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Digital diglossia: Arabic between X and Facebook

Fahad Al Hussen, Mohammed Q. Shormani

arXiv:2609.28352v1cs.CL

TL;DR

The study asks how Standard and Colloquial Arabic are distributed across X and Facebook and discourse domains. It analyzes a classified corpus, finding platform-specific variety patterns and domain associations rather than one uniform distribution.

  • Problem

    Comparative evidence on Arabic diglossia across digital platforms is limited, leaving open how SA and CA are distributed across platforms and discourse domains.

    Source: §1 · p. 2

  • Method

    The study classifies Arabic posts from X and Facebook by variety and discourse category, then tests associations using bivariate analyses and platform-by-category logistic regression.

    Source: §1 · p. 3, §3.4 · p. 11

  • Results

    X had 60.7% SA, while Facebook had 55.4% CA; SA accounted for 52.6% overall, and variety distributions differed across discourse domains.

    Source: §6 · pp. 25–26

  • Takeaways & Limitations

    Digital media expand spaces for written CA while SA remains important across platforms and discourse domains.

    Source: §6 · p. 26

    CA is treated as a broad category rather than regionally specified dialects, limiting the findings’ account of dialect-specific patterns.

    Source: §5.2 · p. 24

Abstract

from arXiv · show

This study highlights the distribution of Standard Arabic (SA; H(igh) variety) and Colloquial Arabic (CA; L(ow) variety) across X and Facebook. 16754 public posts were collected via Python, with 10000 retained as the net dataset. Posts were classified into 7 discourse categories: *politics, technology, science, business, culture, fun,* and *sports*. Bivariate analyses, including Chi-square tests and Cramer's V (CV), examined associations among platform, discourse category, and diglossic choice, while binary logistic regression with Platform x Discourse Category interactions tested whether these associations varied across platforms. Findings reveal that there are significant associations between discourse category and diglossic choice on X, chi-square(6, *N* = 5000) = 600.35, p < .001, CV = .347, and Facebook, chi-square(6, N = 5000) = 1249.52, p < .001, CV = .500. Across platforms, platform was also associated with diglossic choice, chi-square(1, N = 10000) = 262.16, p < .001, CV = .162. Binary logistic regression further shows higher odds of SA use on X than Facebook in the political reference category (*OR* = 1.31, p = .0028), with significant platform-by-domain interactions for Culture (OR = 2.65), Fun (*OR* = 6.34), Sports (*OR* = 26.71), Science (OR = 0.41), and Technology (OR = 0.71). The study concludes that the diglossic use of SA and CA contributes to the growing body of research on digital discourse, unveiling that the digital age reshapes but does not erode diglossic boundaries, giving rise instead to a reconfigured digital diglossia.

1. Introduction

The study asks how digital communication affects Arabic diglossia and whether SA and CA vary across platforms and discourse domains. It compares X and Facebook using a corpus-based analysis.

1. Introduction

  • Digital communication raises questions about how Arabic diglossia changes, what forms digital discourse takes, and how it can be studied.The authors frame their study as addressing questions about digital diglossia and corpus-based analysis.Source: §1 · p. 2
  • Arabic diglossia involves Standard Arabic as the formal variety and colloquial dialects as varieties used in everyday communication.The passage describes SA as an official, formal variety and CA as everyday usage across regional dialects.Source: §1 · p. 2
  • Cross-platform comparisons of Arabic digital discourse remain rare, leaving open how platform-specific constraints relate to language choice.Prior work often focused on one platform or broad language-choice issues; X brevity and Facebook narrative affordances may shape discourse differently.Source: §1 · p. 2
  • The study compares SA and CA across X and Facebook and seven discourse categories, using corpus analysis to investigate platform and domain patterns.Posts were classified using orthography, lexicon, morphology, syntax, and discourse markers.Source: §1 · p. 3

2. Theoretical framework and literature review

The framework treats Arabic digital diglossia as the use of SA and CA in online contexts shaped by platform and communicative norms. It motivates a corpus-based, multi-level analysis while situating the study amid limited comparative research.

2.1. Arabic digital diglossia

  • Arabic digital diglossia concerns strategic use of SA and CA online, where platform affordances may shape language practices; cross-platform comparison remains scarce.The framework links formal and everyday functions to SA and CA, respectively, and describes X as oriented toward brevity and Facebook toward longer discourse.Source: §2.1 · pp. 4–5

2.5. Literature review

  • Arabic social-media research spans corpus resources, pragmatic studies, status updates, and activism, while comparative work on diglossia across platforms remains limited.Examples include COVID-era Facebook discourse, Facebook status acts, Arabic dialect corpora, and Palestine-related Twitter activism.Source: §2.1 · p. 5, §2.5 · pp. 6–8

2.2. Corpus linguistics

  • Corpus linguistics offers systematic analysis of real-world language patterns, but Arabic digital corpus studies have not focused on diglossia.The literature identifies orthographic variation, script mixing, dialect diversity, and thematic discourse as challenges in Arabic digital corpora.Source: §2.2 · p. 5

2.4. Digital discourse analysis

  • Digital discourse analysis adapts discourse methods to digital texts, whose interactivity and heteroglossia may differ from traditional texts.The literature also describes combining digital methods with close reading and text mining.Source: §2.4 · p. 6

2.3. Social media scraping

  • Prior work describes Python tools for collecting Facebook and X data, including crawlers and text-analysis libraries used to structure or examine platform content.This study situates its scraping approach alongside methods for public Facebook data and X API extraction.Source: §2.3 · pp. 5–6, §2.5 · p. 8

3. Methodology

The study builds a balanced corpus from public X and Facebook posts, classifies posts by Arabic variety and discourse theme, and combines quantitative analysis with a proposed digital discourse framework.

3.1 Abiding by SMPs’ terms of use

  • The corpus uses public pages on X and Facebook, excluding personal pages to support privacy and comparable topic coverage.Each platform contributed 5,000 posts after irrelevant data were excluded.Source: §3.1 · p. 8

3.2 Data collection, tools and sources

  • Python tools collected public X and Facebook posts, with the X extraction using Tweepy through the X API.Only post text was collected; engagement metrics were excluded.Source: §3.2 · p. 9, Figure 1 · p. 9

3.3. Data sampling and refinement

  • 5,000 posts per platform were retained from 16,754 collected entries after removing duplicates, non-Arabic, irrelevant, and other invalid posts.The final sample was balanced; hard or ambiguous linguistic cases were among the main exclusions.Source: §3.3 · pp. 9–10

3.4. Data annotation and classification

  • Posts were manually classified as SA or CA using orthography, lexicon, morphology, syntax, and discourse markers, with mixed posts assigned by dominant features.Two coders independently annotated a subset, and agreement was κ = 0.79.Source: §3.4 · p. 11

3.5. Methods of Analysis

  • The proposed digital discourse analysis combines micro-linguistic, macro-sociocultural, and digital-platform levels with quantitative and qualitative analysis.Bivariate tests and logistic regression with platform-by-category interactions assessed whether domain associations varied across platforms.Source: §3.5 · pp. 11–12

4. Results

Diglossic choice varies by discourse category on both platforms, and the category pattern differs across X and Facebook. Overall, X has higher SA use, while the category-specific associations are strongest on Facebook.

4. Results

  • X had more SA than CA (60.7% vs 39.3%), while Facebook had more CA than SA (55.4% vs 44.6%); overall, SA was 52.6% of posts.Politics favored SA on both platforms: 75% on X and 70% on Facebook.Source: Table 2 · p. 12, Table 3 · p. 13, §4 · p. 13
  • Culture favored CA on Facebook (68%) but SA on X (63%); technology and science favored SA on both platforms, especially Facebook science (71%).On X, SA shares were 67% for technology and 57% for science; on Facebook, technology was 58% SA.Source: §4 · pp. 13–14
  • Facebook fun was 98% CA and sports 87% CA, whereas X fun was 86% CA and sports 60% SA; business favored CA on Facebook but was slightly CA-majority on X.Business was 64% CA on Facebook and 53% CA on X.Source: §4 · p. 14
  • χ²(6, n = 10000) = 1883.03, p < .001, CV = .434, for the pooled association between discourse domain and diglossic choice; platform was also associated with choice (χ²(1, n = 10000) = 262.16, p < .001, CV = .162).Within-platform associations were χ²(6, n = 5000) = 600.35, p < .001, CV = .347 on X and χ²(6, n = 5000) = 1249.52, p < .001, CV = .500 on Facebook.Source: §4 · p. 15
  • With Facebook and Politics as references, X had higher odds of SA use in Politics (OR = 1.3090, p = .0028), and platform-by-category interactions were significant for Culture, Fun, Sports, Science, and Technology.Interaction ORs were 2.6533 for Culture, 6.3351 for Fun, 26.7091 for Sports, 0.4088 for Science, and 0.7113 for Technology; Business was not significant.Source: Table 5 · p. 15, §4 · p. 16

5. Discussion

The section introduces a qualitative analysis of corpus examples across seven discourse categories. Table 6 presents examples for both Arabic varieties on both platforms.

5. Discussion

  • The qualitative analysis presents corpus examples representing the seven discourse categories.Examples are organized in Table 6.Source: §5 · p. 16, Table 6 · p. 16
  • The examples are intended to illustrate the diglossic use of Arabic varieties across the two platforms.Source: §5 · p. 16
  • Table 6 presents examples of digital diglossia in Arabic between X and Facebook.Source: Table 6 · p. 16

CA SA CA

The section examines how Arabic SA/CA diglossic choices across X and Facebook vary by discourse domain and relate to linguistic features, discourse functions, and platform affordances. It considers how digital practices reshape, without erasing, traditional SA and CA functions.

CA SA CA

  • The excerpted posts include technology news, sports reporting, and an affectionate personal narrative.Examples include a Samsung Galaxy AI phone, Egypt’s Olympic football victory, and a post about being someone’s source of joy.Source: SA CA SA CA · pp. 17–18

5.1. Thematic analysis

  • X concentrates political and informational themes in SA, while Facebook has broader cultural, personal, and entertainment content, mostly in CA.Across both platforms, politics is strongly associated with SA; culture can also blend SA and CA according to communicative intent.Source: §5.1 · pp. 19–20

5.2. Digital analysis

  • The digital analysis distinguishes formal SA structures and orthography from CA dialectal vocabulary, simplified syntax, and non-standard spelling, linking variation to platform affordances and communicative practices.It examines micro-level linguistic features, macro-level discourse functions, and digital-level platform affordances; repetition is described as largely CA-oriented and rarely found in SA posts.Source: §5.2 · p. 22
  • SA functions as a pan-Arab resource for authority, institutional discourse, collective identity, and nationalism, while CA expresses locally shared concerns and optimism.The passage also identifies SA as a medium of instruction and CA as a within-country device for community concerns.Source: §5.2 · pp. 22–23
  • X’s brevity supports broadcast-oriented SA, while abbreviations, letter repetition, spacing, and emojis provide digital resources for informality, engagement, emphasis, and emotion.Emojis can compensate for absent facial expressions, gestures, or vocal tone and can intensify or contradict verbal meaning.Source: §5.2 · p. 23
  • χ² = 1883.03 (p < .001, CV = .434) shows domain and language choice are associated, with stronger dependence on Facebook (CV = .500) than X (CV = .347).The platform main effect was χ² = 262.16 (p < .001, CV = .162), with X showing higher baseline SA use (OR = 1.3090, p = .0028).Source: §5.2 · pp. 23–24
  • Culture (OR = 2.6533), Fun (OR = 6.3351), and Sports (OR = 26.7091) interactions favor SA on X, while Science (OR = 0.4088) and Technology (OR = 0.7113) interactions disfavor it.All listed interactions were significant; the authors interpret them as evidence that platform affordances recalibrate domain-specific language norms.Source: §5.2 · p. 24
  • The authors characterize digital diglossia as combining structural and functional factors with platform affordances, preserving traditional communication while accommodating new digital features and styles.They also report CA’s flexibility and broad reach, while SA remains dominant on both platforms because of its formality.Source: §5.2 · p. 24
  • The study’s limitations include broad, non-region-specific CA coding, data limited to X and Facebook, and no systematic comparison by genre, country, or dialect region.The authors call for broader platform coverage and finer-grained analysis across genres, contexts, and communities.Source: §5.2 · pp. 24–25

6. Conclusions and implications

SA and CA show different distributions across X and Facebook, and the platform–discourse relationship varies by domain. Overall proportions are close, while SA and CA remain associated with distinct communicative domains.

6. Conclusions and implications

  • X had 60.7% SA and 39.3% CA, whereas Facebook had 44.6% SA and 55.4% CA.Across both platforms, SA accounted for 52.6% of posts and CA for 47.4%.Source: §6 · p. 25
  • Platform-by-domain interactions were significant for Culture (OR = 2.6533), Fun (OR = 6.3351), Sports (OR = 26.7091), Science (OR = 0.4088), and Technology (OR = 0.7113).The Business interaction was not significant (OR = 1.1922, p = .2412).Source: §6 · p. 25
  • On Facebook, SA was most frequent in Science, Technology, and Politics, while CA predominated in Culture, Business, Fun, and Sports; X showed a different distribution.On X, SA was most prominent in Politics, Technology, Culture, Sports, and Science, while CA was more frequent in Business and Fun.Source: §6 · p. 25
  • Differences in communicative context may partly reflect these patterns, but the statistical analysis does not establish those contextual explanations as causal effects.The authors suggest X may offer greater visibility for public or professional discourse, while Facebook may accommodate more personal interaction.Source: §6 · p. 25
  • The near-balanced overall proportions mean neither variety overwhelmingly dominates digital written Arabic across the two platforms.Platform-specific distributions nevertheless show SA strongly represented in politics, science, and technology, and CA especially prevalent in conversationally oriented domains on Facebook.Source: §6 · p. 26
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