288 lines
11 KiB
Python
288 lines
11 KiB
Python
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"""OpenAI-powered toxicity classifier.
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Sends batches of posts to GPT-4.1-nano for multi-category toxicity scoring.
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Returns a list of dicts of category → score (0.0–1.0).
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"""
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from __future__ import annotations
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import asyncio
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import json
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import logging
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import random
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from dataclasses import dataclass
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from openai import AsyncOpenAI, RateLimitError, APITimeoutError, APIConnectionError
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logger = logging.getLogger("analyzer.classifier")
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# The 12 toxicity categories we score
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CATEGORIES = [
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"toxic",
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"threat",
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"hate_speech",
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"racism",
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"antisemitism",
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"islamophobia",
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"sexism",
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"homophobia",
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"insult",
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"dehumanization",
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"extremism",
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"ableism",
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]
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# System prompt for batch classification — multiple numbered posts per call
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SYSTEM_PROMPT = """\
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You are a toxicity classifier for Dutch and English social media posts about politics.
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You will receive one or more numbered posts. Score EACH post on every category from 0.0 (none) to 1.0 (extreme).
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Respond ONLY with a JSON object mapping post numbers (as strings) to their scores. No other text.
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Categories:
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- toxic: rude, disrespectful, or aggressive language
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- threat: threats of violence, harm, intimidation, or calls to action against a person
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- hate_speech: targeting people based on any protected characteristic (race, religion, gender, sexual orientation, disability, nationality)
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- racism: specifically targeting race or ethnicity (e.g. anti-Black, anti-Asian, anti-Moroccan sentiment, "Zwarte Piet" debates when derogatory)
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- antisemitism: targeting Jewish people, Holocaust denial or minimization, Jewish conspiracy theories, coded language like "globalists", "Rothschilds", triple parentheses
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- islamophobia: anti-Muslim hate, mosque opposition framed as hate, "Islam is not a religion" rhetoric, "takeover/omvolking" narratives, halal/hijab targeting
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- sexism: gender-based discrimination, harassment, misogyny, or misandry
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- homophobia: targeting sexual orientation or gender identity, anti-LGBTQ+ rhetoric
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- insult: personal attacks, name-calling, belittling
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- dehumanization: comparing people to animals, vermin, disease, parasites, or other dehumanizing language
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- extremism: far-right or far-left extremist rhetoric, Nazi symbolism or glorification, white supremacist language, Great Replacement theory ("omvolkingstheorie"), calls for political violence, fascist/authoritarian glorification
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- ableism: targeting people with disabilities, using mental health conditions as insults (e.g. "gestoord", "autist" as slur, "mongool")
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Important context:
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- Many posts are in Dutch. Handle Dutch slang, insults, and coded political language.
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- Dutch-specific coded terms: "gelukszoekers", "kutmarokkanen", "omvolking", "landverrader", "volksverrader", "linkse ratten", "wappie", "tokkie" — score appropriately based on context.
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- Political disagreement and criticism are NOT toxic — only score actual hostility, hate, or threats.
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- Satire and parody accounts may use irony — consider context but still score the literal content.
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- A score of 0.0 means the category is completely absent. A score of 1.0 means extreme/explicit.
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- Most posts will score 0.0 on most categories. Only flag genuine toxicity.
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Example for 2 posts:
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{"1":{"toxic":0.0,"threat":0.0,"hate_speech":0.0,"racism":0.0,"antisemitism":0.0,"islamophobia":0.0,"sexism":0.0,"homophobia":0.0,"insult":0.0,"dehumanization":0.0,"extremism":0.0,"ableism":0.0},"2":{"toxic":0.3,"threat":0.0,"hate_speech":0.0,"racism":0.0,"antisemitism":0.0,"islamophobia":0.0,"sexism":0.0,"homophobia":0.0,"insult":0.2,"dehumanization":0.0,"extremism":0.0,"ableism":0.0}}"""
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@dataclass
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class ToxicityScores:
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"""Classification result for a single post."""
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toxic: float = 0.0
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threat: float = 0.0
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hate_speech: float = 0.0
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racism: float = 0.0
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antisemitism: float = 0.0
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islamophobia: float = 0.0
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sexism: float = 0.0
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homophobia: float = 0.0
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insult: float = 0.0
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dehumanization: float = 0.0
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extremism: float = 0.0
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ableism: float = 0.0
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@property
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def overall(self) -> float:
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"""Overall toxicity = max of all categories."""
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return max(
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self.toxic,
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self.threat,
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self.hate_speech,
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self.racism,
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self.antisemitism,
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self.islamophobia,
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self.sexism,
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self.homophobia,
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self.insult,
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self.dehumanization,
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self.extremism,
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self.ableism,
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)
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def is_flagged(self, threshold: float = 0.5) -> bool:
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return self.overall >= threshold
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def to_dict(self) -> dict:
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return {
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"toxic": self.toxic,
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"threat": self.threat,
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"hate_speech": self.hate_speech,
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"racism": self.racism,
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"antisemitism": self.antisemitism,
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"islamophobia": self.islamophobia,
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"sexism": self.sexism,
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"homophobia": self.homophobia,
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"insult": self.insult,
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"dehumanization": self.dehumanization,
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"extremism": self.extremism,
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"ableism": self.ableism,
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"overall": self.overall,
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}
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# Approximate token counts for cost tracking
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input_tokens: int = 0
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output_tokens: int = 0
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def parse_scores(raw: str) -> ToxicityScores:
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"""Parse the JSON scores for a single post into ToxicityScores."""
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try:
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data = json.loads(raw) if isinstance(raw, str) else raw
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except json.JSONDecodeError:
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logger.warning("Failed to parse JSON response: %s", str(raw)[:200])
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return ToxicityScores()
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def clamp(val) -> float:
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try:
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f = float(val)
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return max(0.0, min(1.0, f))
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except (TypeError, ValueError):
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return 0.0
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return ToxicityScores(
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toxic=clamp(data.get("toxic")),
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threat=clamp(data.get("threat")),
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hate_speech=clamp(data.get("hate_speech")),
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racism=clamp(data.get("racism")),
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antisemitism=clamp(data.get("antisemitism")),
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islamophobia=clamp(data.get("islamophobia")),
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sexism=clamp(data.get("sexism")),
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homophobia=clamp(data.get("homophobia")),
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insult=clamp(data.get("insult")),
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dehumanization=clamp(data.get("dehumanization")),
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extremism=clamp(data.get("extremism")),
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ableism=clamp(data.get("ableism")),
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)
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def parse_batch_response(raw: str, batch_size: int) -> list[ToxicityScores]:
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"""Parse a batched JSON response into a list of ToxicityScores.
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Expected format: {"1": {...scores...}, "2": {...scores...}, ...}
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Returns a list of ToxicityScores in the same order as the input batch.
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"""
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try:
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data = json.loads(raw)
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except json.JSONDecodeError:
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logger.warning("Failed to parse batch JSON: %s", raw[:300])
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return [ToxicityScores() for _ in range(batch_size)]
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results = []
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for i in range(1, batch_size + 1):
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key = str(i)
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if key in data and isinstance(data[key], dict):
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results.append(parse_scores(data[key]))
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else:
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logger.warning("Missing scores for post %d in batch response", i)
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results.append(ToxicityScores())
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return results
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class ToxicityClassifier:
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"""Async OpenAI-based toxicity classifier with batch support."""
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def __init__(self, api_key: str, model: str = "gpt-4.1-nano"):
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self.client = AsyncOpenAI(api_key=api_key)
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self.model = model
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async def classify_batch(
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self, texts: list[str], max_retries: int = 5
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) -> list[ToxicityScores]:
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"""Classify multiple posts in a single API call.
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Args:
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texts: List of post texts to classify (1–batch_size items).
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max_retries: Number of retries on rate limit / transient errors.
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Returns:
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List of ToxicityScores, one per input text, in the same order.
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"""
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if not texts:
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return []
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# Handle single-item batches efficiently
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batch_size = len(texts)
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# Build the numbered user message
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parts = []
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for i, text in enumerate(texts, 1):
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# Truncate very long posts
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t = text.strip() if text else ""
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if len(t) > 2000:
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t = t[:2000]
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if not t:
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t = "(empty)"
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parts.append(f"[{i}] {t}")
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user_message = "\n\n".join(parts)
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# Scale max_tokens by batch size.
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# Each post's JSON scores ≈ 60 tokens compact, but the model often
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# outputs formatted JSON (whitespace/newlines) which can double that.
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# Use a generous budget to avoid truncation.
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max_tokens = max(300, batch_size * 200)
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last_err = None
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for attempt in range(max_retries):
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try:
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response = await self.client.chat.completions.create(
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model=self.model,
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temperature=0,
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max_tokens=max_tokens,
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response_format={"type": "json_object"},
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messages=[
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{"role": "system", "content": SYSTEM_PROMPT},
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{"role": "user", "content": user_message},
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],
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)
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content = response.choices[0].message.content or "{}"
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results = parse_batch_response(content, batch_size)
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# Distribute token usage evenly for cost tracking
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if response.usage:
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per_post_input = response.usage.prompt_tokens // batch_size
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per_post_output = response.usage.completion_tokens // batch_size
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for scores in results:
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scores.input_tokens = per_post_input
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scores.output_tokens = per_post_output
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return results
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except RateLimitError as e:
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last_err = e
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wait = min(2 ** attempt + random.uniform(0.5, 1.5), 30)
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logger.debug(
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"Rate limited (attempt %d/%d), waiting %.1fs",
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attempt + 1, max_retries, wait,
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)
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await asyncio.sleep(wait)
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except (APITimeoutError, APIConnectionError) as e:
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last_err = e
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wait = 2 ** attempt + random.uniform(0, 1)
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logger.debug(
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"Transient error (attempt %d/%d), retrying in %.1fs: %s",
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attempt + 1, max_retries, wait, e,
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)
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await asyncio.sleep(wait)
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except Exception:
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logger.exception(
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"Batch classification API call failed (%d posts)", batch_size
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)
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raise
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# All retries exhausted
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logger.error("Rate limit retries exhausted for batch of %d posts", batch_size)
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raise last_err
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async def classify(self, text: str, max_retries: int = 5) -> ToxicityScores:
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"""Classify a single post (convenience wrapper around classify_batch)."""
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results = await self.classify_batch([text], max_retries=max_retries)
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return results[0]
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async def close(self):
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await self.client.close()
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