Combating Misinformation in the Arab World: Challenges & Opportunities

Azza Abouzied; Firoj Alam; Paolo Papotti; Raian Ali

REVIEW 3 major objections 5 minor 23 references

Combating Misinformation in the Arab World: Challenges & Opportunities

T0 review · 3 major / 5 minor · reviewed 2026-08-07 · deepseek-v4-flash

Challenge this review Re-run · record.json Download PDF Read on arXiv ↗

Pith's one-line read To counter misinformation in the Arab world, the paper argues, automated detection, linked networks of credible multilingual sources, and culturally grounded social correction must operate as one coordinated system.

desk verdict Solid, well-written roadmap for Arabic misinformation research; the main soft spot is a load-bearing mitigation recommendation that cites prior simulation without exposing its assumptions. read the letter →

arxiv 2506.05582 v1 pith:5OCAQYL7 submitted 2025-06-05 cs.CL cs.AIcs.SI

Azza Abouzied , Firoj Alam , Raian Ali , Paolo Papotti This is my paper

classification cs.CLcs.AIcs.SI

keywords misinformationdisinformationArabworlddatavoidexploitsArabicnaturallanguageprocessingsocialcorrectionbehavioralchangefact-checkingnetworks

verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

This paper argues that no single technical tool or social campaign can counter misinformation in the Arab world; what is needed is a coordinated socio-technical response. It reviews Arabic-language detection and finds that dialect diversity, scarce annotated data, and the multidimensional space of context and modality limit current AI systems. It makes a specific mitigation claim: data void exploits, the gaps in search results that disinformers fill with false content, are best countered by building high-influence networks of credible, multilingual sources. It then argues that user correction can be encouraged by correcting misperceptions about social norms and framing correction as a pro-social act. The paper matters because it points to concrete, cost-effective roles for fact-checkers, news media, platforms, and governments in a region it describes as uniquely vulnerable.

What carries the argument

The carrying mechanism is the data void exploit, a gap in search results for a query that disinformers fill before authoritative content appears; the paper's counterweight is the high-influence information network, a coalition of credible, multilingual fact-checking and news sources linked so their content ranks and spreads. The paper's tracking tool uses search result rank as the observable signal of whether a disinformation or mitigation campaign is winning for a given keyword, and its game-theoretic simulations on a proxy web model are what justify the network-building strategy. On the social side, the social norms approach carries the argument: reluctance to correct others is treated as a misperception about relationship costs and futility that messaging and platform design can shift.

What would settle it

A concrete test: during a real crisis, track search result rankings for Arabic data-void keywords, launch a coordinated coalition of credible Arabic fact-checkers and news outlets publishing linked content, and measure whether their pages rise in top results and change searchers' beliefs; if the coalition's content does not displace disinforming material, the mitigation claim fails. The social correction claim would be settled similarly by a randomized norm-messaging campaign measuring actual correction behavior across several Arab countries.

Watch

Extended reading notes

Core claim

The paper's central claim, stated in its final section, is that effectively countering misinformation in the Arab region requires a coordinated socio-technical approach bringing together automated tools, networked mitigation strategies, and culturally grounded user engagement. Individually, AI detection systems cannot overcome the scarcity of annotated data across Arabic dialects, modalities, and contexts; mitigation cannot rely on platforms that are withdrawing from moderation; and social correction will not happen if users overestimate the relationship costs and futility of challenging misinformation. The paper supports this with a language-agnostic tracking tool that uses search result rank to measure data void exploit progress, adversarial game-theoretic simulations showing that high-influence networks of credible multilingual sources are the most cost-effective mitigation, and cross-cultural studies of UK and Arab samples identifying misperceptions about correcting others. Together these are meant to build a resilient information ecosystem through AI detection with human oversight, networked credible sources, and norm-based community engagement.

Load-bearing premise

The load-bearing premise is that the game-theoretic simulations on a simplified proxy model of the web capture how real searchers encounter and trust information; if that proxy does not reflect actual search behavior, the recommended credible-source networks may not be the cost-effective fix the paper says they are, and the social-correction findings likewise generalize from only two cultural samples.

Editorial extensions

If this is right

Independent mitigators can track an ongoing data void exploit in any language by monitoring search result rank, without needing platform cooperation.
If the simulations hold, linking credible multilingual fact-checkers and news outlets into high-influence networks is the most cost-effective response, even when platforms step back from moderation.
Correcting users' misperceptions about the social cost of challenging misinformation, through norm messaging and platform design, should increase willingness to correct misinformation in Arab communities.
Scaling Arabic detection requires culturally sensitive annotated data across dialects and modalities, and grass-roots fact-checking organizations can supply the repositories AI systems need.
Inoculation and norm-based messaging together offer a platform-level way to build resilience before false narratives take hold, though the paper notes they need long-term institutional investment.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

An editorial extension: the mitigation claim could be tested live by running the search-rank tracking on real Arabic queries during a crisis and checking whether a newly linked coalition of credible sources actually displaces disinforming content in top results; the simulation has not been validated against real search behavior.
The social-correction findings draw on UK and Arab samples; a multi-country Arab study with representative populations would reveal whether norm misperceptions and correction willingness differ between the Gulf, the Levant, and North Africa, which would change campaign design.
If platform moderation continues to retreat, the paper's logic implies that civil-society fact-checking networks become critical public infrastructure, raising questions about funding, trust, and coordination that the paper does not address.
A testable hypothesis implied by 'culturally grounded' engagement is that correction attempts may work better when framed as protecting the group rather than as individual truth-seeking, which could be compared directly in experiments.

Share X Bluesky LinkedIn Reddit HN

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Desk Editor's Note

read the letter

Read arXiv:2506.05582. It's a well-written review and position paper on misinformation in the Arab world, organized around detection, tracking/mitigation, and community engagement. No new experiments, datasets, or formal results. What it does well: it synthesizes a scattered literature into a coherent socio-technical framework, with a nice concrete example of dialectal ambiguity (Tunisian vs Egyptian 'ynajjim/yenaggim'), and it makes a sensible case for combining automated tools, networked credible sources, and culturally grounded correction. Sections 2 and 4 are accurate, fair summaries of the cited work, including the authors' own prior studies on social norms and misperceptions. The paper is honest that many open problems remain.

The soft spots are where the paper moves from synthesis to prescription. Section 3's central recommendation — that building high-influence networks of credible multilingual sources is the core cost-effective mitigation for data void exploits — rests on "adversarial game-theoretic simulations on a proxy model of the web" from the authors' previous CIKM paper [15]. The manuscript doesn't restate the simulation's assumptions, the agent payoffs, the visibility model, or any sensitivity analysis, and it doesn't validate the proxy against real search behavior. That's a load-bearing missing argument for a position paper making a concrete cost-effectiveness claim. The reader's conditional verdict is justified on this point. The social correction recommendations also generalize from two UK/Arab samples to the whole region; that's a minor but real overreach, worth stating as a limitation.

None of this undermines the paper's value as a roadmap. For a reader working on Arabic NLP, fact-checking, or platform governance in the region, it's a useful map of challenges, datasets, and open problems. The framework figure is reasonable. The citation pattern is fine, heavy on the authors' own prior work but those are the concrete empirical studies in the area.

My sense: this paper should go to peer review as a survey/position paper. A good referee would ask the authors to either briefly expose the simulation model in Section 3 or explicitly label that recommendation as derived from prior work with a pointer, and to temper the regional generalization of the correction studies. That's revision territory, not desk-reject territory.

Referee Report

3 major / 5 minor

Summary. This position paper reviews the landscape of misinformation research in the Arab world, organizing it into three pillars: automated detection, tracking and mitigation of data void exploits, and community engagement through social correction. The authors argue that no single technical or social intervention is sufficient and that a coordinated socio-technical approach, combining AI detection tools, networked credible-source infrastructures, and culturally grounded user engagement, is required for a resilient information ecosystem. The paper synthesizes recent Arabic NLP datasets and benchmarks, introduces data void exploits as a distinct threat model, and draws on the authors' prior studies on social-norm misperceptions and co-designed interventions. It concludes with a framework (Figure 1) that maps challenges to technical tools, social actions, and resilience outcomes.

Significance. If its central claim is accepted, the paper provides a useful conceptual map for researchers and policymakers working on Arabic-language misinformation, emphasizing dialectal diversity and data sparsity as first-order problems rather than afterthoughts. The paper's strength is its credible synthesis of recent benchmarks (e.g., AraNews, ArMeme, CheckThat!, LAraBench) and its integration of psychological approaches (social norms, inoculation) with technical detection and mitigation. As a position paper, it offers no original experiments, but it does advance a falsifiable framework: that networked mitigation and social correction are necessary complements to automated detection. The manuscript would be more persuasive if it exposed the evidence base for its most concrete operational claim, the cost-effectiveness of high-influence information networks, rather than relying on an unpublished-in-paper simulation.

major comments (3)

[Section 3, 'Tracking and Mitigation'] The claim that 'establishing high-influence information networks is core to a cost-effective response to an ongoing exploit' is presented as a result of 'adversarial game-theoretic simulations on a proxy model of the web' [15], but the manuscript does not describe the proxy model's structural assumptions, the agents' payoff functions, the ranking or visibility model, or any sensitivity analysis. Because this recommendation is carried forward into Section 5 ('our research shows that cost-effective responses ... require ... high-influence networks of credible, multilingual sources') and is one of the three pillars of the socio-technical framework in Figure 1, it is load-bearing. Please either (a) summarize the simulation setup and its key findings in enough detail for a reader to assess the inference, or (b) explicitly temper the claim as a hypothesis contingent on the proxy model's fidelity.
[Section 4, 'Community Engagement'] The recommendations on social correction—such as the social norms approach and framing correction as an altruistic act—are generalized from 'two distinct cultural contexts, Arab and UK populations' [10, 18] to the entire Arab region. The manuscript does not report the sample composition (which countries, how recruited, sample size) or discuss how representative these samples are of the region's linguistic and cultural diversity. Given that the paper's own introduction emphasizes heterogeneity across Arab societies, this unqualified generalization is a gap that should be addressed by stating the scope and limitations of the underlying studies.
[Section 2, 'Detection'] The statement that 'recent benchmarks demonstrate considerable variability in state-of-the-art performance across tasks ... with accuracy scores ranging from 0.55 to 0.95' is too vague to support the conclusion that 'significant room for improvement' remains. No specific tasks, datasets, or model configurations are tied to the endpoints of this range. Since the paper's framework depends on a reliable assessment of detection capabilities, please specify which systems and benchmarks produce these values and whether the range reflects different tasks, languages, or evaluation protocols.

minor comments (5)

[Introduction] The sentence 'One such threat the region to which is particularly susceptible to is a data void exploit' is ungrammatical; please rewrite.
[Introduction] The Arabic word in the dialectal example appears to be missing from the text (after 'the word'); the rendered manuscript shows no glyph, which makes the example impossible to follow.
[References] Reference [8] is formatted as 'Golebiewski and boyd'; the surname 'boyd' should be lowercase 'boyd' in the text as well, and the citation style should be consistent throughout.
[Section 4] The phrase 'the Spiral of Silence Theory applies' is attributable to Olson and LaPoe [5], but the sentence would benefit from a page number or a direct quote to clarify whether the authors are endorsing or merely reporting the original claim.
[Figure 1] The figure is informative but the text labels ('Mitigation Correction/Resilience', 'Synergy') are not explained in the caption; a brief description of the flow and the meaning of the arrows would improve accessibility.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: the paper's recommendations are synthesized from cited empirical studies, and no derivation reduces to its own inputs.

full rationale

This is a position and survey paper, not a formal derivation. The central claim in Section 5 (a coordinated socio-technical approach combining automated tools, networked mitigation, and culturally grounded user engagement) is a synthesis of findings drawn from prior work rather than a prediction derived within the manuscript. The most concrete load-bearing assertions are the data-void tracking tool and mitigation results in Section 3, attributed to the authors' CIKM 2024 paper [15], and the social-correction misperception findings in Section 4, attributed to [10] and [18]. These are cited peer-reviewed empirical and simulation studies published elsewhere; the present paper does not redefine any term in terms of its conclusions, and it does not fit parameters to data and then present those fits as predictions. The lack of restated assumptions for the proxy web model in [15] is a transparency and correctness concern, not a circularity, because the cited result is independent support rather than an input to this paper's argument. No equations or definitions in this manuscript exhibit self-definitional or fitted-input circularity. Therefore, the paper is not circular, and any concerns about reliance on self-citations are about evidence weight, not logical circularity.

Assumptions & free parameters 0 free parameters · 3 assumptions · 0 invented entities

The paper's framework relies on several domain assumptions imported from cited work: the salience of data void exploits, the generality of cross-cultural survey findings on correction avoidance, and the transferability of inoculation from gambling to misinformation. There are no fitted parameters or invented entities in this review; the ledger entries capture the load-bearing premises.

assumptions (3)

domain assumption Data void exploits are a major disinformation vector in the Arab region, and search-result rank tracks their efficacy.
Invoked in Section 3 and Section 5 to justify the tracking tool and network strategy; sourced to [8] and [15], with simulation evidence not shown in this paper.
domain assumption The reasons and misperceptions behind refusing to correct misinformation, measured in UK and Arab samples, generalize across the diverse Arab world.
Section 4 and Section 5 use this to propose social norms interventions; the samples come from [10] and [18].
domain assumption Attitudinal inoculation and explainable-interface effects, shown in gambling contexts, transfer to misinformation correction settings.
Section 4 and Section 5 combine findings from [6] and [14] to propose platform-based inoculation for misinformation, a domain transfer not directly tested.

how reviews work

0 comments

Cite this review

Pith. "Pith review of Combating Misinformation in the Arab World: Challenges & Opportunities." pith.science (2026). https://pith.science/paper/5OCAQYL7

@misc{pith2026250605582,
  author       = {Pith},
  title        = {Pith review of: Combating Misinformation in the Arab World: Challenges & Opportunities},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/5OCAQYL7}},
  note         = {Machine review of arXiv:2506.05582}
}

read the original abstract

Misinformation and disinformation pose significant risks globally, with the Arab region facing unique vulnerabilities due to geopolitical instabilities, linguistic diversity, and cultural nuances. We explore these challenges through the key facets of combating misinformation: detection, tracking, mitigation and community-engagement. We shed light on how connecting with grass-roots fact-checking organizations, understanding cultural norms, promoting social correction, and creating strong collaborative information networks can create opportunities for a more resilient information ecosystem in the Arab world.

Figures

Figures reproduced from arXiv: 2506.05582 by the authors.

Discussion (0). Sign in to comment.

Reference graph

Works this paper leans on

23 extracted references · 20 canonical work pages

[15]

Miro Mannino, Junior Garcia, Reem Hazim, Azza Abouzied, and Paolo Papotti. 2024. Data Void Exploits: Tracking & Mitigation Strategies. In Proceedings of the 33rd ACM International Conference on Information and Knowledge Management, CIKM 2024, Boise, ID, USA, October 21-25, 2024 , Edoardo Serra and Francesca Spezzano (Eds.). ACM, 1627–1637. https://doi.org...

arXiv 2024
[1]

Ahmed Abdelali, Hamdy Mubarak, Shammur Chowdhury, Maram Hasanain, Basel Mousi, Sabri Boughorbel, Samir Abdaljalil, Yassine El Kheir, Daniel Izham, Fahim Dalvi, Majd Hawasly, Nizi Nazar, Youssef Elshahawy, Ahmed Ali, Nadir Durrani, Natasa Milic-Frayling, and Firoj Alam. 2024. LAraBench: Benchmarking Arabic AI with Large Language Models. In Proceedings of t...

work page 2024
[2]

Arid Hasan, and Maram Hasanain

Firoj Alam, Abul Hasnat, Fatema Ahmad, Md. Arid Hasan, and Maram Hasanain. 2024. ArMeme: Propagandistic Content in Arabic Memes. In Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing , Yaser Al-Onaizan, Mohit Bansal, and Yun-Nung Chen (Eds.). Association for Computational Linguistics, Miami, Florida, USA, 21071–21090. h...

work page doi:10.18653/v1/2024.emnlp-main.1173 2024
[3]

Firoj Alam, Shaden Shaar, Fahim Dalvi, Hassan Sajjad, Alex Nikolov, Hamdy Mubarak, Giovanni Da San Martino, Ahmed Abdelali, Nadir Durrani, Kareem Darwish, Abdulaziz Al-Homaid, Wajdi Zaghouani, Tommaso Caselli, Gijs Danoe, Friso Stolk, Britt Bruntink, and Preslav Nakov. 2021. Fighting the COVID-19 Infodemic: Modeling the , Vol. 1, No. 1, Article . Publicat...

2021
[4]

Taghreed Alotaibi and Hmood Al-Dossari. 2024. A Review of Fake News Detection Techniques for Arabic Language. International Journal of Advanced Computer Science & Applications 15, 1 (2024)

work page 2024
[5]

Candi Carter Olson and Victoria LaPoe. 2018. Combating the digital spiral of silence: Academic activists versus social media trolls. Mediating misogyny: Gender, technology, and harassment (2018), 271–291

work page 2018
[6]

Deniz Cemiloglu, Selin Gurgun, Emily Arden-Close, Nan Jiang, and Raian Ali. 2024. Explainability as a psychological inoculation: building resistance to digital persuasion in online gambling through explainable interfaces. International Journal of Human–Computer Interaction 40, 23 (2024), 8378–8396

work page 2024
[7]

Abdelrahim Elmadany, Muhammad Abdul-Mageed, Tariq Alhindi, et al. 2020. Machine Generation and Detection of Arabic Manipulated and Fake News. In Proceedings of the Fifth Arabic Natural Language Processing Workshop . 69–84

work page 2020

Show all 23 references

[8]

Michael Golebiewski and danah boyd. 2019. Data Voids: Where Missing Data Can Easily Be Exploited. https: //datasociety.net/library/data-voids/

2019
[9]

Selin Gurgun, Emily Arden-Close, Keith Phalp, and Raian Ali. 2024. Motivated by Design: A Codesign Study to Promote Challenging Misinformation on Social Media. Human Behavior and Emerging Technologies 2024, 1 (2024), 5595339

2024
[10]

Selin Gurgun, Muaadh Noman, Emily Arden-Close, Keith Phalp, and Raian Ali. 2024. How Would I Be Perceived If I Challenge Individuals Sharing Misinformation? Exploring Misperceptions in the UK and Arab Samples and the Potential for the Social Norms Approach. In International Co...

2024
[11]

Maram Hasanain, Fatema Ahmad, and Firoj Alam. 2024. Can GPT-4 Identify Propaganda? Annotation and Detection of Propaganda Spans in News Articles. In Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COL...

2024
[12]

Mike Isaac and Theodore Schleifer. 2025. Meta Says It Will End Its Fact-Checking Program on Social Media Posts. The New York Times (2025). https://www.nytimes.com/2025/01/07/business/meta-community-notes-x.html

2025
[13]

Mohamed Bayan Kmainasi, Ali Ezzat Shahroor, Maram Hasanain, Sahinur Rahman Laskar, Naeemul Hassan, and Firoj Alam. 2025. LlamaLens: Specialized Multilingual LLM for Analyzing News and Social Media Content. In Findings of the Association for Computational Linguistics: NAACL 202...

2025
[14]

Stephan Lewandowsky and Sander van der Linden. 2021. Countering Misinformation and Fake News Through Inoculation and Prebunking. European Review of Social Psychology 32, 2 (2021), 348–384. https://doi.org/10.1080/ 10463283.2021.1876983 arXiv:https://doi.org/10.1080/10463283.20...

2021
[16]

Muhammad Shujaat Mirza, Labeeba Begum, Liang Niu, Sarah Pardo, Azza Abouzied, Paolo Papotti, and Christina Pöpper
[17]

Muaadh Noman, Selin Gurgun, Keith Phalp, and Raian Ali. 2024. Designing social media to foster user engagement in challenging misinformation: a cross-cultural comparison between the UK and Arab countries. Humanities and Social Sciences Communications 11, 1 (2024), 1–13

2024
[18]

Muaadh Noman, Selin Gurgun, Keith Phalp, Preslav Nakov, and Raian Ali. 2023. Challenging others when posting misinformation: a UK vs. Arab cross-cultural comparison on the perception of negative consequences and injunctive norms. Behaviour & Information Technology (2023), 1–21

2023
[19]

Mohammed Saeed, Nicolas Traub, Maelle Nicolas, Gianluca Demartini, and Paolo Papotti. 2022. Crowdsourced Fact- Checking at Twitter: How Does the Crowd Compare With Experts?. In Proceedings of the 31st ACM International Conference on Information & Knowledge Management, Atlanta,...

2022
[20]

Zien Sheikh Ali, Watheq Mansour, Tamer Elsayed, and Abdulaziz Al-Ali. 2021. AraFacts: The First Large Arabic Dataset of Naturally Occurring Claims. In Proceedings of the Sixth Arabic Natural Language Processing Workshop , Nizar Habash, Houda Bouamor, Hazem Hajj, Walid Magdy, W...

2021
[21]

Mohammed Abbas Yousef, Abeer ElKorany, and Hanaa Bayomi. 2024. Fake-news detection: a survey of evaluation Arabic datasets. Social Network Analysis and Mining 14, 1 (2024), 225. https://doi.org/10.1007/s13278-024-01370-2

2024 doi
[22]

Yifan Zhang, Giovanni Da San Martino, Alberto Barrón-Cedeño, Salvatore Romeo, Jisun An, Haewoon Kwak, Todor Staykovski, Israa Jaradat, Georgi Karadzhov, Ramy Baly, et al. 2019. Tanbih: Get To Know What You Are Reading. In Proceedings of the 2019 Conference on Empirical Methods...

2019
[2023]

In 30th Annual Network and Distributed System Security Symposium, NDSS 2023, San Diego, California, USA, February 27 - March 3, 2023

Tactics, Threats & Targets: Modeling Disinformation and its Mitigation. In 30th Annual Network and Distributed System Security Symposium, NDSS 2023, San Diego, California, USA, February 27 - March 3, 2023 . The Internet Society. https://www.ndss-symposium.org/ndss-paper/tactic...

2023

Pith tools

Reviewed August 7, 2026 · model on record in the stance chip above.

[1] [15]

Miro Mannino, Junior Garcia, Reem Hazim, Azza Abouzied, and Paolo Papotti. 2024. Data Void Exploits: Tracking & Mitigation Strategies. In Proceedings of the 33rd ACM International Conference on Information and Knowledge Management, CIKM 2024, Boise, ID, USA, October 21-25, 2024 , Edoardo Serra and Francesca Spezzano (Eds.). ACM, 1627–1637. https://doi.org...

arXiv 2024

[2] [1]

Ahmed Abdelali, Hamdy Mubarak, Shammur Chowdhury, Maram Hasanain, Basel Mousi, Sabri Boughorbel, Samir Abdaljalil, Yassine El Kheir, Daniel Izham, Fahim Dalvi, Majd Hawasly, Nizi Nazar, Youssef Elshahawy, Ahmed Ali, Nadir Durrani, Natasa Milic-Frayling, and Firoj Alam. 2024. LAraBench: Benchmarking Arabic AI with Large Language Models. In Proceedings of t...

work page 2024

[3] [2]

Arid Hasan, and Maram Hasanain

Firoj Alam, Abul Hasnat, Fatema Ahmad, Md. Arid Hasan, and Maram Hasanain. 2024. ArMeme: Propagandistic Content in Arabic Memes. In Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing , Yaser Al-Onaizan, Mohit Bansal, and Yun-Nung Chen (Eds.). Association for Computational Linguistics, Miami, Florida, USA, 21071–21090. h...

work page doi:10.18653/v1/2024.emnlp-main.1173 2024

[4] [3]

Firoj Alam, Shaden Shaar, Fahim Dalvi, Hassan Sajjad, Alex Nikolov, Hamdy Mubarak, Giovanni Da San Martino, Ahmed Abdelali, Nadir Durrani, Kareem Darwish, Abdulaziz Al-Homaid, Wajdi Zaghouani, Tommaso Caselli, Gijs Danoe, Friso Stolk, Britt Bruntink, and Preslav Nakov. 2021. Fighting the COVID-19 Infodemic: Modeling the , Vol. 1, No. 1, Article . Publicat...

2021

[5] [4]

Taghreed Alotaibi and Hmood Al-Dossari. 2024. A Review of Fake News Detection Techniques for Arabic Language. International Journal of Advanced Computer Science & Applications 15, 1 (2024)

work page 2024

[6] [5]

Candi Carter Olson and Victoria LaPoe. 2018. Combating the digital spiral of silence: Academic activists versus social media trolls. Mediating misogyny: Gender, technology, and harassment (2018), 271–291

work page 2018

[7] [6]

Deniz Cemiloglu, Selin Gurgun, Emily Arden-Close, Nan Jiang, and Raian Ali. 2024. Explainability as a psychological inoculation: building resistance to digital persuasion in online gambling through explainable interfaces. International Journal of Human–Computer Interaction 40, 23 (2024), 8378–8396

work page 2024

[8] [7]

Abdelrahim Elmadany, Muhammad Abdul-Mageed, Tariq Alhindi, et al. 2020. Machine Generation and Detection of Arabic Manipulated and Fake News. In Proceedings of the Fifth Arabic Natural Language Processing Workshop . 69–84

work page 2020