegie Mellon University Qatar contributes Arabic dialect resources. Qatar Foundation contributes early discourse and tagging studies. Qatar University contributes applied Arabic language tools. Northwestern University in Qatar contributes recent safety benchmarks for large language models.

### History

The earliest work connected with Qatar on this site dates from 2012. It is a study of [ambiguous discourse connectives](https://md.research.elamur.ai/free/en/papers/zhou-2012-cross-lingual-identification-ambiguous-discourse/) in resource-poor languages, attributed to Qatar Foundation. It builds a cross-lingual pipeline that leans on English resources to handle languages lacking their own annotated material. That concern with languages that have few tools runs through much of what follows.

In 2014 several foundational Arabic resources appear. Carnegie Mellon University Qatar contributed a [multidialectal parallel corpus of Arabic](https://md.research.elamur.ai/free/en/papers/bouamor-2014-multidialectal-parallel-corpus-arabic/). It also produced the [guidelines for large-scale Arabic error annotation](https://md.research.elamur.ai/free/en/papers/zaghouani-2014-large-scale-arabic-error-annotation/) of the Qatar Arabic Language Bank project. Qatar Foundation produced a stem-template approach to Arabic part-of-speech and gender/number tagging. At Hamad bin Khalifa University, [the AMARA corpus](https://md.research.elamur.ai/free/en/papers/abdelali-2014-amara-corpus-building-parallel-language/) assembled parallel language material for education.

From 2015 to 2018 the output widened and took on international shared evaluations. Researchers in Doha co-organised several SemEval tasks on Twitter sentiment, community question answering and affect. They also took part in the DSL and VarDial campaigns on telling similar languages and dialects apart. Arabic tooling matured in the same years with the Farasa segmenter and a study of diacritization.

From 2019 onward the centre of gravity moved toward harmful content. The topics were offensive posts, propaganda in news, factuality of media sources, stance detection and harmful memes. The most recent years, up to 2026, bring large language models into view. That period includes an Arabic-centric generative platform, medical chatbots and legal outcome prediction. It also includes a set of safety benchmarks for Arabic dialects, Kazakh and Albanian.

### Research profile

The dominant topic is [Semitic languages](https://md.research.elamur.ai/free/en/topics/semitic-languages/). Most of the works sit on the [linguistics and languages](https://md.research.elamur.ai/free/en/shelves/linguistics/) shelf, and many also sit under [natural language processing](https://md.research.elamur.ai/free/en/shelves/nlp/).

Arabic processing forms the core. [Farasa](https://md.research.elamur.ai/free/en/papers/abdelali-2016-farasa-fast-furious-segmenter-arabic/) breaks Arabic words into their prefix, stem and suffix parts for fast segmentation. The study [Arabic Diacritization: Stats, Rules, and Hacks](https://md.research.elamur.ai/free/en/papers/darwish-2017-arabic-diacritization-stats-rules-hacks/) tackles restoring short vowels to unvowelled text.

Dialects get sustained attention at Carnegie Mellon University Qatar. Its [unified guidelines for dialect orthography](https://md.research.elamur.ai/free/en/papers/habash-2018-unified-guidelines-resources-arabic-dialect/) propose CODA*, a spelling convention that works across dialects. Its [hierarchical aggregation of dialectal data](https://md.research.elamur.ai/free/en/papers/baimukan-2022-hierarchical-aggregation-dialectal-data-arabic/) labels dialects by region, country and city. The [Arabic Parallel Gender Corpus 2.0](https://md.research.elamur.ai/free/en/papers/alhafni-2022-arabic-parallel-gender-corpus-2/) addresses systems that ignore who is speaking or being addressed. These works also touch on [morphology](https://md.research.elamur.ai/free/en/topics/morphology/) and [part-of-speech tagging](https://md.research.elamur.ai/free/en/topics/part-of-speech-tagging/).

[Sentiment and emotion](https://md.research.elamur.ai/free/en/topics/sentiment-emotion/) is the second large line. It includes successive SemEval tasks on Twitter sentiment and on affect intensity in tweets. Qatar University added [ArSentD-LEV](https://md.research.elamur.ai/free/en/papers/baly-2019-arsentd-lev-multi-topic-corpus/), a corpus that attaches sentiment to specific targets in Levantine Arabic posts rather than giving one label per text. Qatar Foundation contributed a 2020 study of Arabic emotion recognition from speech with deep neural networks.

[Bias, fairness and safety](https://md.research.elamur.ai/free/en/topics/bias-fairness-safety/), together with [evaluation and benchmarks](https://md.research.elamur.ai/free/en/topics/evaluation-benchmarks/), make up a third line. It runs from offensive language detection to recent benchmarks that test how language models respond to harmful prompts. [ArabicDialectSafety](https://md.research.elamur.ai/free/en/papers/zaghouani-2026-arabicdialectsafety-dialect-aware-benchmark-arabic/) defines binary and fine-grained harm tasks, with results reported per dialect. Companion resources extend the same idea to Kazakh and Albanian. Close to this line is a body of work on propaganda, stance and the reliability of news outlets.

[Machine translation](https://md.research.elamur.ai/free/en/topics/machine-translation/) is a smaller but well-cited strand, and it also sits on the [machine translation](https://md.research.elamur.ai/free/en/shelves/machine-translation/) shelf. One study probes [what neural translation models learn about morphology](https://md.research.elamur.ai/free/en/papers/belinkov-2017-what-do-neural-machine-translation/). Another applies a cognitive approach to judging how hard Arabic machine output is to post-edit. A third asks whether [discourse structure](https://md.research.elamur.ai/free/en/topics/discourse-analysis/) can improve automatic evaluation.

[Speech](https://md.research.elamur.ai/free/en/topics/speech/) and [language models](https://md.research.elamur.ai/free/en/topics/language-models/) are the newest areas. [Fanar](https://md.research.elamur.ai/free/en/papers/abbas-2025-fanar-arabic-centric-multimodal-generative/) presents an Arabic-centred generative platform that spans text and other modalities. The [Almieyar benchmark](https://md.research.elamur.ai/free/en/papers/ghahroodi-2026-almieyar-culturally-grounded-benchmark-multi/) offers a culturally grounded test of speech recognition across dialects, including Ahwazi Arabic. Work on [vision and language](https://md.research.elamur.ai/free/en/topics/multimodal/) covers harmful memes.

[Legal AI](https://md.research.elamur.ai/free/en/topics/legal-ai/), on the [law](https://md.research.elamur.ai/free/en/shelves/law/) shelf, enters through a Qatar University study. It asks [whether language models can predict judicial outcomes](https://md.research.elamur.ai/free/en/papers/kmainasi-2025-large-language-models-predict-outcome/) in a low-resource legal setting. A historical paper from the same university looks at how an Arabic dialect was taught to a non-native learner in the mid-fifteenth century, which reaches into language learning.

### Leading scientists

The first scientist shown for Qatar is [Preslav Nakov](https://md.research.elamur.ai/free/en/authors/preslav-nakov/), of Hamad bin Khalifa University. Nakov appears on many of the country's most cited works. These include the SemEval sentiment tasks of 2016 and 2017, the 2019 work on offensive posts and the OffensEval task, and the 2016 community question answering task. They also include the second VarDial campaign and a 2022 survey of stance detection for mis- and disinformation. Nakov's work spans evaluation campaigns, social media analysis and misinformation.

[Francisco Guzman](https://md.research.elamur.ai/free/en/authors/francisco-guzman/), also at Hamad bin Khalifa University, is an author of the AMARA corpus, which built parallel resources for the educational domain. That work links Guzman to machine translation and multilingual resources.

[Lluís Màrquez](https://md.research.elamur.ai/free/en/authors/lluis-marquez/) is at Hamad bin Khalifa University and is also associated with Qatar Foundation. Màrquez co-authored the 2016 community question answering task. That task worked with threads from forums such as Qatar Living, where helpful replies sit among much noise.

[Hamdy Mubarak](https://md.research.elamur.ai/free/en/authors/hamdy-mubarak/) works on Arabic text at Hamad bin Khalifa University. Mubarak's papers here include [abusive language detection on Arabic social media](https://md.research.elamur.ai/free/en/papers/mubarak-2017-abusive-language-detection-arabic-social/), which released lexicons, tweets and annotated user comments. Mubarak also co-wrote the diacritization study.

[Kareem Darwish](https://md.research.elamur.ai/free/en/authors/kareem-darwish/) appears across the core Arabic tooling. That includes Farasa, diacritization, abusive language detection, and the earlier stem-template tagging work credited to Qatar Foundation.

[Alessandro Moschitti](https://md.research.elamur.ai/free/en/authors/alessandro-moschitti/), at Hamad bin Khalifa University, is an author of the community question answering task. That task is the site's single work under [question answering](https://md.research.elamur.ai/free/en/topics/question-answering/).

[Stephan Vogel](https://md.research.elamur.ai/free/en/authors/stephan-vogel/) is another Hamad bin Khalifa University author with much-cited work here. The individual papers can be followed on Vogel's own entry.

[Houda Bouamor](https://md.research.elamur.ai/free/en/authors/houda-bouamor/), of Carnegie Mellon University Qatar, is first author of the multidialectal parallel corpus of Arabic. That work gives dialect translation and dialect identification a shared resource.

[Ahmed Abdelali](https://md.research.elamur.ai/free/en/authors/ahmed-abdelali/), at Hamad bin Khalifa University, led the AMARA corpus and Farasa and co-authored the diacritization study. Abdelali's work is about practical resources and tools for Arabic.

[Ingmar Weber](https://md.research.elamur.ai/free/en/authors/ingmar-weber/) has a small number of works here from Hamad bin Khalifa University that have drawn wide citation. They belong to the university's work on social media.

[Nadir Durrani](https://md.research.elamur.ai/free/en/authors/nadir-durrani/) co-authored Farasa and the study of what neural translation models learn about morphology. Those two works join Arabic segmentation to interpretability in translation.

[Mohammad Salameh](https://md.research.elamur.ai/free/en/authors/mohammad-salameh/), of Carnegie Mellon University Qatar, is an author of [SemEval-2018 Task 1: Affect in Tweets](https://md.research.elamur.ai/free/en/papers/mohammad-2018-semeval-2018-task-1-affect/). That task asked not only whether a tweet carries an emotion but also how intense it is.

Further names run through the institutional entries. At Carnegie Mellon University Qatar they include [Kemal Oflazer](https://md.research.elamur.ai/free/en/authors/kemal-oflazer/), co-author of the dialect corpus, and [Behrang Mohit](https://md.research.elamur.ai/free/en/authors/behrang-mohit/), on the error annotation project. [Bashar Alhafni](https://md.research.elamur.ai/free/en/authors/bashar-alhafni/) worked on the gender corpus.

[Wajdi Zaghouani](https://md.research.elamur.ai/free/en/authors/wajdi-zaghouani/) links Carnegie Mellon University Qatar with Northwestern University in Qatar. Zaghouani worked on error annotation in 2014. In 2026 Zaghouani worked on the safety benchmarks and on a paper about cognitive alignment in language models.

At Qatar University, [Tamer Elsayed](https://md.research.elamur.ai/free/en/authors/tamer-elsayed/) comes first. Colleagues there include [Reem Suwaileh](https://md.research.elamur.ai/free/en/authors/reem-suwaileh/), author of ThatiAR, and [Mohamed Bayan Kmainasi](https://md.research.elamur.ai/free/en/authors/mohamed-bayan-kmainasi/), author of the judicial prediction study. [Hassan Sajjad](https://md.research.elamur.ai/free/en/authors/hassan-sajjad/) and [Wei Gao](https://md.research.elamur.ai/free/en/authors/wei-gao/) appear with Qatar Foundation.

### Landmark works

The most cited work connected with Qatar is [SemEval-2017 Task 4](https://md.research.elamur.ai/free/en/papers/rosenthal-2017-semeval-2017-task-4-sentiment/). It was the fifth round of the SemEval evaluation of Twitter sentiment, and Nakov was among the organisers. Its predecessor, [SemEval-2016 Task 4](https://md.research.elamur.ai/free/en/papers/nakov-2016-semeval-2016-task-4-sentiment/), was the fourth round and drew many participating teams. It is credited to both Hamad bin Khalifa University and Qatar Foundation.

Two closely related 2019 works come next. [Predicting the Type and Target of Offensive Posts](https://md.research.elamur.ai/free/en/papers/zampieri-2019-predicting-type-target-offensive-posts/) introduced a three-level scheme. It asks whether a post is offensive, whether the offence is targeted, and at whom. The [OffensEval task](https://md.research.elamur.ai/free/en/papers/zampieri-2019-semeval-2019-task-6-identifying/) then turned that scheme into a shared evaluation of offensive content on social media.

The 2018 affect task from Carnegie Mellon University Qatar is also among the most cited. So is [Automated Hate Speech Detection and the Problem of Offensive Language](https://md.research.elamur.ai/free/en/papers/davidson-2017-automated-hate-speech-detection-problem/), held among Hamad bin Khalifa University's works. It addressed how systems confuse hate speech with language that is merely offensive. [Fine-grained Opinion Mining with Recurrent Neural Networks](https://md.research.elamur.ai/free/en/papers/liu-2015-fine-grained-opinion-mining-recurrent/) treated opinion extraction as token-level tagging. It tested whether recurrent networks could replace hand-built features.

Further down the citation order come works on propaganda and news. [Fine-Grained Analysis of Propaganda in News Articles](https://md.research.elamur.ai/free/en/papers/martino-2019-fine-grained-analysis-propaganda-news/) proposed locating the precise fragments where a technique appears and labelling them. The [SemEval-2020 task on propaganda techniques](https://md.research.elamur.ai/free/en/papers/martino-2020-semeval-2020-task-11-detection/) carried that idea into a shared evaluation. [Predicting Factuality of Reporting and Bias of News Media Sources](https://md.research.elamur.ai/free/en/papers/baly-2018-predicting-factuality-reporting-bias-news/) shifted the question from single claims to whole outlets.

Several other works complete the group. [SemEval-2016 Task 3](https://md.research.elamur.ai/free/en/papers/nakov-2016-semeval-2016-task-3-community/) set up community question answering on forum threads. The [VarDial 2017 findings](https://md.research.elamur.ai/free/en/papers/zampieri-2017-findings-vardial-evaluation-campaign/) gave shared resources and a common setup for closely related, low-resource varieties. [MOMENTA](https://md.research.elamur.ai/free/en/papers/pramanick-2021-momenta-multimodal-framework-detecting-harmful/) built a multimodal framework for harmful memes and their targets. [A Survey on Stance Detection](https://md.research.elamur.ai/free/en/papers/hardalov-2022-survey-stance-detection-mis-disinformation/) reviewed stance methods for misinformation. Among recent works, Fanar has drawn the most citation.

### Labs and units

Hamad bin Khalifa University holds most of the country's works in our library and most of their citations. Its people include Nakov, Mubarak, Darwish, Guzman, Weber, Abdelali, Màrquez and Durrani. Its works cover sentiment tasks, offensive language, Arabic segmentation and translation analysis.

Qatar Foundation, the one nonprofit, holds a smaller set of works with notable citation for its size. It is the home of the earliest strands here. These are the 2012 discourse connectives study, Arabic tagging in 2014 and the 2015 DSL task. They also include speech act modelling in 2016 and discourse-based translation evaluation in 2017. The speech act work places it in [speech act theory](https://md.research.elamur.ai/free/en/topics/speech-act-theory/).

Carnegie Mellon University Qatar centres on Arabic dialects and language resources. Its work runs from the 2014 corpora to dialect identification and gender-aware resources in 2022. Its people include [Ossama Obeid](https://md.research.elamur.ai/free/en/authors/ossama-obeid/) and [Emad Mohamed](https://md.research.elamur.ai/free/en/authors/emad-mohamed/).

Qatar University's works are recent and applied. They cover the stance of authorities towards rumours, a layered language model classifier and medical chatbots trained with synthetic material. They also cover legal prediction and dialect speech evaluation. Its people include [Khaled Shaban](https://md.research.elamur.ai/free/en/authors/khaled-shaban/), [Maram Hasanain](https://md.research.elamur.ai/free/en/authors/maram-hasanain/), [Hunzalah Hassan Bhatti](https://md.research.elamur.ai/free/en/authors/hunzalah-hassan-bhatti/), [Ali Hamdi](https://md.research.elamur.ai/free/en/authors/ali-hamdi/) and [Zien Sheikh Ali](https://md.research.elamur.ai/free/en/authors/zien-sheikh-ali/).

Northwestern University in Qatar holds the newest cluster. Its work covers propaganda and hate in Arabic memes, subjectivity in news sentences and the MarsadLab shared task entry. It also covers the ArGuard shared task and the multilingual safety benchmarks. [Mabrouka Bessghaier](https://md.research.elamur.ai/free/en/authors/mabrouka-bessghaier/), [Shimaa Amer Ibrahim](https://md.research.elamur.ai/free/en/authors/shimaa-amer-ibrahim/) and [Kholoud Khalil Aldous](https://md.research.elamur.ai/free/en/authors/kholoud-khalil-aldous/) appear there with Zaghouani.

### Collaborations

The strongest links on this site run between the Doha institutions themselves. Nakov, Màrquez, Darwish, Mubarak, Durrani and Guzman appear with both Hamad bin Khalifa University and Qatar Foundation. Zaghouani connects Carnegie Mellon University Qatar with Northwestern University in Qatar. ThatiAR, with Qatar University author Suwaileh, appears among Northwestern University in Qatar's works, which suggests ties between those two universities.

Beyond Qatar, the shared tasks bring in many outside co-authors. Marcos Zampieri and Shervin Malmasi recur on the offensive language and VarDial works. Sara Rosenthal and Alan Ritter appear on the sentiment tasks. Saif Mohammad led the affect task. Nizar Habash co-authored the dialect corpus, the error annotation work and the orthography work. Giovanni Da San Martino and Alberto Barrón-Cedeño appear on propaganda. Ramy Baly, Mitra Mohtarami and James Glass appear on stance and media factuality. Most of these partnerships came through international evaluation campaigns organised partly from Doha.

### Timeline

From 2012 to 2015 the work is early and resource-building. It starts with discourse connectives in 2012. In 2014 came the dialect corpus, the QALB error annotation, AMARA and stem-template tagging. The 2015 DSL task and recurrent-network opinion mining close the period.

From 2016 to 2018 Qatar's institutions co-organised widely used evaluations. These were sentiment in Twitter in 2016 and 2017, community question answering in 2016, and affect in tweets in 2018. Farasa, the diacritization study, abusive language detection and the morphology probe of translation models appeared in the same years. Work on stance and media factuality began in 2018.

From 2019 to 2022 harmful and misleading content dominates. Offensive posts and OffensEval came in 2019, and propaganda techniques in 2019 and 2020. Harmful memes followed in 2021 and the stance survey in 2022. Carnegie Mellon University Qatar added dialect and gender resources in 2022.

From 2023 to 2026 language models take over. Rumour stance came in 2023, and multimodal propaganda and subjectivity in 2024. Fanar, medical chatbots, legal prediction and AraGenEval followed in 2025. The 2026 safety benchmarks, the ArGuard task and the Almieyar speech benchmark close the current span on this site.

## Where

## Agglomerations (1)

1. [**Doha**5 institutions*14,524 citations*](https://md.research.elamur.ai/free/en/agglomerations/doha/)

## Institutions (5)

### 🎓 Universities (4)

1. **[Hamad bin Khalifa University](https://md.research.elamur.ai/free/en/universities/hamad-bin-khalifa-university/)**University · [Doha](https://md.research.elamur.ai/free/en/agglomerations/doha/), Qatar*58 works · 11,454 citations*
2. **[Carnegie Mellon University Qatar](https://md.research.elamur.ai/free/en/universities/carnegie-mellon-university-qatar/)**University · [Doha](https://md.research.elamur.ai/free/en/agglomerations/doha/), Qatar*8 works · 1,587 citations*
3. **[Northwestern University in Qatar](https://md.research.elamur.ai/free/en/universities/northwestern-university-in-qatar/)**University · [Doha](https://md.research.elamur.ai/free/en/agglomerations/doha/), Qatar*8 works · 30 citations*
4. **[Qatar University](https://md.research.elamur.ai/free/en/universities/qatar-university/)**University · [Doha](https://md.research.elamur.ai/free/en/agglomerations/doha/), Qatar*7 works · 78 citations*

### 🤝 Nonprofits (1)

1. **[Qatar Foundation](https://md.research.elamur.ai/free/en/ngos/qatar-foundation/)**Nonprofit · [Doha](https://md.research.elamur.ai/free/en/agglomerations/doha/), Qatar*7 works · 1,375 citations*

## People (43)

Authors with a page confirmed at its institutions, the most cited first.

1. [**Preslav Nakov**Hamad bin Khalifa University*32 papers here · 13,932 citations*](https://md.research.elamur.ai/free/en/authors/preslav-nakov/)
2. [**Francisco Guzman**Hamad bin Khalifa University*14 papers here · 6,891 citations*](https://md.research.elamur.ai/free/en/authors/francisco-guzman/)
3. [**Lluís Màrquez**Hamad bin Khalifa University*11 papers here · 5,880 citations*](https://md.research.elamur.ai/free/en/authors/lluis-marquez/)
4. [**Hamdy Mubarak**Hamad bin Khalifa University*25 papers here · 4,301 citations*](https://md.research.elamur.ai/free/en/authors/hamdy-mubarak/)
5. [**Kareem Darwish**Hamad bin Khalifa University*18 papers here · 3,599 citations*](https://md.research.elamur.ai/free/en/authors/kareem-darwish/)
6. [**Alessandro Moschitti**Hamad bin Khalifa University*9 papers here · 3,469 citations*](https://md.research.elamur.ai/free/en/authors/alessandro-moschitti/)
7. [**Stephan Vogel**Hamad bin Khalifa University*8 papers here · 3,083 citations*](https://md.research.elamur.ai/free/en/authors/stephan-vogel/)
8. [**Houda Bouamor**Carnegie Mellon University Qatar*11 papers here · 2,643 citations*](https://md.research.elamur.ai/free/en/authors/houda-bouamor/)
9. [**Ahmed Abdelali**Hamad bin Khalifa University*18 papers here · 2,352 citations*](https://md.research.elamur.ai/free/en/authors/ahmed-abdelali/)
10. [**Ingmar Weber**Hamad bin Khalifa University*3 papers here · 2,278 citations*](https://md.research.elamur.ai/free/en/authors/ingmar-weber/)
11. [**Nadir Durrani**Hamad bin Khalifa University*8 papers here · 2,221 citations*](https://md.research.elamur.ai/free/en/authors/nadir-durrani/)
12. [**Mohammad Salameh**Carnegie Mellon University Qatar*2 papers here · 2,192 citations*](https://md.research.elamur.ai/free/en/authors/mohammad-salameh/)
13. [**Wajdi Zaghouani**Hamad bin Khalifa University*21 papers here · 2,049 citations*](https://md.research.elamur.ai/free/en/authors/wajdi-zaghouani/)
14. [**Hassan Sajjad**Hamad bin Khalifa University*7 papers here · 1,781 citations*](https://md.research.elamur.ai/free/en/authors/hassan-sajjad/)
15. [**Wei Gao**Qatar Foundation*4 papers here · 1,710 citations*](https://md.research.elamur.ai/free/en/authors/wei-gao/)
16. [**Kemal Oflazer**Carnegie Mellon University Qatar*5 papers here · 1,665 citations*](https://md.research.elamur.ai/free/en/authors/kemal-oflazer/)
17. [**Ahmed Ali**Hamad bin Khalifa University*16 papers here · 1,596 citations*](https://md.research.elamur.ai/free/en/authors/ahmed-ali/)
18. [**Fahim Dalvi**Hamad bin Khalifa University*8 papers here · 1,506 citations*](https://md.research.elamur.ai/free/en/authors/fahim-dalvi/)
19. [**Ossama Obeid**Carnegie Mellon University Qatar*5 papers here · 1,348 citations*](https://md.research.elamur.ai/free/en/authors/ossama-obeid/)
20. [**Younes Samih**Hamad bin Khalifa University*11 papers here · 994 citations*](https://md.research.elamur.ai/free/en/authors/younes-samih/)
21. [**Bashar Alhafni**Carnegie Mellon University Qatar*14 papers here · 912 citations*](https://md.research.elamur.ai/free/en/authors/bashar-alhafni/)
22. [**Firoj Alam**Hamad bin Khalifa University*13 papers here · 885 citations*](https://md.research.elamur.ai/free/en/authors/firoj-alam/)
23. [**Mustafa Jarrar**Hamad bin Khalifa University*10 papers here · 782 citations*](https://md.research.elamur.ai/free/en/authors/mustafa-jarrar/)
24. [**Shammur Absar Chowdhury**Hamad bin Khalifa University*16 papers here · 743 citations*](https://md.research.elamur.ai/free/en/authors/shammur-absar-chowdhury/)
25. [**Behrang Mohit**Carnegie Mellon University Qatar*2 papers here · 700 citations*](https://md.research.elamur.ai/free/en/authors/behrang-mohit/)

[See all 43 people →](https://md.research.elamur.ai/free/en/countries/qatar/people/)

## Works in our library (79)

Works of its institutions, the most cited first.

1. [**SemEval-2017 Task 4: Sentiment Analysis in Twitter**Sara Rosenthal, Noura Farra, Preslav Nakov*2017 · 1,159 citations*](https://md.research.elamur.ai/free/en/papers/rosenthal-2017-semeval-2017-task-4-sentiment/)
2. [**Predicting the Type and Target of Offensive Posts in Social Media**Marcos Zampieri, Shervin Malmasi, Preslav Nakov, et al.*2019 · 1,113 citations*](https://md.research.elamur.ai/free/en/papers/zampieri-2019-predicting-type-target-offensive-posts/)
3. [**SemEval-2019 Task 6: Identifying and Categorizing Offensive Language in Social Media (OffensEval)**Marcos Zampieri, Shervin Malmasi, Preslav Nakov, et al.*2019 · 1,109 citations*](https://md.research.elamur.ai/free/en/papers/zampieri-2019-semeval-2019-task-6-identifying/)
4. [**SemEval-2018 Task 1: Affect in Tweets**Saif Mohammad, Felipe Bravo-Marquez, Mohammad Salameh, et al.*2018 · 1,092 citations*](https://md.research.elamur.ai/free/en/papers/mohammad-2018-semeval-2018-task-1-affect/)
5. [**SemEval-2016 Task 4: Sentiment Analysis in Twitter**Preslav Nakov, Alan Ritter, Sara Rosenthal, et al.*2016 · 1,081 citations*](https://md.research.elamur.ai/free/en/papers/nakov-2016-semeval-2016-task-4-sentiment/)
6. [**Automated Hate Speech Detection and the Problem of Offensive Language**Thomas Davidson, Dana Warmsley, Michael Macy, et al.*2017 · 1,007 citations*](https://md.research.elamur.ai/free/en/papers/davidson-2017-automated-hate-speech-detection-problem/)
7. [**Fine-grained Opinion Mining with Recurrent Neural Networks and Word Embeddings**Pengfei Liu, Shafiq Joty, Helen Meng*2015 · 589 citations*](https://md.research.elamur.ai/free/en/papers/liu-2015-fine-grained-opinion-mining-recurrent/)
8. [**Farasa: A Fast and Furious Segmenter for Arabic**Ahmed Abdelali, Kareem Darwish, Nadir Durrani, et al.*2016 · 506 citations*](https://md.research.elamur.ai/free/en/papers/abdelali-2016-farasa-fast-furious-segmenter-arabic/)
9. [**What do Neural Machine Translation Models Learn about Morphology?**Yonatan Belinkov, Nadir Durrani, Fahim Dalvi, et al.*2017 · 485 citations*](https://md.research.elamur.ai/free/en/papers/belinkov-2017-what-do-neural-machine-translation/)
10. [**Abusive Language Detection on Arabic Social Media**Hamdy Mubarak, Kareem Darwish, Walid Magdy*2017 · 411 citations*](https://md.research.elamur.ai/free/en/papers/mubarak-2017-abusive-language-detection-arabic-social/)
11. [**Fine-Grained Analysis of Propaganda in News Articles**Giovanni Da San Martino, Seunghak Yu, Alberto Barrón-Cedeño, et al.*2019 · 379 citations*](https://md.research.elamur.ai/free/en/papers/martino-2019-fine-grained-analysis-propaganda-news/)
12. [**SemEval-2020 Task 11: Detection of Propaganda Techniques in News Articles**Giovanni Da San Martino, Alberto Barrón-Cedeño, Henning Wachsmuth, et al.*2020 · 371 citations*](https://md.research.elamur.ai/free/en/papers/martino-2020-semeval-2020-task-11-detection/)
13. [**Predicting Factuality of Reporting and Bias of News Media Sources**Ramy Baly, Georgi Karadzhov, Dimitar Alexandrov, et al.*2018 · 334 citations*](https://md.research.elamur.ai/free/en/papers/baly-2018-predicting-factuality-reporting-bias-news/)
14. [**SemEval-2016 Task 3: Community Question Answering**Preslav Nakov, Lluís Màrquez, Alessandro Moschitti, et al.*2016 · 316 citations*](https://md.research.elamur.ai/free/en/papers/nakov-2016-semeval-2016-task-3-community/)
15. [**Findings of the VarDial Evaluation Campaign 2017**Marcos Zampieri, Shervin Malmasi, Nikola Ljubešić, et al.*2017 · 261 citations*](https://md.research.elamur.ai/free/en/papers/zampieri-2017-findings-vardial-evaluation-campaign/)
16. [**MOMENTA: A Multimodal Framework for Detecting Harmful Memes and Their Targets**Shraman Pramanick, Shivam Sharma, Dimitar Dimitrov, et al.*2021 · 212 citations*](https://md.research.elamur.ai/free/en/papers/pramanick-2021-momenta-multimodal-framework-detecting-harmful/)
17. [**A Survey on Stance Detection for Mis- and Disinformation Identification**Momchil Hardalov, Arnav Arora, Preslav Nakov, et al.*2022 · 187 citations*](https://md.research.elamur.ai/free/en/papers/hardalov-2022-survey-stance-detection-mis-disinformation/)
18. [**Integrating Stance Detection and Fact Checking in a Unified Corpus**Ramy Baly, Mitra Mohtarami, James Glass, et al.*2018 · 186 citations*](https://md.research.elamur.ai/free/en/papers/baly-2018-integrating-stance-detection-fact-checking/)
19. [**A Multidialectal Parallel Corpus of Arabic**Houda Bouamor, Nizar Habash, Kemal Oflazer*2014 · 175 citations*](https://md.research.elamur.ai/free/en/papers/bouamor-2014-multidialectal-parallel-corpus-arabic/)
20. [**Language Identification and Morphosyntactic Tagging: The Second VarDial Evaluation Campaign**Marcos Zampieri, Shervin Malmasi, Preslav Nakov, et al.*2018 · 169 citations*](https://md.research.elamur.ai/free/en/papers/zampieri-2018-language-identification-morphosyntactic-tagging-second/)
21. [**The AMARA Corpus: Building Parallel Language Resources for the Educational Domain**Ahmed Abdelali, Francisco Guzman, Hassan Sajjad, et al.*2014 · 165 citations*](https://md.research.elamur.ai/free/en/papers/abdelali-2014-amara-corpus-building-parallel-language/)
22. [**Overview of the DSL Shared Task 2015**Marcos Zampieri, Liling Tan, Nikola Ljubešić, et al.*2015 · 164 citations*](https://md.research.elamur.ai/free/en/papers/zampieri-2015-overview-dsl-shared-task/)
23. [**Large Scale Arabic Error Annotation: Guidelines and Framework**Wajdi Zaghouani, Behrang Mohit, Nizar Habash, et al.*2014 · 160 citations*](https://md.research.elamur.ai/free/en/papers/zaghouani-2014-large-scale-arabic-error-annotation/)
24. [**Automatic Stance Detection Using End-to-End Memory Networks**Mitra Mohtarami, Ramy Baly, James Glass, et al.*2018 · 156 citations*](https://md.research.elamur.ai/free/en/papers/mohtarami-2018-automatic-stance-detection-end-end/)
25. [**Fanar: An Arabic-Centric Multimodal Generative AI Platform**Fanar Team: Ummar Abbas, Mohammad Shahmeer Ahmad, Firoj Alam, et al.*2025 · 127 citations*](https://md.research.elamur.ai/free/en/papers/abbas-2025-fanar-arabic-centric-multimodal-generative/)

[See all 79 works →](https://md.research.elamur.ai/free/en/countries/qatar/works/)

## Concepts, shelves and topics

Of its works in our library: the number of works on each and its rating among them.

💡 Concepts 1[Word embeddings · 1 work](https://md.research.elamur.ai/free/en/concepts/word-embeddings/)

📚 Shelves 9[Natural language processing · 21 works](https://md.research.elamur.ai/free/en/shelves/nlp/) · [Linguistics & languages · 60 works](https://md.research.elamur.ai/free/en/shelves/linguistics/) · [Other · 2 works](https://md.research.elamur.ai/free/en/shelves/other/) · [Semitic languages · 5 works](https://md.research.elamur.ai/free/en/shelves/sem/) · [Computing & GPUs · 2 works](https://md.research.elamur.ai/free/en/shelves/computing/) · [Machine translation · 5 works](https://md.research.elamur.ai/free/en/shelves/machine-translation/) · [AI & machine learning · 1 work](https://md.research.elamur.ai/free/en/shelves/ai/) · [Law · 2 works](https://md.research.elamur.ai/free/en/shelves/law/) · [English · 1 work](https://md.research.elamur.ai/free/en/shelves/en/)

🧭 Topics 19[Sentiment & emotion · 18 works](https://md.research.elamur.ai/free/en/topics/sentiment-emotion/) · [Semitic languages · 51 works](https://md.research.elamur.ai/free/en/topics/semitic-languages/) · [Evaluation & benchmarks · 8 works](https://md.research.elamur.ai/free/en/topics/evaluation-benchmarks/) · [Bias, fairness & safety · 8 works](https://md.research.elamur.ai/free/en/topics/bias-fairness-safety/) · [Machine translation · 6 works](https://md.research.elamur.ai/free/en/topics/machine-translation/) · [Question answering · 1 work](https://md.research.elamur.ai/free/en/topics/question-answering/) · [Vision & language · 3 works](https://md.research.elamur.ai/free/en/topics/multimodal/) · [Part-of-speech tagging · 2 works](https://md.research.elamur.ai/free/en/topics/part-of-speech-tagging/) · [Morphology · 2 works](https://md.research.elamur.ai/free/en/topics/morphology/) · [Speech & spoken language · 6 works](https://md.research.elamur.ai/free/en/topics/speech/) · [Speech act theory · 1 work](https://md.research.elamur.ai/free/en/topics/speech-act-theory/) · [Language models & pretraining · 7 works](https://md.research.elamur.ai/free/en/topics/language-models/) · [Discourse analysis · 2 works](https://md.research.elamur.ai/free/en/topics/discourse-analysis/) · [Low-resource languages · 1 work](https://md.research.elamur.ai/free/en/topics/low-resource-languages/) · [Multilingual & cross-lingual NLP · 1 work](https://md.research.elamur.ai/free/en/topics/multilingual-nlp/)+4 more

## Works by year

### 2020s

[**2026** 11 works](https://md.research.elamur.ai/free/en/countries/qatar/year/2026/) · [**2025** 7 works](https://md.research.elamur.ai/free/en/countries/qatar/year/2025/) · [**2024** 4 works](https://md.research.elamur.ai/free/en/countries/qatar/year/2024/) · [**2023** 6 works](https://md.research.elamur.ai/free/en/countries/qatar/year/2023/) · [**2022** 6 works](https://md.research.elamur.ai/free/en/countries/qatar/year/2022/) · [**2021** 6 works](https://md.research.elamur.ai/free/en/countries/qatar/year/2021/) · [**2020** 4 works](https://md.research.elamur.ai/free/en/countries/qatar/year/2020/)

### 2010s

[**2019** 5 works](https://md.research.elamur.ai/free/en/countries/qatar/year/2019/) · [**2018** 7 works](https://md.research.elamur.ai/free/en/countries/qatar/year/2018/) · [**2017** 10 works](https://md.research.elamur.ai/free/en/countries/qatar/year/2017/) · [**2016** 6 works](https://md.research.elamur.ai/free/en/countries/qatar/year/2016/) · [**2015** 2 works](https://md.research.elamur.ai/free/en/countries/qatar/year/2015/) · [**2014** 4 works](https://md.research.elamur.ai/free/en/countries/qatar/year/2014/) · [**2012** 1 work](https://md.research.elamur.ai/free/en/papers/zhou-2012-cross-lingual-identification-ambiguous-discourse/)

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Updated 23 October 2026