--- title: "Kawarith: an Arabic Twitter Corpus for Crisis Events (2021)" description: "Arabic is heavily used on Twitter, yet before this work there was no openly available… 94% accuracy. 2 references, cited by 12 papers here." authors: ["Alaa Alharbi", "Mark Lee"] year: 2021 venue: "Proceedings of the Sixth Arabic Natural Language Processing Workshop" source: "https://aclanthology.org/2021.wanlp-1.5/" licence: "CC BY 4.0 (ACL Anthology)" licence_url: "https://creativecommons.org/licenses/by/4.0/" summary_licence: "Elamur Cosmic License" canonical: "https://research.elamur.ai/free/en/papers/alharbi-2021-kawarith-arabic-twitter-corpus-crisis/" --- [Elamur Research](https://md.research.elamur.ai/free/en/) · [this page in HTML](https://research.elamur.ai/free/en/papers/alharbi-2021-kawarith-arabic-twitter-corpus-crisis/) [home](https://md.research.elamur.ai/free/en/) / [papers](https://md.research.elamur.ai/free/en/papers/) / [2021](https://md.research.elamur.ai/free/en/papers/2021/page/6/) / Kawarith: an Arabic Twitter Corpus for Crisis Events # Kawarith: an Arabic Twitter Corpus for Crisis Events **Authors:** [Alaa Alharbi](https://md.research.elamur.ai/free/en/authors/alaa-alharbi/) and [Mark Lee](https://md.research.elamur.ai/free/en/authors/mark-lee/) · **Year:** 2021 · **Venue:** [Proceedings of the Sixth Arabic Natural Language Processing Workshop](https://md.research.elamur.ai/free/en/venues/wanlp/) ([WANLP 2021](https://md.research.elamur.ai/free/en/venues/wanlp/2021/)) · **Publisher:** [Association for Computational Linguistics](https://md.research.elamur.ai/free/en/publishers/association-for-computational-linguistics/) · **Licence:** [CC BY 4.0 (ACL Anthology)](https://creativecommons.org/licenses/by/4.0/) **Links:** [Publisher's page](https://aclanthology.org/2021.wanlp-1.5/) · [PDF](https://aclanthology.org/2021.wanlp-1.5.pdf) · [OpenAlex](https://openalex.org/W3155928145) **Citations:** 13 citations (OpenAlex, 2026-10-14) · [12](https://md.research.elamur.ai/free/en/papers/alharbi-2021-kawarith-arabic-twitter-corpus-crisis/cited-by/) in our library, of the 149,439 papers whose bibliographies we read ## Summary Arabic is heavily used on Twitter, yet before this work there was no openly available Arabic corpus covering many different crises. Alharbi and Lee built Kawārith, a multi-dialect collection of over one million Arabic tweets from 22 Middle Eastern emergencies between October 2018 and September 2020. The events span floods, shootings, bombings, wildfires, sandstorms, explosions and the COVID-19 pandemic. Tweets were gathered through the Twitter search API with an iterative, precision-oriented keyword and hashtag procedure. The authors explored the content to find the dominant information types. They then had annotators hand-label a subset from seven events with a multi-label scheme; COVID-19 tweets were labelled only for relevance. As a benchmark, they fine-tuned a separate AraBERT base model for each event. Even with small per-event training sets, the models reached solid accuracy and low Hamming loss, and macro-F1 stayed above 70% in nearly every case without any rebalancing. The resource supports situational-awareness tools, crisis detection and opinion analysis for a low-resource, dialect-rich language. ## Key points - Kawārith holds more than 1M Arabic tweets from 22 crises (2018–2020), released with the query terms and collection dates. - The gold set has 12,446 labelled tweets from seven events, including Jordan floods, Cairo bombing, Dragon storms, Beirut explosion and COVID-19. - Average Krippendorff's alpha is about 0.7. - Only 4.4% of items carry more than one label. - About 15% of the COVID-19 sample is irrelevant. - The authors also publish 405 domain-independent, multi-dialect Arabic stop words. - AraBERT base setup: - per-event stratified 80/20 split; - dropout 0.2 before a linear layer, binary cross-entropy; - learning rate 5e-5, maximum length 60, 5 epochs; - batch sizes 8 and 32, each run with three seeds. - Results: - COVID-19 relevance classification reached about 94% accuracy. - Multi-label Hamming loss stayed roughly between 0.05 and 0.08. - With fewer than about 600 training examples, batch size 8 beat 32. ## Interesting ideas - Collection grows from seed hashtags, adds newly found tags, and stops once no fresh relevant ones appear; it prefers event-specific terms over generic hazard words, trading recall for cleaner data. - Disagreements go to a third annotator, majority wins, and a tweet is dropped entirely when all three disagree, rather than forcing a label. - Labels are multi-label (a tweet can be both an update and a prayer), and scoring adds Hamming loss alongside macro and micro F1. - The authors note that irrelevant posts were often confused with the opinions class, and propose a binary relevance filter before fine-grained categorisation. - Every experiment is repeated with three seeds, and a smaller batch size proved better in the low-data regime. ## For Mova How these ideas could serve Mova, our small open language model — its files, stages and tests: - **I1** (learn): silver corpora for rare constructions could be built the same way, expanding seed patterns only while they keep pulling in genuine examples, so the training set stays clean. - **I2** (learn): when several witness parsers produce three different analyses, the sentence could be discarded from silver data instead of resolved by a tie-breaking rule. - **I3** (frames): speech-act and implicature tags could be allowed to co-occur, with Hamming loss showing partially correct label sets that exact-match accuracy would hide. - **I4** (answer): a cheap gate that checks whether the input is actually a question about the text could run before the readers, so off-topic inputs do not get forced into some answer type. - **I5** (learn): parser and classifier evaluations could report means over several seeds, and small-treebank training could default to smaller batches. *Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.* 📚 Shelves (4): [🖥️ Computer Science](https://md.research.elamur.ai/free/en/shelves/computer-science/) · 18,614 works · [🤖 Artificial Intelligence](https://md.research.elamur.ai/free/en/shelves/computer-science/artificial-intelligence/) · 17,965 works · [🤝 Social Sciences](https://md.research.elamur.ai/free/en/shelves/social-sciences/) · 1,954 works · [🤝 Communication](https://md.research.elamur.ai/free/en/shelves/social-sciences/communication/) · 104 works 🧭 Topics (1): [Semitic languages](https://md.research.elamur.ai/free/en/topics/semitic-languages/) · 3,066 works ## Cite Cite as: Alaa Alharbi and Mark Lee. 2021. Kawarith: an Arabic Twitter Corpus for Crisis Events. Proceedings of the Sixth Arabic Natural Language Processing Workshop. The paper itself, not this page. BibTeX from the library's record of the work. ```bibtex @inproceedings{alharbi2021kawarith, title = {{Kawarith: an Arabic Twitter Corpus for Crisis Events}}, author = {Alaa Alharbi and Mark Lee}, booktitle = {Proceedings of the Sixth Arabic Natural Language Processing Workshop}, year = {2021}, url = {https://aclanthology.org/2021.wanlp-1.5/} } ``` - [← previous Improving Arabic Diacritization with Regularized Decoding and Adversarial Training](https://md.research.elamur.ai/free/en/papers/qin-2021-improving-arabic-diacritization-regularized-decoding/) - [next → Machine Learning-Based Model for Sentiment and Sarcasm Detection](https://md.research.elamur.ai/free/en/papers/nayel-2021-machine-learning-based-model-sentiment/) - [↑ back to papers of 2021](https://md.research.elamur.ai/free/en/papers/2021/page/6/) ## References 2 references, 0 of them with a card of ours. ↗ leads to the work. 1. Aseel Addawood. 2020. Coronavirus: Public arabic twitter dataset. [↗](https://doi.org/10.20944/preprints202004.0263.v1) 2. Firoj Alam, Ferda Ofli, and Muhammad Imran. 2018a. Crisismmd: Multimodal twitter datasets from natural disasters. In Twelfth International AAAI Conference on Web and Social Media. [↗](https://doi.org/10.48550/arxiv.1805.00713) *Updated 1 November 2026*