---
title: "Detecting Stance of Authorities towards — Haouari 2023"
description: "The paper introduces a new task: deciding whether a tweet from a trusted authority supports, denies, or is unrelated to a rumor circulating on Arabic…"
authors: ["Fatima Haouari", "Tamer Elsayed"]
year: 2023
venue: "arXiv:2301.05863"
source: "https://arxiv.org/abs/2301.05863"
doi: "10.48550/arxiv.2301.05863"
arxiv: "2301.05863"
licence: "arXiv — non-exclusive license to distribute"
licence_url: "http://arxiv.org/licenses/nonexclusive-distrib/1.0/"
summary_licence: "Elamur Cosmic License"
canonical: "https://research.elamur.ai/free/en/papers/haouari-2023-detecting-stance-authorities-towards-rumors/"
---
[Elamur Research](https://md.research.elamur.ai/free/en/) · [this page in HTML](https://research.elamur.ai/free/en/papers/haouari-2023-detecting-stance-authorities-towards-rumors/)

[home](https://md.research.elamur.ai/free/en/) / [papers](https://md.research.elamur.ai/free/en/papers/) / [2023](https://md.research.elamur.ai/free/en/papers/2023/page/7/) / Detecting Stance of Authorities towards Rumors in Arabic Tweets: A Preliminary Study

# Detecting Stance of Authorities towards Rumors in Arabic Tweets: A Preliminary Study

**Authors:** Fatima Haouari, [Tamer Elsayed](https://md.research.elamur.ai/free/en/authors/tamer-elsayed/) · **Institutions:** [Qatar University](https://md.research.elamur.ai/free/en/universities/qatar-university/) · **Year:** 2023 · **Venue:** arXiv:2301.05863 · **Licence:** [arXiv — non-exclusive license to distribute](http://arxiv.org/licenses/nonexclusive-distrib/1.0/)

**Links:** [Publisher's page](https://arxiv.org/abs/2301.05863) · [PDF](https://arxiv.org/pdf/2301.05863) · [DOI 10.48550/arxiv.2301.05863](https://doi.org/10.48550/arxiv.2301.05863) · [arXiv 2301.05863](https://arxiv.org/abs/2301.05863) · [OpenAlex](https://openalex.org/W4327644041)

**Citations:** 9 citations (OpenAlex, 2026-10-14) · [3](https://md.research.elamur.ai/free/en/papers/haouari-2023-detecting-stance-authorities-towards-rumors/cited-by/) in our library, of the 140884 papers whose bibliographies we read

## Summary
The paper introduces a new task: deciding whether a tweet from a trusted authority supports, denies, or is unrelated to a rumor circulating on Arabic Twitter. Earlier rumor-verification work relied on reply threads, propagation structure, or Web evidence. Statements from the people or bodies who actually know the truth had not been used. The authors release AuSTR, a small test collection of rumor–evidence tweet pairs. It was built in two ways: from fact-checking articles in AraFacts, and by starting from authority timelines and then searching for user tweets that repeat the claims. Because AuSTR is small, it serves only as a test set. ARBERT stance classifiers are fine-tuned on three existing Arabic claim-stance corpora (ANS, ArabicFC, AraStance) and their combinations. The best transfer comes from training on AraStance alone, at 0.772 macro-F1. Almost all models score lower on AuSTR than on their own test sets. Denials remain the hardest class. The authors conclude that in-domain tweet data from authorities is needed. They argue that authority stance is a valuable extra evidence source for fact-checkers.

## Key points
- AuSTR has 409 pairs over 171 unique claims (41 true, 130 false):
  - 118 disagree (29%),
  - 62 agree (15%),
  - 229 unrelated (56%).
- The fact-checking route yielded only 4 agree pairs against 118 disagree. Authority-first collection was added to fill the agree class. Extra unrelated pairs were sampled from authority timelines posted before the rumor, to mimic realistic class skew.
- Training data and model setup:
  - Training corpora: ANS (3,786 pairs, sentences written by annotators), ArabicFC (3,042 pairs, Syria-war politics, only 3.3% disagree), and AraStance (3,676 pairs from 3 fact-checking sites).
  - All were collapsed to three labels: agree, disagree, unrelated.
  - Model: ARBERT with the claim as sentence A and the evidence as sentence B. A [CLS]-based classification layer sits on top.
  - Hyperparameters: up to 25 epochs, early-stopping patience of 5, sequence length 512.
  - Seven dataset combinations were trained.
- Results on AuSTR:
  - AraStance alone: agree F1 0.837, macro-F1 0.772.
  - All three corpora combined: macro-F1 0.751, with the top disagree F1 of 0.646 but weaker agree F1 (0.741).
  - ArabicFC alone: disagree F1 0.434.
- Failure analysis covered 17 pairs that all 7 models got wrong. Of these, 64.7% were authorities denying a rumor about themselves in the first person. The rest involved indirect agreement or denial.

## Interesting ideas
- When one class is rare in naturally found data, the authors flip the collection direction: they gather the evidence first and then hunt for matching claims, which rebalances labels without synthetic text. — For Mova (learn): Silver-data mining for rare constructions could start from the target structure and search for sentences that realize it, instead of waiting for them to appear in random text.
- All pairs sharing a claim go to the same split, so a model cannot score well just by memorizing a claim seen in training. — For Mova (learn): Evaluation sets for fables or word problems should be split by underlying story or problem template, so that leakage through near-duplicates does not inflate scores.
- Looking only at items that every trained model fails exposes systematic gaps in the training data, such as missing first-person evidence, rather than random noise. — For Mova (learn): Running several witness parsers or readers and inspecting their shared failures would point directly to phenomena absent from the silver data or the repair rules.
- The dominant failure is a speaker refuting a claim about themselves, which a model only catches if it links the author of the evidence to the subject of the rumor. — For Mova (frames): Coreference should tie first-person pronouns to the known speaker entity, so that a statement like "I am having dinner" is resolved as contradicting "X died".
- The authors noticed that disagreeing training articles share cue words like "denied" or "fake", which may let models ride on lexical shortcuts. — For Mova (learn): Negative controls could strip or swap such cue words to check that a contradiction decision comes from the meaning graph rather than from surface keywords.

*Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.*

Fields: [Linguistics & languages](https://md.research.elamur.ai/free/en/shelves/linguistics/)

Topics: [Semitic languages](https://md.research.elamur.ai/free/en/topics/semitic-languages/)

## Cite

The paper itself, not this page. BibTeX from the library's record of the work.

```bibtex
@misc{haouari2023detecting,
  title = {{Detecting Stance of Authorities towards Rumors in Arabic Tweets: A Preliminary Study}},
  author = {Haouari, Fatima and Elsayed, Tamer},
  year = {2023},
  doi = {10.48550/arxiv.2301.05863},
  eprint = {2301.05863},
  archivePrefix = {arXiv},
  url = {https://doi.org/10.48550/arxiv.2301.05863}
}
```

- [← previous A Multi-Task Dataset for Assessing Discourse Coherence in Chinese Essays: Structure, Theme, and Logic Analysis](https://md.research.elamur.ai/free/en/papers/wu-2023-multi-task-dataset-assessing-discourse/)
- [next → Dual Cache for Long Document Neural Coreference Resolution](https://md.research.elamur.ai/free/en/papers/guo-2023-dual-cache-long-document-neural/)
- [↑ back to papers of 2023](https://md.research.elamur.ai/free/en/papers/2023/page/7/)

## References

34 references, 9 of them with a card of ours. ↗ leads to the work, «find» searches for one we have no id for yet.

1. Abdul-Mageed, M., Elmadany, A., et al.: ARBERT & MARBERT: Deep Bidirectional Transformers for Arabic. In: Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers). pp. 7088–7105 (2021) [find](https://scholar.google.com/scholar?q=Abdul%2DMageed%2C+M%2E%2C+Elmadany%2C+A%2E%2C+et+al%2E%3A+ARBERT+%26+MARBERT%3A+Deep+Bidirectional+Transformers+for+Arabic%2E+In%3A+Proceedings+of+the+59th+Annual+Meeting+of+the+Association+for+Computational+Linguistics+and+th) [our card](https://md.research.elamur.ai/free/en/papers/abdul-2021-mageed-arbert-marbert-deep-bidirectional-transformers/)
2. Al-Yahya, M., Al-Khalifa, H., Al-Baity, H., AlSaeed, D., Essam, A.: Arabic Fake News Detection: Comparative Study of Neural Networks and Transformer-Based Approaches. Complexity 2021 (2021) [find](https://scholar.google.com/scholar?q=Al%2DYahya%2C+M%2E%2C+Al%2DKhalifa%2C+H%2E%2C+Al%2DBaity%2C+H%2E%2C+AlSaeed%2C+D%2E%2C+Essam%2C+A%2E%3A+Arabic+Fake+News+Detection%3A+Comparative+Study+of+Neural+Networks+and+Transformer%2DBased+Approaches%2E+Complexity+2021+%282021%29)
3. Alhindi, T., Alabdulkarim, A., Alshehri, A., Abdul-Mageed, M., Nakov, P.: AraStance: A Multi-Country and Multi-Domain Dataset of Arabic Stance Detection for Fact Checking. NLP4IF 2021 p. 57 (2021) [find](https://scholar.google.com/scholar?q=Alhindi%2C+T%2E%2C+Alabdulkarim%2C+A%2E%2C+Alshehri%2C+A%2E%2C+Abdul%2DMageed%2C+M%2E%2C+Nakov%2C+P%2E%3A+AraStance%3A+A+Multi%2DCountry+and+Multi%2DDomain+Dataset+of+Arabic+Stance+Detection+for+Fact+Checking%2E+NLP4IF+2021+p%2E+57+%282021%29) [our card](https://md.research.elamur.ai/free/en/papers/alhindi-2021-arastance-multi-country-multi-domain/)
4. Ali, Z.S., Mansour, W., Elsayed, T., Al-Ali, A.: AraFacts: The First Large Arabic Dataset of Naturally Occurring Claims. In: Proceedings of the Sixth Arabic Natural Language Processing Workshop. pp. 231–236 (2021) [find](https://scholar.google.com/scholar?q=Ali%2C+Z%2ES%2E%2C+Mansour%2C+W%2E%2C+Elsayed%2C+T%2E%2C+Al%2DAli%2C+A%2E%3A+AraFacts%3A+The+First+Large+Arabic+Dataset+of+Naturally+Occurring+Claims%2E+In%3A+Proceedings+of+the+Sixth+Arabic+Natural+Language+Processing+Workshop%2E+pp%2E+2)
5. Alqurashi, S., Hamoui, B., Alashaikh, A., Alhindi, A., Alanazi, E.: Eating Garlic Prevents COVID-19 Infection: Detecting Misinformation on the Arabic Content of Twitter. arXiv preprint arXiv:2101.05626 (2021) [↗](https://arxiv.org/abs/2101.05626)
6. Alqurashi, T.: Stance Analysis of Distance Education in the Kingdom of Saudi Arabia during the COVID-19 Pandemic Using Arabic Twitter Data. Sensors 22(3), 1006 (2022) [find](https://scholar.google.com/scholar?q=Alqurashi%2C+T%2E%3A+Stance+Analysis+of+Distance+Education+in+the+Kingdom+of+Saudi+Arabia+during+the+COVID%2D19+Pandemic+Using+Arabic+Twitter+Data%2E+Sensors+22%283%29%2C+1006+%282022%29)
7. Antoun, W., Baly, F., Hajj, H.: AraBERT: Transformer-based Model for Arabic Language Understanding. In: LREC 2020 Workshop Language Resources and Evaluation Conference 11–16 May 2020. p. 9 (2020) Title Suppressed Due to Excessive Length F. Haouari et al. [find](https://scholar.google.com/scholar?q=Antoun%2C+W%2E%2C+Baly%2C+F%2E%2C+Hajj%2C+H%2E%3A+AraBERT%3A+Transformer%2Dbased+Model+for+Arabic+Language+Understanding%2E+In%3A+LREC+2020+Workshop+Language+Resources+and+Evaluation+Conference+11%E2%80%9316+May+2020%2E+p%2E+9+%282020%29+Titl) [our card](https://md.research.elamur.ai/free/en/papers/antoun-2020-arabert-transformer-based-model-arabic/)
8. Bai, N., Meng, F., Rui, X., Wang, Z.: A multi-task attention tree neural net for stance classification and rumor veracity detection. Applied Intelligence pp. 1–11 (2022) [find](https://scholar.google.com/scholar?q=Bai%2C+N%2E%2C+Meng%2C+F%2E%2C+Rui%2C+X%2E%2C+Wang%2C+Z%2E%3A+A+multi%2Dtask+attention+tree+neural+net+for+stance+classification+and+rumor+veracity+detection%2E+Applied+Intelligence+pp%2E+1%E2%80%9311+%282022%29)
9. Bai, N., Meng, F., Rui, X., Wang, Z.: Rumor detection based on a Source-Replies conversation Tree Convolutional Neural Net. Computing 104(5), 1155–1171 (2022) [find](https://scholar.google.com/scholar?q=Bai%2C+N%2E%2C+Meng%2C+F%2E%2C+Rui%2C+X%2E%2C+Wang%2C+Z%2E%3A+Rumor+detection+based+on+a+Source%2DReplies+conversation+Tree+Convolutional+Neural+Net%2E+Computing+104%285%29%2C+1155%E2%80%931171+%282022%29)
10. Baly, R., Mohtarami, M., Glass, J., Màrquez, L., Moschitti, A., Nakov, P.: Integrating Stance Detection and Fact Checking in a Unified Corpus. In: Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 2 (Short Papers). pp. 21–27. Association for Computational Linguistics, New Orleans, Louisiana (Jun 2018) [find](https://scholar.google.com/scholar?q=Baly%2C+R%2E%2C+Mohtarami%2C+M%2E%2C+Glass%2C+J%2E%2C+Ma%CC%80rquez%2C+L%2E%2C+Moschitti%2C+A%2E%2C+Nakov%2C+P%2E%3A+Integrating+Stance+Detection+and+Fact+Checking+in+a+Unified+Corpus%2E+In%3A+Proceedings+of+the+2018+Conference+of+the+North+Amer) [our card](https://md.research.elamur.ai/free/en/papers/baly-2018-integrating-stance-detection-fact-checking/)
11. Barrón-Cedeño, A., Alam, F., Caselli, T., Da San Martino, G., Elsayed, T., Galassi, A., Haouari, F., Ruggeri, F., et al.: The CLEF-2023 CheckThat! Lab: Checkworthiness, Subjectivity, Political bias, Factuality, and Authority of News Articles and Their Sources. In: Proceedings of the 45th European Conference on Information Retrieval (ECIR’23) (2023) [find](https://scholar.google.com/scholar?q=Barro%CC%81n%2DCeden%CC%83o%2C+A%2E%2C+Alam%2C+F%2E%2C+Caselli%2C+T%2E%2C+Da+San+Martino%2C+G%2E%2C+Elsayed%2C+T%2E%2C+Galassi%2C+A%2E%2C+Haouari%2C+F%2E%2C+Ruggeri%2C+F%2E%2C+et+al%2E%3A+The+CLEF%2D2023+CheckThat%21+Lab%3A+Checkworthiness%2C+Subjectivity%2C+Political+bias%2C)
12. Bian, T., Xiao, X., Xu, T., Zhao, P., Huang, W., Rong, Y., Huang, J.: Rumor Detection on Social Media with Bi-Directional Graph Convolutional Networks. In: Proceedings of the AAAI Conference on Artificial Intelligence. pp. 549–556 (2020) [find](https://scholar.google.com/scholar?q=Bian%2C+T%2E%2C+Xiao%2C+X%2E%2C+Xu%2C+T%2E%2C+Zhao%2C+P%2E%2C+Huang%2C+W%2E%2C+Rong%2C+Y%2E%2C+Huang%2C+J%2E%3A+Rumor+Detection+on+Social+Media+with+Bi%2DDirectional+Graph+Convolutional+Networks%2E+In%3A+Proceedings+of+the+AAAI+Conference+on+Artifi)
13. Chen, L., Wei, Z., Li, J., Zhou, B., Zhang, Q., Huang, X.J.: Modeling Evolution of Message Interaction for Rumor Resolution. In: Proceedings of the 28th International Conference on Computational Linguistics. pp. 6377–6387 (2020) [find](https://scholar.google.com/scholar?q=Chen%2C+L%2E%2C+Wei%2C+Z%2E%2C+Li%2C+J%2E%2C+Zhou%2C+B%2E%2C+Zhang%2C+Q%2E%2C+Huang%2C+X%2EJ%2E%3A+Modeling+Evolution+of+Message+Interaction+for+Rumor+Resolution%2E+In%3A+Proceedings+of+the+28th+International+Conference+on+Computational+Lingu)
14. Choi, J., Ko, T., Choi, Y., Byun, H., Kim, C.k.: Dynamic graph convolutional networks with attention mechanism for rumor detection on social media. Plos one 16(8), e0256039 (2021) [find](https://scholar.google.com/scholar?q=Choi%2C+J%2E%2C+Ko%2C+T%2E%2C+Choi%2C+Y%2E%2C+Byun%2C+H%2E%2C+Kim%2C+C%2Ek%2E%3A+Dynamic+graph+convolutional+networks+with+attention+mechanism+for+rumor+detection+on+social+media%2E+Plos+one+16%288%29%2C+e0256039+%282021%29)
15. Darwish, K., Magdy, W., Zanouda, T.: Improved Stance Prediction in a User Similarity Feature Space. In: Proceedings of the 2017 IEEE/ACM international conference on advances in social networks analysis and mining 2017. pp. 145–148 (2017) [find](https://scholar.google.com/scholar?q=Darwish%2C+K%2E%2C+Magdy%2C+W%2E%2C+Zanouda%2C+T%2E%3A+Improved+Stance+Prediction+in+a+User+Similarity+Feature+Space%2E+In%3A+Proceedings+of+the+2017+IEEE%2FACM+international+conference+on+advances+in+social+networks+analysi)
16. Devlin, J., Chang, M.W., Lee, K., Toutanova, K.: BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding. arXiv preprint arXiv:1810.04805 (2018) [↗](https://arxiv.org/abs/1810.04805) [our card](https://md.research.elamur.ai/free/en/papers/devlin-2019-bert-pre-training-deep-bidirectional/)
17. Dougrez-Lewis, J., Kochkina, E., Arana-Catania, M., Liakata, M., He, Y.: PHE-MEPlus: Enriching Social Media Rumour Verification with External Evidence. In: Proceedings of the Fifth Fact Extraction and VERification Workshop (FEVER). pp. 49–58 (2022) [find](https://scholar.google.com/scholar?q=Dougrez%2DLewis%2C+J%2E%2C+Kochkina%2C+E%2E%2C+Arana%2DCatania%2C+M%2E%2C+Liakata%2C+M%2E%2C+He%2C+Y%2E%3A+PHE%2DMEPlus%3A+Enriching+Social+Media+Rumour+Verification+with+External+Evidence%2E+In%3A+Proceedings+of+the+Fifth+Fact+Extraction+and)
18. Elhadad, M.K., Li, K.F., Gebali, F.: COVID-19-FAKES: A Twitter (Arabic/English) Dataset for Detecting Misleading Information on COVID-19. In: International Conference on Intelligent Networking and Collaborative Systems. pp. 256–268. Springer (2020) [find](https://scholar.google.com/scholar?q=Elhadad%2C+M%2EK%2E%2C+Li%2C+K%2EF%2E%2C+Gebali%2C+F%2E%3A+COVID%2D19%2DFAKES%3A+A+Twitter+%28Arabic%2FEnglish%29+Dataset+for+Detecting+Misleading+Information+on+COVID%2D19%2E+In%3A+International+Conference+on+Intelligent+Networking+and+Col)
19. Haouari, F., Hasanain, M., Suwaileh, R., Elsayed, T.: ArCOV19-Rumors: Arabic COVID-19 Twitter Dataset for Misinformation Detection. In: Proceedings of the Sixth Arabic Natural Language Processing Workshop. pp. 72–81 (2021) [find](https://scholar.google.com/scholar?q=Haouari%2C+F%2E%2C+Hasanain%2C+M%2E%2C+Suwaileh%2C+R%2E%2C+Elsayed%2C+T%2E%3A+ArCOV19%2DRumors%3A+Arabic+COVID%2D19+Twitter+Dataset+for+Misinformation+Detection%2E+In%3A+Proceedings+of+the+Sixth+Arabic+Natural+Language+Processing+Work)
20. Hasanain, M., Haouari, F., Suwaileh, R., Ali, Z.S., Hamdan, B., Elsayed, T., Barrón-Cedeno, A., Da San Martino, G., Nakov, P.: Overview of CheckThat! 2020 Arabic: Automatic Identification and Verification of Claims in Social Media. In: CLEF (2020) [find](https://scholar.google.com/scholar?q=Hasanain%2C+M%2E%2C+Haouari%2C+F%2E%2C+Suwaileh%2C+R%2E%2C+Ali%2C+Z%2ES%2E%2C+Hamdan%2C+B%2E%2C+Elsayed%2C+T%2E%2C+Barro%CC%81n%2DCedeno%2C+A%2E%2C+Da+San+Martino%2C+G%2E%2C+Nakov%2C+P%2E%3A+Overview+of+CheckThat%21+2020+Arabic%3A+Automatic+Identification+and+Verific)
21. Inoue, G., Alhafni, B., Baimukan, N., Bouamor, H., Habash, N.: The Interplay of Variant, Size, and Task Type in Arabic Pre-trained Language Models. In: Proceedings of the Sixth Arabic Natural Language Processing Workshop. pp. 92–104 (2021) [find](https://scholar.google.com/scholar?q=Inoue%2C+G%2E%2C+Alhafni%2C+B%2E%2C+Baimukan%2C+N%2E%2C+Bouamor%2C+H%2E%2C+Habash%2C+N%2E%3A+The+Interplay+of+Variant%2C+Size%2C+and+Task+Type+in+Arabic+Pre%2Dtrained+Language+Models%2E+In%3A+Proceedings+of+the+Sixth+Arabic+Natural+Language) [our card](https://md.research.elamur.ai/free/en/papers/inoue-2021-interplay-variant-size-task-type/)
22. Jaziriyan, M.M., Akbari, A., Karbasi, H.: ExaASC: A General Target-Based Stance Detection Corpus in Arabic Language. In: 2021 11th International Conference on Computer Engineering and Knowledge (ICCKE). pp. 424–429. IEEE (2021) [find](https://scholar.google.com/scholar?q=Jaziriyan%2C+M%2EM%2E%2C+Akbari%2C+A%2E%2C+Karbasi%2C+H%2E%3A+ExaASC%3A+A+General+Target%2DBased+Stance+Detection+Corpus+in+Arabic+Language%2E+In%3A+2021+11th+International+Conference+on+Computer+Engineering+and+Knowledge+%28ICCKE) [our card](https://md.research.elamur.ai/free/en/papers/jaziriyan-2022-exaasc-general-target-based-stance/)
23. Khouja, J.: Stance Prediction and Claim Verification: An Arabic Perspective. In: Proceedings of the Third Workshop on Fact Extraction and VERification (FEVER). Association for Computational Linguistics, Seattle, USA (2020) [find](https://scholar.google.com/scholar?q=Khouja%2C+J%2E%3A+Stance+Prediction+and+Claim+Verification%3A+An+Arabic+Perspective%2E+In%3A+Proceedings+of+the+Third+Workshop+on+Fact+Extraction+and+VERification+%28FEVER%29%2E+Association+for+Computational+Linguistic)
24. Kumar, S., Carley, K.: Tree LSTMs with Convolution Units to Predict Stance and Rumor Veracity in Social Media Conversations. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics. Association for Computational Linguistics, Florence, Italy (Jul 2019) [find](https://scholar.google.com/scholar?q=Kumar%2C+S%2E%2C+Carley%2C+K%2E%3A+Tree+LSTMs+with+Convolution+Units+to+Predict+Stance+and+Rumor+Veracity+in+Social+Media+Conversations%2E+In%3A+Proceedings+of+the+57th+Annual+Meeting+of+the+Association+for+Computati)
25. Lan, W., Chen, Y., Xu, W., Ritter, A.: An Empirical Study of Pre-trained Transformers for Arabic Information Extraction. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). pp. 4727–4734. Association for Computational Linguistics, Online (Nov 2020) [find](https://scholar.google.com/scholar?q=Lan%2C+W%2E%2C+Chen%2C+Y%2E%2C+Xu%2C+W%2E%2C+Ritter%2C+A%2E%3A+An+Empirical+Study+of+Pre%2Dtrained+Transformers+for+Arabic+Information+Extraction%2E+In%3A+Proceedings+of+the+2020+Conference+on+Empirical+Methods+in+Natural+Language)
26. Liu, Y., Wu, Y.F.B.: Early Detection of Fake News on Social Media Through Propagation Path Classification with Recurrent and Convolutional Networks. In: Thirty-Second AAAI Conference on Artificial Intelligence (2018) [find](https://scholar.google.com/scholar?q=Liu%2C+Y%2E%2C+Wu%2C+Y%2EF%2EB%2E%3A+Early+Detection+of+Fake+News+on+Social+Media+Through+Propagation+Path+Classification+with+Recurrent+and+Convolutional+Networks%2E+In%3A+Thirty%2DSecond+AAAI+Conference+on+Artificial+Int)
27. Ma, J., Gao, W., Wong, K.F.: Rumor Detection on Twitter with Tree-structured Recursive Neural Networks. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). pp. 1980– 1989 (2018) [find](https://scholar.google.com/scholar?q=Ma%2C+J%2E%2C+Gao%2C+W%2E%2C+Wong%2C+K%2EF%2E%3A+Rumor+Detection+on+Twitter+with+Tree%2Dstructured+Recursive+Neural+Networks%2E+In%3A+Proceedings+of+the+56th+Annual+Meeting+of+the+Association+for+Computational+Linguistics+%28Vol) [our card](https://md.research.elamur.ai/free/en/papers/ma-2018-rumor-detection-twitter-tree-structured/)
28. Mahlous, A.R., Al-Laith, A.: Fake News Detection in Arabic Tweets during the COVID-19 Pandemic. International Journal of Advanced Computer Science and Applications 12(6) (2021) [find](https://scholar.google.com/scholar?q=Mahlous%2C+A%2ER%2E%2C+Al%2DLaith%2C+A%2E%3A+Fake+News+Detection+in+Arabic+Tweets+during+the+COVID%2D19+Pandemic%2E+International+Journal+of+Advanced+Computer+Science+and+Applications+12%286%29+%282021%29)
29. Roy, S., Bhanu, M., Saxena, S., Dandapat, S., Chandra, J.: gDART: Improving rumor verification in social media with Discrete Attention Representations. Information Processing & Management 59(3), 102927 (2022) [find](https://scholar.google.com/scholar?q=Roy%2C+S%2E%2C+Bhanu%2C+M%2E%2C+Saxena%2C+S%2E%2C+Dandapat%2C+S%2E%2C+Chandra%2C+J%2E%3A+gDART%3A+Improving+rumor+verification+in+social+media+with+Discrete+Attention+Representations%2E+Information+Processing+%26+Management+59%283%29%2C+10292)
30. Safaya, A., Abdullatif, M., Yuret, D.: KUISAIL at SemEval-2020 Task 12: BERT-CNN for Offensive Speech Identification in Social Media. In: Proceedings of the Fourteenth Workshop on Semantic Evaluation. pp. 2054–2059. International Committee for Computational Linguistics, Barcelona (online) (Dec 2020) [find](https://scholar.google.com/scholar?q=Safaya%2C+A%2E%2C+Abdullatif%2C+M%2E%2C+Yuret%2C+D%2E%3A+KUISAIL+at+SemEval%2D2020+Task+12%3A+BERT%2DCNN+for+Offensive+Speech+Identification+in+Social+Media%2E+In%3A+Proceedings+of+the+Fourteenth+Workshop+on+Semantic+Evaluation%2E) [our card](https://md.research.elamur.ai/free/en/papers/safaya-2020-kuisail-semeval-2020-task-12/)
31. Sawan, A., Thaher, T., Abu-el rub, N.: Sentiment Analysis Model for Fake News Identification in Arabic Tweets. In: 2021 IEEE 15th International Conference on Application of Information and Communication Technologies (AICT). pp. 1–6 (2021) [find](https://scholar.google.com/scholar?q=Sawan%2C+A%2E%2C+Thaher%2C+T%2E%2C+Abu%2Del+rub%2C+N%2E%3A+Sentiment+Analysis+Model+for+Fake+News+Identification+in+Arabic+Tweets%2E+In%3A+2021+IEEE+15th+International+Conference+on+Application+of+Information+and+Communicati)
32. Song, C., Shu, K., Wu, B.: Temporally evolving graph neural network for fake news detection. Information Processing & Management 58(6), 102712 (2021) [find](https://scholar.google.com/scholar?q=Song%2C+C%2E%2C+Shu%2C+K%2E%2C+Wu%2C+B%2E%3A+Temporally+evolving+graph+neural+network+for+fake+news+detection%2E+Information+Processing+%26+Management+58%286%29%2C+102712+%282021%29)
33. Wu, L., Rao, Y., Jin, H., Nazir, A., Sun, L.: Different Absorption from the Same Sharing: Sifted Multi-task Learning for Fake News Detection. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China (Nov 2019) [find](https://scholar.google.com/scholar?q=Wu%2C+L%2E%2C+Rao%2C+Y%2E%2C+Jin%2C+H%2E%2C+Nazir%2C+A%2E%2C+Sun%2C+L%2E%3A+Different+Absorption+from+the+Same+Sharing%3A+Sifted+Multi%2Dtask+Learning+for+Fake+News+Detection%2E+In%3A+Proceedings+of+the+2019+Conference+on+Empirical+Method)
34. Yu, J., Jiang, J., Khoo, L.M.S., Chieu, H.L., Xia, R.: Coupled Hierarchical Transformer for Stance-Aware Rumor Verification in Social Media Conversations. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). pp. 1392–1401. Association for Computational Linguistics, Online (Nov 2020) Title Suppressed Due to Excessive Length [find](https://scholar.google.com/scholar?q=Yu%2C+J%2E%2C+Jiang%2C+J%2E%2C+Khoo%2C+L%2EM%2ES%2E%2C+Chieu%2C+H%2EL%2E%2C+Xia%2C+R%2E%3A+Coupled+Hierarchical+Transformer+for+Stance%2DAware+Rumor+Verification+in+Social+Media+Conversations%2E+In%3A+Proceedings+of+the+2020+Conference+on+Emp)