A Computational-Augmented Critical Discourse Analysis of Tweets on the Saudi General Entertainment Authority Activities
Bibliographic record
Abstract
This study used both computational tools in the form of a machine learning predictive model (Support Vector Machine) and a critical discourse analysis model (Van Dijk’s ideological square model) (Van Dijk, 1993, 2008, 2009) to fulfill three objectives: (1) clustering the Saudis’ Twitter-based opinions and sentiments regarding the entertaining and recreational activities run by the Saudi General Entertainment Authority (GEA); (2) offering empirical evidence on how computational linguistic methods could be implemented for offering a reliable conceptual framing of such opinionated big data; and (3) outlining the central themes generating ideologically motivated polarity in Saudi public opinion and the macrostrategies through which this polarity is textually instantiated and actualized. Toward fulfilling these objectives, we designed a purpose-built corpus of 9378 tweets based on five trending hashtags, covering the period between 2020 and 2022. Findings affirmed the efficacy of synergizing the Support Vector Machine model and the ideological square model in clustering and interpreting the target tweets. Based on the output discourse features and thematization of the tweets, two main groups with different ideologically motivated perspectives were identified. This ideological polarity was achieved through the use of two macrostrategies: positive self-presentation and negative other-presentation. These findings may prompt policymakers to reconsider current (mis)practices in order to achieve long-term sustainable development goals.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".