Social Network Misinformation and Attitudinal Shift: A Sociolinguistic Perspective
Bibliographic record
Abstract
This paper attempts to investigate the extent to which linguistic misinformation via social networking platforms affects an attitudinal shift on the part of Saudis in terms of the social, political, and religious issues propagated by the various social networks. This study delves into the verbal and nonverbal linguistic strategies employed to influence the cognitive background of Saudis as well as their ideological beliefs in a way that targets a shift in their attitudinal behavior, socially, politically, and religiously. The paper analytically covers two linguistic dimensions of using language to influence others, either persuasively or manipulatively: the lexical level, which focuses on the lexical choices of particular words that serve to create a specific attitudinal shift in the recipients’ personalities, and the pragmatic level, which constitutes the intended meaning of speakers or writers that lies beyond the surface propositional meaning of the linguistic expression. To achieve its objective, the paper draws on two analytical strands: critical discourse analysis (CDA) and the social cognitive theory (SCT). The paper has three main findings: first, language is a rhetorical device for influencing the public’s political, social, and religious views, and, therefore, the rhetorical power of the word significantly contributes to attitudes shift; second, misinformation propagated via social networks influences the attitudinal behavior of recipients, particularly at the social level; and, third, social platforms are ideology conduits via which various meanings targeting attitudes shift are communicated.
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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 | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".