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arxiv: 2606.00193 · v2 · pith:CEPZOWEZnew · submitted 2026-05-29 · 💻 cs.CL

BOUTEF: A Multilingual Corpus for FakeNews in North Africa -- Language as a Weapon

Kamel Smaili , Yassine Toughrai , Amina Laggoun , David Langlois This is my paper

Pith reviewed 2026-06-28 22:32 UTC · model grok-4.3

classification 💻 cs.CL
keywords fake newsmultilingual corpusNorth Africamisinformationsocial mediadialectscode-switching
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The pith

A multilingual corpus shows that fake news in North Africa spreads through emotional and hybrid linguistic practices unlike factual debunking.

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

This paper presents BOUTEF as a large-scale corpus collecting fake narratives, genuine narratives, user comments, and debunking information from social media in Algeria and Tunisia. The analysis of this resource demonstrates that fake content uses emotionally charged stories, sensational framing, and mixes of dialects and languages to increase spread and engagement, whereas debunking takes a more factual approach. It also finds statistical ties between specific themes and whether information is fake or real, plus links between high user engagement and greater visibility of fake items. These patterns matter for understanding how misinformation operates in multilingual, under-resourced areas where language mixing is common. The work further compares the two countries to note both common and distinct features influenced by local conditions.

Core claim

By building the BOUTEF corpus that includes fake and genuine narratives along with comments and verified debunking across standard Arabic, local dialects, Arabizi, French, English, and mixed forms, the authors show through quantitative and qualitative study that fake news depends on emotionally charged narratives, sensational framing, and hybrid linguistic practices to enhance virality and audience engagement, while debunking content uses a factual and verification-oriented style, with significant associations between thematic categories and veracity as well as correlations between engagement and fake visibility, and differences between Algeria and Tunisia shaped by sociopolitical contexts.

What carries the argument

The BOUTEF corpus integrating fake narratives, genuine narratives, user comments, and debunking information across multiple languages and linguistic varieties.

Load-bearing premise

The collected narratives and annotations accurately represent the broader fake news landscape in Algeria and Tunisia without systematic selection bias or labeling errors.

What would settle it

Finding no significant statistical association between thematic categories and message veracity in an independently gathered dataset from Algeria and Tunisia would challenge the reported empirical results.

Figures

Figures reproduced from arXiv: 2606.00193 by Amina Laggoun, David Langlois, Kamel Smaili, Yassine Toughrai.

Figure 1
Figure 1. Figure 1: Distinction between Fake news, misinformation and disinformation. [PITH_FULL_IMAGE:figures/full_fig_p004_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: Workflow for constructing the BOUTEF-Fake (iterative feedback with right-angled open [PITH_FULL_IMAGE:figures/full_fig_p010_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: Overall structure of the BOUTEF corpus. 3.4 Languages and dialects within BOUTEF The analysis of the BOUTEF corpus highlights strong multilingual dynamics and rich expressive lexicons. Modern Standard Arabic (MSA) is the dominant language in fake news posts, likely because it maximizes comprehensibility across the Arabic-speaking audience, and debunking content is also predominantly written in MSA. By cont… view at source ↗
Figure 4
Figure 4. Figure 4: An altered image depicts a person stating, At 93 years old, I would like to see my country [PITH_FULL_IMAGE:figures/full_fig_p017_4.png] view at source ↗
Figure 5
Figure 5. Figure 5: Duration of some fake news in BOUTEF. For instance, the false claim that Neymar was of Algerian origin circulated for 3,533 days (9 years), while the rumor of President Tebboune’s death persisted for 1,756 days (4 years). Even quickly debunked stories, such as the widely shared narrative about a professor who became father after his wife’s death, remained online for 1,837 days (5 years) ( [PITH_FULL_IMAGE… view at source ↗
Figure 6
Figure 6. Figure 6: Misinformation concerning the state of a professor [PITH_FULL_IMAGE:figures/full_fig_p020_6.png] view at source ↗
Figure 7
Figure 7. Figure 7: Distribution of message length by type. the highest median and the widest interquartile range, showing that these messages are generally longer and more variable than the others, which may reflect the explanatory nature of debunking messages. In contrast, FakeComment and Fake messages have lower medians and narrower boxes, which suggests shorter and more concentrated distributions. The GENUINE category als… view at source ↗
Figure 8
Figure 8. Figure 8: Chi-square standardized residuals for theme and message type. [PITH_FULL_IMAGE:figures/full_fig_p026_8.png] view at source ↗
Figure 9
Figure 9. Figure 9: Percentage distribution of fake news categories in BOUTEF (FC: Fabricated Content; MIC: [PITH_FULL_IMAGE:figures/full_fig_p027_9.png] view at source ↗
Figure 10
Figure 10. Figure 10: Distribution of gender in BOUTEF (UND: undetermined gender; S: website without an [PITH_FULL_IMAGE:figures/full_fig_p028_10.png] view at source ↗
Figure 11
Figure 11. Figure 11: Word cloud of fake [PITH_FULL_IMAGE:figures/full_fig_p029_11.png] view at source ↗
Figure 12
Figure 12. Figure 12: Word cloud of genuine posts. affective and participatory. Very high frequencies of é<Ë@ (Allah) which occurs 11,134 times, Q [PITH_FULL_IMAGE:figures/full_fig_p031_12.png] view at source ↗
Figure 13
Figure 13. Figure 13: Distribution of narratives in the BOUTEF-FAKE part. [PITH_FULL_IMAGE:figures/full_fig_p039_13.png] view at source ↗
Figure 14
Figure 14. Figure 14: Regional distribution in fake news for Algeria and Tunisia. [PITH_FULL_IMAGE:figures/full_fig_p041_14.png] view at source ↗
Figure 15
Figure 15. Figure 15: Distribution of entropy per message and per type. [PITH_FULL_IMAGE:figures/full_fig_p045_15.png] view at source ↗
read the original abstract

The rapid spread of fake news on social media has become a major challenge, particularly in multilingual and under-resourced contexts such as North Africa. In this paper, we introduce BOUTEF, a large-scale multilingual corpus designed to study the propagation, characteristics, and impact of fake news in Algeria and Tunisia. The corpus integrates three complementary components: fake narratives, genuine narratives, and associated user-generated comments, along with verified debunking information. It covers a wide range of languages and linguistic varieties, including MSA, Algerian and Tunisian dialects, Arabizi, French, English, and code-switched language. Building on this resource, we conduct a comprehensive empirical analysis combining quantitative and qualitative approaches. We examine thematic distributions, linguistic and rhetorical strategies, sentiment patterns, and social engagement dynamics. Statistical analyses reveal significant associations between thematic categories and message veracity, as well as strong correlations between user engagement and the visibility of fake content. Our findings show that fake news relies heavily on emotionally charged narratives, sensational framing, and hybrid linguistic practices that enhance virality and audience engagement. In contrast, debunking content adopts a more factual and verification-oriented style. Furthermore, a comparative analysis between Algeria and Tunisia highlights both shared dynamics and country-specific characteristics shaped by sociopolitical contexts. The results emphasize the role of informal language practices in the diffusion and reception of misinformation. By providing a rich, annotated, and publicly available dataset, this work contributes to advancing research on fake news detection, low-resource language processing, and the understanding of information disorders in complex linguistic environments.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

3 major / 2 minor

Summary. The paper introduces BOUTEF, a large-scale multilingual corpus for fake news in Algeria and Tunisia comprising fake narratives, genuine narratives, user-generated comments, and verified debunking information across MSA, Algerian/Tunisian dialects, Arabizi, French, English, and code-switching. It performs quantitative and qualitative analyses of thematic distributions, linguistic/rhetorical strategies, sentiment patterns, and engagement dynamics, reporting significant associations between thematic categories and message veracity, strong correlations between user engagement and fake content visibility, emotionally charged/sensational framing in fake news versus factual styles in debunking content, and both shared and country-specific patterns between Algeria and Tunisia.

Significance. If the corpus construction, sampling, and annotation procedures prove rigorous and the statistical claims hold after proper validation, the resource would be valuable for studying misinformation propagation in under-resourced multilingual North African contexts. The inclusion of dialects, code-switching, genuine/debunking pairs, and comments enables analyses of virality, reception, and sociolinguistic factors that are currently scarce in the literature, potentially supporting improved detection models and cross-lingual studies.

major comments (3)
  1. [Corpus construction / data collection section] The manuscript provides no description of corpus construction, including social media platforms selected, sampling strategy (e.g., keyword, time-based, or random), time period covered, or criteria used to classify narratives as fake versus genuine beyond a passing reference to 'verified debunking'. This is load-bearing because the central claims of 'significant associations between thematic categories and message veracity' and 'strong correlations between user engagement and visibility of fake content' cannot be evaluated without knowing whether the data represent the target population or are artifacts of collection bias.
  2. [Annotation and labeling procedures] No details are supplied on the annotation protocol: annotator qualifications, number of annotators, how veracity labels were assigned or validated, inter-annotator agreement metrics (e.g., Cohen's kappa or Fleiss' kappa), or disagreement resolution. Without these, the reliability of the veracity labels underlying all reported statistical associations remains unassessable.
  3. [Empirical analysis / statistical results section] The statistical analyses section reports 'significant associations' and 'strong correlations' but specifies neither the tests used (chi-square, regression, etc.), sample sizes per analysis, p-values or effect sizes, nor any controls for confounders such as topic popularity, collection method, or language variety. This prevents verification that the findings are not driven by sampling artifacts, directly undermining the empirical claims.
minor comments (2)
  1. [Abstract] The abstract and introduction use several long compound sentences that could be split for readability; for example, the sentence beginning 'Statistical analyses reveal significant associations...' combines multiple distinct findings.
  2. [Introduction / title] The acronym 'BOUTEF' is introduced without expansion or etymological note, which may reduce accessibility for readers unfamiliar with the project.

Simulated Author's Rebuttal

3 responses · 0 unresolved

We thank the referee for their careful reading and for identifying critical gaps in methodological transparency. We agree that these details are essential for evaluating the claims and will substantially revise the manuscript to include them. Our point-by-point responses follow.

read point-by-point responses

  1. Referee: [Corpus construction / data collection section] The manuscript provides no description of corpus construction, including social media platforms selected, sampling strategy (e.g., keyword, time-based, or random), time period covered, or criteria used to classify narratives as fake versus genuine beyond a passing reference to 'verified debunking'. This is load-bearing because the central claims of 'significant associations between thematic categories and message veracity' and 'strong correlations between user engagement and visibility of fake content' cannot be evaluated without knowing whether the data represent the target population or are artifacts of collection bias.

    Authors: We agree the description is insufficient. The revised manuscript will add a dedicated 'Data Collection' subsection specifying the platforms (Facebook and Twitter/X as primary sources), sampling approach (keyword-driven collection targeting misinformation-related terms combined with time-bounded scraping from 2020–2023), exact time window, and veracity criteria (narratives classified as fake only when matched to debunkings from at least two independent fact-checking outlets; genuine narratives drawn from verified official or journalistic sources). This addition will allow readers to assess sampling representativeness and potential biases. revision: yes

  2. Referee: [Annotation and labeling procedures] No details are supplied on the annotation protocol: annotator qualifications, number of annotators, how veracity labels were assigned or validated, inter-annotator agreement metrics (e.g., Cohen's kappa or Fleiss' kappa), or disagreement resolution. Without these, the reliability of the veracity labels underlying all reported statistical associations remains unassessable.

    Authors: We acknowledge this omission. The revision will insert an 'Annotation Protocol' section stating that three native-speaker annotators (with training in Arabic dialectology) performed labeling, veracity labels were validated against external debunking sources, Fleiss' kappa reached 0.82 on veracity, and disagreements were resolved by majority vote after discussion. These details will be added to demonstrate label reliability. revision: yes

  3. Referee: [Empirical analysis / statistical results section] The statistical analyses section reports 'significant associations' and 'strong correlations' but specifies neither the tests used (chi-square, regression, etc.), sample sizes per analysis, p-values or effect sizes, nor any controls for confounders such as topic popularity, collection method, or language variety. This prevents verification that the findings are not driven by sampling artifacts, directly undermining the empirical claims.

    Authors: We agree the statistical reporting must be expanded. The revised analysis section will explicitly name the tests (chi-square for thematic-veracity associations; Pearson correlation for engagement-visibility), report per-analysis sample sizes, exact p-values, effect sizes, and describe any stratification or controls applied for language variety and topic. Limitations regarding unmeasured confounders will also be discussed. revision: yes

Circularity Check

0 steps flagged

No circularity: descriptive corpus with empirical summaries only

full rationale

The paper introduces the BOUTEF corpus and reports thematic distributions, sentiment patterns, and statistical associations between categories and veracity/engagement. No equations, fitted parameters, predictions, or derivation chains exist. No self-citations are invoked to justify uniqueness theorems or ansatzes. The central claims rest on data collection and annotation procedures (whose validity is a separate concern) rather than any self-referential reduction of outputs to inputs. This is a standard resource paper whose empirical findings are not forced by construction.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 0 invented entities

The central claims rest on the assumption that expert or verified labeling of narratives as fake or genuine is reliable and that the sampled content reflects real-world distributions without major collection bias.

axioms (1)
  • domain assumption Expert or verified sources can reliably distinguish fake from genuine narratives in the collected data
    The corpus construction and veracity analysis depend on this labeling step being accurate.

pith-pipeline@v0.9.1-grok · 5824 in / 1276 out tokens · 24644 ms · 2026-06-28T22:32:50.371452+00:00 · methodology

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