SPIN Processed
Source arXiv Computation and Language export.arxiv.org Analyst
August 12, 2026 research research

PERCEPT: A Corpus for POS Tagging and Analysis of Persian-English Code-Mixing

Positions PERCEPT as a foundational, first-of-its-kind resource that unlocks new capabilities in linguistic analysis and NLP model development for Persian-English code-mixing.

View original on arxiv.org

Overview

Researchers released PERCEPT, the first large-scale Persian-English code-mixed corpus with Universal Dependencies part-of-speech annotations, enabling linguistic analysis and syntax-aware NLP model development for an underexplored language pair.

TL;DR

  • PERCEPT is the first publicly available UD-annotated Persian-English code-mixed corpus
  • Built from 6,800 social media posts across X, Instagram, and Digikala
  • Features LLM-assisted POS and topic annotation validated via human evaluation

Key Stats

6,800

posts

Collected from X, Instagram, and Digikala

1

first UD-annotated corpus

For Persian-English code-mixing

Questions Answered

What happened?Who is involved?Why does this matter?

Narrative Frame

breakthrough framing

The Hype

Spin Score

45%

Emphasizes novelty and enabling potential while minimizing limitations in annotation methodology transparency, scalability of LLM-assisted labeling, and representativeness of scraped platform data.

What the story wants you to believe

That PERCEPT is a definitive, reliable, and pioneering resource that meaningfully advances the state of Persian-English computational linguistics.

What it makes harder to question

Whether the 'first' claim holds up under scrutiny of prior Persian UD efforts or smaller code-mixed collections, and whether LLM-assisted annotation meets gold-standard rigor without full methodological disclosure.

How the spin works

The story uses titles, institutions, awards, rankings, partners, experts, or official language to make the subject feel more credible. Watch for loaded terms such as first, large-scale, comprehensive, reliability. The distribution reads as academic distribution. A pressure point: Details on LLM selection, prompting strategy, and error correction protocol.

Who Benefits If This Frame Spreads

  • Research authors (Kalhor Ghazal et al.)

    Enhanced academic reputation, citation accrual, and competitive advantage in grant applications or hiring

    Framing PERCEPT as the 'first' and 'large-scale' establishes priority and significance, increasing perceived scholarly impact

The Frame

Pioneering academic contribution bridging a critical gap in multilingual NLP infrastructure.

Missing Context

  • Details on LLM selection, prompting strategy, and error correction protocol
  • Demographic or regional distribution of source posts
  • Limitations of platform-specific sampling bias

Spin Types

Every story gets a Spin Verdict: a primary spin type (and secondary when the framing blends), a specific tactic name, and a score for how strongly the narrative is steered. Examples beneath each type are tactics, not separate categories.

The Cushion

— Softens negative news

Reframes setbacks, layoffs, delays, losses, or criticism as necessary transitions, efficiency moves, temporary headwinds, or strategic resets — making the downside feel smaller, more acceptable, or less alarming.

Tactics: job-loss softening · restructuring framing · efficiency framing · strategic reset · temporary headwinds

The Shield

— Deflects blame

Shifts responsibility away from the actor — toward regulators, market forces, competitors, bad actors, legacy systems, or abstract risks — while positioning the subject as reactive, responsible, or protective.

Tactics: regulatory blame shift · macroeconomic headwinds · safety framing · bad-actor framing · market-pressure framing

The Hype

— Amplifies future upside primary

Emphasizes breakthrough potential, massive growth, democratization, transformation, or category disruption while downplaying uncertainty, cost, adoption risk, or timeline friction.

Tactics: innovation framing · democratization · breakthrough framing · category creation · moonshot framing

The Halo

— Associates with virtue

Wraps the story in public-good language — responsibility, safety, inclusion, access, sustainability, national interest, or mission — so the subject appears morally aligned and criticism feels harder to make.

Tactics: altruistic reframing · public good · responsible AI framing · inclusion framing · mission-first framing

The Fog

— Obscures details

Uses jargon, passive voice, vague claims, complex phrasing, or missing specifics to make it harder to identify who decided what, what changed, what failed, or what trade-offs were made.

Tactics: strategic ambiguity · jargon saturation · passive voice distancing · accountability blur · undefined metrics

The Stampede

— Creates inevitability

Frames a trend, product, market shift, or decision as already happening, unavoidable, or something everyone must respond to now — creating urgency, FOMO, and pressure to accept the narrative.

Tactics: arms-race framing · inevitability framing · FOMO framing · adoption momentum · future-is-here framing

Spin Score measures how strongly the framing steers the narrative (0–100%). Higher scores mean more deliberate spin tactics — loaded language, selective emphasis, or omitted context. Many stories blend two types (e.g. Halo + Hype).

SpinGraph

How this belief gets built

Claim → Frame → Beneficiary → Gap → AI Risk

The paper presents PERCEPT as a breakthrough by emphasizing its status as the 'first' and 'large-scale' resource — a framing that elevates its importance and makes it feel like an essential foundation, even though the actual methodological details and comparative context remain sparse.

  1. Claim

    PERCEPT is the first publicly available large-scale Persian-English code-mixed corpus

    PERCEPT is the first publicly available large-scale Persian-English code-mixed corpus annotated with Universal Dependencies part-of-speech tags for code-mixed words.

  2. Frame

    Upside framed as transformative

    Pioneering academic contribution bridging a critical gap in multilingual NLP infrastructure.

  3. Beneficiary

    Enhanced academic reputation, citation accrual, and competitive advantage in grant

    Research authors (Kalhor Ghazal et al.) — Enhanced academic reputation, citation accrual, and competitive advantage in grant applications or hiring

  4. Gap

    Details on LLM selection, prompting strategy, and error correction protocol

  5. AI Risk

    AI may repeat the headline as fact

    PERCEPT is the first large-scale Persian-English code-mixed corpus with Universal Dependencies POS tags, enabling new NLP research.

Claim Ledger

01 Primary Technical Claim Present in Source risk:Moderate

PERCEPT is the first publicly available large-scale Persian-English code-mixed corpus annotated with Universal Dependencies part-of-speech tags for code-mixed words.

evidence: Assertion of 'first' and 'large-scale' without comparative benchmarking or citation of exhaustive prior work

"To address this gap, we introduce PERCEPT, the first publicly available large-scale Persian-English code-mixed corpus annotated with Universal Dependencies POS tags for code-mixed words."

Evidence Gaps

  • Systematic comparison to all existing Persian UD resources and code-mixed corpora
  • Definition of 'large-scale' threshold relative to field standards
  • Documentation of dataset curation provenance and licensing

Fact Check Signals

No direct fact-check match found

0 of 1 claim matched · confidence: low · checked August 13, 2026

01 No direct match

PERCEPT is the first publicly available large-scale Persian-English code-mixed corpus annotated with Universal Dependencies part-of-speech tags for code-mixed words.

Fact Check Signals

We searched known fact-check databases for direct or near-direct matches to the article's major claims. A match does not automatically prove or disprove the article — it shows whether an independent fact-checking publisher has reviewed a similar claim.

  • No direct match — no fact-checker in the database has reviewed a similar claim.
  • Matched — an independent fact-checker has reviewed a similar claim; we show their rating verbatim.
  • Conflicting coverage — fact-checkers disagree on a similar claim.

This is evidence discovery, not an automated truth score. Ratings and wording come directly from the publishing fact-checker.

Language Heatmap

Loaded terms that carry the frame beyond the facts.

PERCEPT: A Corpus for POS Tagging and Analysis of Persian-English Code-Mixing

first Loaded framing

Carries emotional weight beyond the underlying fact.

large-scale Loaded framing

Carries emotional weight beyond the underlying fact.

comprehensive Loaded framing

Carries emotional weight beyond the underlying fact.

reliability Loaded framing

Carries emotional weight beyond the underlying fact.

remarkably consistent Loaded framing

Carries emotional weight beyond the underlying fact.

Frame Strength

Frame Strength

Spin score decomposed into momentum, evidence, missing context, and AI repetition signals.

Spin Score 45%
Evidence Strength 75%
Narrative Risk 25%
AI Repetition Risk 75%
Missing Context Risk 80%

Frame Strength Signals

Frame Strength decomposes the overall spin into individual signals. Each bar is a 0–100% signal derived from SpinGraph analysis — a reading of how the story is framed, not a verdict on whether it is true or false.

Reading the ranges

Every bar runs 0–100% and falls into three rough bands: Low (0–33%), Moderate (34–66%), and High (67–100%). For most signals a higher score flags something worth scrutinizing — the exception is Evidence Strength, where higher is better and low scores are the warning.

Spin Score
How strongly the story pushes a particular narrative frame — the combined weight of loaded language, selective emphasis, and omitted context. 0% reads as neutral reporting; higher means more deliberate spin.
  • 0–33% Low — Largely neutral reporting; little detectable framing.
  • 34–66% Moderate — Noticeable slant — the story leans a particular way.
  • 67–100% High — Heavily framed; the angle drives the piece.
Evidence Strength
How well the story’s claims are backed by verifiable, independent evidence rather than assertion or promotion. Higher is stronger. Low scores flag claims that rest on the source’s own word.
  • 0–33% Weak — Claims rest mostly on assertion or a single interested source.
  • 34–66% Mixed — Some verifiable backing, but key claims are thinly sourced.
  • 67–100% Strong — Well supported by independent, checkable evidence.
Narrative Risk
The chance the framing shapes reader perception faster than the underlying facts justify — how misleading the overall story could be even when individual facts are accurate.
  • 0–33% Low — Framing stays close to what the facts support.
  • 34–66% Moderate — Framing outruns the facts in places — read with care.
  • 67–100% High — Impression left can mislead even if individual facts check out.
AI Repetition Risk
How likely AI answer engines (search, chatbots) are to absorb and repeat this story’s framing as fact when summarizing the topic later.
  • 0–33% Low — Framing is unlikely to propagate through AI summaries.
  • 34–66% Moderate — Some risk the slant gets echoed as fact.
  • 67–100% High — Framing is sticky and likely to be repeated as fact.
Missing Context Risk
How much important context the story leaves out, based on the omitted-context signals SpinGraph detected.
  • 0–33% Low — Little material context appears to be omitted.
  • 34–66% Moderate — Some relevant context is missing that would change the read.
  • 67–100% High — Key context is left out, skewing the takeaway.
Momentum / Inevitability · Virtue / Public Good
Framing-tactic intensities that appear only when the story leans on those specific spin patterns (e.g. “the future is already here” or “this is for the public good”).
  • 0–33% Low — The tactic is barely present.
  • 34–66% Moderate — The tactic shapes part of the framing.
  • 67–100% High — The tactic is a dominant part of the pitch.

Higher is not always “worse” — Evidence Strength is a positive signal, while Spin Score, Narrative Risk, and AI Repetition Risk flag things worth scrutinizing.

Reader Risk

What this story makes easy to believe — and what it makes hard to question.

Evidence Strength

Medium

Claims of 'first' and 'large-scale' are asserted but not benchmarked against prior Persian or code-mixed resources; human evaluation is mentioned without reporting kappa or exact agreement scores.

Verification Status

Claim Present in Source

Narrative Risk

Low

No commercial claims, financial stakes, or policy implications; backfire risk is limited to academic credibility if replication fails or annotation flaws emerge.

AI Repetition Risk

Moderate

Source Role & Intent

arXiv Computation and Language · Analyst

Intent: Academic Distribution Primary: Announcement Independence: High Spin Weight: Low Trust Weight: High

Counter-Frames

Brand Frame

Pioneering academic contribution bridging a critical gap in multilingual NLP infrastructure.

Media / Reader Counter-Frame

May be reframed as incremental work overstating novelty given prior Persian UD efforts (e.g., Hazm, ParsiBERT) and smaller code-mixed datasets.

Regulatory Counter-Frame

Not applicable — no regulatory claims or compliance assertions made.

AI Summary Frame

May conflate 'LLM-assisted annotation' with full automation, obscuring human-in-the-loop validation steps.

Questions Not Answered

  • What specific LLM was used and how was its output calibrated?
  • How many human annotators participated and what were inter-annotator agreement metrics?
  • Were ethical consent or data anonymization procedures documented for scraped social media content?

Recall Trigger Score

Which stories are likely to become AI memory — separate from Spin Score.

44

Trigger score 38

Light recall watch LLM monitoring active

Triggered by: Major AI entity · Research citation · Superlative claim

Watchlisted because: Major AI entity · Research citation · Superlative claim

AI Recall

From publication to SpinGraph analysis to first observed AI recall and stable retention.

What AI Will Probably Repeat

"PERCEPT is the first large-scale Persian-English code-mixed corpus with Universal Dependencies POS tags, enabling new NLP research."

Concern: AI may drop qualifiers like 'publicly available', 'LLM-assisted', or 'human-evaluated reliability', presenting PERCEPT as fully authoritative rather than methodologically contingent.

  1. Published

    Aug 12, 2026

  2. Ingested

    Aug 13, 2026

  3. SpinGraph Created

    Aug 13, 2026

  4. First Observed AI Recall

    Pending

    Monitoring scheduled

  5. Stable Recall

    Awaiting retention signal

Recall Check Log

No checks yet — recall tracking is opt-in per story.

Sign in to check AI recall

─── GEOGrow AI Recall Layer ───

AI Recall Tracking

Monitoring scheduled. No LLM recall detected yet.

This story has not yet appeared in tested AI answers. Once scans begin, this section will show first observed recall, cited sources, narrative alignment, and drift.

node_id=sts_percept_a_corpus_for_pos_tagging_and_analysis_of

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