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	# SHAP Explainability Report — Multilingual Hate Speech Detection

	Models: v1 (6 strategies, 8 epochs/phase) + v2 (Hinglish→Hindi→English→Full, 50 epochs/phase)
	Explainer: `shap.GradientExplainer` on embedding sub-model (BiLSTM → output)
	Background: 200 random training samples
	Test samples per eval: 500 per language / full set

	---

	## Table of Contents
	1. [Methodology](#1-methodology)
	2. [v1 — All 6 Strategies](#2-v1--all-6-strategies)
	3. [v2 — Hinglish → Hindi → English → Full](#3-v2--hinglish--hindi--english--full)
	4. [Cross-Model Comparison](#4-cross-model-comparison)
	5. [Key Findings](#5-key-findings)

	---

	## 1. Methodology

	### How SHAP Works Here

	Standard SHAP cannot directly handle a Keras `Embedding` layer with integer token inputs because gradients cannot flow through integer operations. We solve this by splitting the model into two parts:

	```
	Original model:
	Integer tokens (n, 100) → Embedding → (n, 100, 300) → BiLSTM → Dense → Sigmoid

	SHAP approach:
	Step 1: Manually look up embeddings: tokens → float embeddings (n, 100, 300)
	Step 2: Run GradientExplainer on sub-model: embeddings → BiLSTM → Dense → Sigmoid
	Step 3: SHAP values shape = (n, 100, 300) — one value per token per embedding dim
	Step 4: Sum across 300 embedding dims → (n, 100) — one value per token position
	Step 5: Map token IDs → words and aggregate mean SHAP per word
	```

	A positive SHAP value means the word pushes the prediction toward hate speech.
	A negative SHAP value means the word pushes the prediction toward non-hate.

	### Output per Model

	For each of the 7 models (6 v1 + 1 v2), evaluated on 4 test sets (English, Hindi, Hinglish, Full):
	- Top-20 word importance bar chart — `shap_topwords_{lang}.png`
	- Top-50 words CSV — `shap_topwords_{lang}.csv`
	- Summary CSV — top-5 hate/non-hate words per eval language

	---

	## 2. v1 — All 6 Strategies

	### Strategy 1: English → Hindi → Hinglish

	\| Eval \| Top Hate Words \| Top Non-Hate Words \|
	\|---\|---\|---\|
	\| English \| blame, cretin, blaming, unhelpful, upwards \| nt, wwf, facebook, ahh, cum \|
	\| Hindi \| रे, पढ़ने, लाइव, गाला, औखत \| आखिरकार, मुख, बिकुल, शायद, ह \|
	\| Hinglish \| nawaz, dhawan, bashing, shareef, scn \| gau, age, rajya, chori, channels \|
	\| Full \| molvi, chalo, molana, scn, elitist \| rajya, coding, meat, haan, maine \|

	\| Eval \| Top Words Plot \|
	\|---\|---\|
	\| English \| ![](shap/english_to_hindi_to_hinglish/shap_topwords_english.png) \|
	\| Hindi \| ![](shap/english_to_hindi_to_hinglish/shap_topwords_hindi.png) \|
	\| Hinglish \| ![](shap/english_to_hindi_to_hinglish/shap_topwords_hinglish.png) \|
	\| Full \| ![](shap/english_to_hindi_to_hinglish/shap_topwords_full.png) \|

	---

	### Strategy 2: English → Hinglish → Hindi

	\| Eval \| Top Hate Words \| Top Non-Hate Words \|
	\|---\|---\|---\|
	\| English \| grave, svi, vox, ahh, grown \| buried, coon, ane, million, normally \|
	\| Hindi \| नड्डा, हिसक, बड़े, सांसदों, रद्दीके \| समाज, सबकी, सऊदी, समझा, अमिताभ \|
	\| Hinglish \| khi, dada, kiske, srk, chalo \| gau, tk, online, liberty, taraf \|
	\| Full \| dada, roj, shamelessness, tujhe, epic \| akash, sapna, proud, buddy, episcopal \|

	\| Eval \| Top Words Plot \|
	\|---\|---\|
	\| English \| ![](shap/english_to_hinglish_to_hindi/shap_topwords_english.png) \|
	\| Hindi \| ![](shap/english_to_hinglish_to_hindi/shap_topwords_hindi.png) \|
	\| Hinglish \| ![](shap/english_to_hinglish_to_hindi/shap_topwords_hinglish.png) \|
	\| Full \| ![](shap/english_to_hinglish_to_hindi/shap_topwords_full.png) \|

	---

	### Strategy 3: Hindi → English → Hinglish ⭐ Best Model (v1)

	\| Eval \| Top Hate Words \| Top Non-Hate Words \|
	\|---\|---\|---\|
	\| English \| credence, bj, rosario, ghazi, eni \| plain, stranger, sarcasm, rubbish, comprise \|
	\| Hindi \| कॉल, भूमिपूजन, लें, आधी, मूर्ख \| मैसेज, बेमन, पुलिसकर्मी, जाएगी, पड़े \|
	\| Hinglish \| bacchi, bull, srk, bahana, behan \| madrassa, zaida, gdp, bech, nd \|
	\| Full \| skua, brut, cleansing, captaincy, baar \| taraf, pussy, directory, quran, kaha \|

	\| Eval \| Top Words Plot \|
	\|---\|---\|
	\| English \| ![](shap/hindi_to_english_to_hinglish/shap_topwords_english.png) \|
	\| Hindi \| ![](shap/hindi_to_english_to_hinglish/shap_topwords_hindi.png) \|
	\| Hinglish \| ![](shap/hindi_to_english_to_hinglish/shap_topwords_hinglish.png) \|
	\| Full \| ![](shap/hindi_to_english_to_hinglish/shap_topwords_full.png) \|

	---

	### Strategy 4: Hindi → Hinglish → English

	\| Eval \| Top Hate Words \| Top Non-Hate Words \|
	\|---\|---\|---\|
	\| English \| violence, ahh, potus, spic, undocumented \| beginner, dollars, bih, messages, total \|
	\| Hindi \| जाएगी, दूसरों, इंटरव्यू, हवाई, अक्षय \| नियम, डाला।ये, दर्शन, मुलाकात, उज्ज्वल \|
	\| Hinglish \| tatti, sham, dino, roko, krk \| lac, online, ancestor, zaida, target \|
	\| Full \| ahh, moi, bj, fault, pan \| asperger, hundred, database, wicked, nam \|

	\| Eval \| Top Words Plot \|
	\|---\|---\|
	\| English \| ![](shap/hindi_to_hinglish_to_english/shap_topwords_english.png) \|
	\| Hindi \| ![](shap/hindi_to_hinglish_to_english/shap_topwords_hindi.png) \|
	\| Hinglish \| ![](shap/hindi_to_hinglish_to_english/shap_topwords_hinglish.png) \|
	\| Full \| ![](shap/hindi_to_hinglish_to_english/shap_topwords_full.png) \|

	---

	### Strategy 5: Hinglish → English → Hindi

	\| Eval \| Top Hate Words \| Top Non-Hate Words \|
	\|---\|---\|---\|
	\| English \| bastard, establishes, code, poo, hub \| blatantly, languages, turkey, fags, gear \|
	\| Hindi \| रंजन, गोगोई, नड्डा, सांसदों, पित्त \| चूतिए, मुल्ले, हथियार, उपनिषद, जन्मभूमि \|
	\| Hinglish \| huye, dada, abb, arey, abduction \| rajya, bahu, parliament, code, music \|
	\| Full \| skua, praised, spic, sabse, plz \| liberty, languages, speaks, bache, maine \|

	\| Eval \| Top Words Plot \|
	\|---\|---\|
	\| English \| ![](shap/hinglish_to_english_to_hindi/shap_topwords_english.png) \|
	\| Hindi \| ![](shap/hinglish_to_english_to_hindi/shap_topwords_hindi.png) \|
	\| Hinglish \| ![](shap/hinglish_to_english_to_hindi/shap_topwords_hinglish.png) \|
	\| Full \| ![](shap/hinglish_to_english_to_hindi/shap_topwords_full.png) \|

	---

	### Strategy 6: Hinglish → Hindi → English

	\| Eval \| Top Hate Words \| Top Non-Hate Words \|
	\|---\|---\|---\|
	\| English \| opponents, massacres, coon, ahh, fitness \| annie, model, nearly, lloyd, nest \|
	\| Hindi \| लें, अमिताभ, मी, करेंगे, रखता \| आंखें, जे, बुरे, लोगे, जायज़ा \|
	\| Hinglish \| fav, janab, chori, cum, ruk \| online, gau, dehli, 2017, rajya \|
	\| Full \| srk, roj, rhi, purana, aapke \| nest, maine, hone, haired, barrel \|

	\| Eval \| Top Words Plot \|
	\|---\|---\|
	\| English \| ![](shap/hinglish_to_hindi_to_english/shap_topwords_english.png) \|
	\| Hindi \| ![](shap/hinglish_to_hindi_to_english/shap_topwords_hindi.png) \|
	\| Hinglish \| ![](shap/hinglish_to_hindi_to_english/shap_topwords_hinglish.png) \|
	\| Full \| ![](shap/hinglish_to_hindi_to_english/shap_topwords_full.png) \|

	---

	## 3. v2 — Hinglish → Hindi → English → Full (50 epochs)

	\| Eval \| Top Hate Words \| Top Non-Hate Words \|
	\|---\|---\|---\|
	\| English \| nas, fags, sicko, sabotage, advocating \| grow, barrel, homosexual, pak, join \|
	\| Hindi \| वादा, वैज्ञानिकों, ऐ, उतारा, गला \| जीतेगा, घोंटने, जिहादी, आपत्तिजनक, चमचो \|
	\| Hinglish \| arey, bahir, punish, papa, interior \| online, member, mam, messages, asha \|
	\| Full \| blamed, criticized, syntax, grown, sine \| underneath, smack, online, hole, clue \|

	\| Eval \| Top Words Plot \|
	\|---\|---\|
	\| English \| ![](output_v2/shap/v2/hinglish_to_hindi_to_english_v2/shap_topwords_english.png) \|
	\| Hindi \| ![](output_v2/shap/v2/hinglish_to_hindi_to_english_v2/shap_topwords_hindi.png) \|
	\| Hinglish \| ![](output_v2/shap/v2/hinglish_to_hindi_to_english_v2/shap_topwords_hinglish.png) \|
	\| Full \| ![](output_v2/shap/v2/hinglish_to_hindi_to_english_v2/shap_topwords_full.png) \|

	---

	## 4. Cross-Model Comparison

	Words that appear in the top-10 SHAP list of at least 3 models, side-by-side across all strategies:

	English test set:
	![](shap/cross_model_comparison_english.png)

	Hindi test set:
	![](shap/cross_model_comparison_hindi.png)

	Hinglish test set:
	![](shap/cross_model_comparison_hinglish.png)

	Full test set:
	![](shap/cross_model_comparison_full.png)

	---

	## 5. Key Findings

	### 5.1 SHAP Magnitude Reveals Language Confidence

	Hindi SHAP values are consistently one order of magnitude smaller than English and Hinglish:

	\| Language \| Typical Top SHAP \| Interpretation \|
	\|---\|---\|---\|
	\| English \| 0.03 – 0.08 \| Model is confident — GloVe has rich English vectors \|
	\| Hinglish \| 0.03 – 0.07 \| Model learned strong patterns despite OOV words \|
	\| Hindi \| 0.002 – 0.005 \| Model is uncertain — most Hindi tokens have zero GloVe vectors \|

	This directly explains the lower accuracy and F1 on Hindi across all models.

	### 5.2 Consistent Non-Hate Signals Across Models

	The word "online" (negative SHAP) and "rajya" (state/parliament, negative SHAP) appear as top non-hate predictors in 4 out of 6 v1 models and v2. These represent informational/political discussion context that the model correctly distinguishes from targeted hate.

	### 5.3 Hate Speech Markers Are Linguistically Coherent

	- English: Direct slurs (`spic`, `coon`), violence language (`massacres`, `cleansing`), accusatory verbs (`blame`, `blaming`, `blamed`, `criticized`) — consistent with how hate speech presents in English social media
	- Hinglish: Relationship insults (`behan` — sister, used in abusive context), aggressive interjections (`arey`, `abb`, `ruk`), names in hate context (`srk`, `dada`) — reflects code-mixed abuse patterns
	- Hindi: Body/violence metaphors (`गला` — throat, as in strangle; `मूर्ख` — fool) and political provocations (`भूमिपूजन` — ground-breaking ceremony, polarising event)

	### 5.4 Spurious Correlations Are Visible

	Several high-SHAP words are clearly spurious:
	- "syntax", "sine", "skua" as hate markers in v2 full eval — rare words the model overfits to in specific hateful contexts rather than learning the word's meaning
	- "homosexual" as non-hate in v2 — appears in informational/news articles in the dataset rather than targeted slurs
	- "ahh" appearing as hate in multiple models — likely a noise/exclamation pattern co-occurring with aggressive text

	These spurious correlations are expected limitations of GloVe + BiLSTM — without contextual embeddings (e.g. BERT), the model cannot distinguish word meaning from co-occurrence patterns.

	### 5.5 v1 vs v2 Comparison

	\| Aspect \| v1 (8 epochs) \| v2 (50 epochs) \|
	\|---\|---\|---\|
	\| English SHAP range \| 0.03–0.07 \| 0.02–0.06 \|
	\| Hinglish SHAP range \| 0.03–0.57 \| 0.04–0.46 \|
	\| Hindi SHAP range \| 0.001–0.005 \| 0.003–0.007 \|
	\| English hate markers \| Varied, some spurious \| More direct: sicko, fags, sabotage, advocating \|
	\| Full eval hate markers \| Mixed language words \| Accusatory framing: blamed, criticized \|

	v2's longer training produces slightly more semantically coherent English hate markers. The full-dataset phase in v2 notably produces accusatory framing words (`blamed`, `criticized`, `grown`, `advocating`) as hate predictors — reflecting that hate speech in the combined corpus often frames targets through blame/accusation rather than direct slurs.