Architecture Teardown: How Viral Tech Opinions Spread on Twitter/X in 2026

import os\nimport json\nimport time\nimport logging\nfrom typing import List, Dict, Optional, Tuple\nfrom dataclasses import dataclass\n\nimport tweepy\nfrom transformers import AutoTokenizer, AutoModelForSequenceClassification, pipeline\nimport torch\nfrom tenacity import retry, stop_after_attempt, wait_exponential, retry_if_exception_type\n\n# Configure logging for audit trails\nlogging.basicConfig(\n    level=logging.INFO,\n    format=\"%(asctime)s - %(name)s - %(levelname)s - %(message)s\"\n)\nlogger = logging.getLogger(__name__)\n\n# Retry decorator for X API rate limits (429 errors)\n@retry(\n    stop=stop_after_attempt(5),\n    wait=wait_exponential(multiplier=1, min=2, max=60),\n    retry=retry_if_exception_type(tweepy.TooManyRequests)\n)\ndef fetch_x_post(post_id: str, client: tweepy.Client) -> Optional[Dict]:\n    \"\"\"Fetch a single X post by ID with rate limit handling.\"\"\"\n    try:\n        response = client.get_tweet(\n            id=post_id,\n            tweet_fields=[\"created_at\", \"public_metrics\", \"author_id\", \"conversation_id\"]\n        )\n        if response.data:\n            return response.data.data\n        logger.warning(f\"No data returned for post {post_id}\")\n        return None\n    except tweepy.NotFound:\n        logger.error(f\"Post {post_id} not found\")\n        return None\n    except Exception as e:\n        logger.error(f\"Unexpected error fetching post {post_id}: {str(e)}\")\n        raise\n\n@dataclass\nclass SentimentCluster:\n    \"\"\"Represents a pre-defined viral sentiment cluster.\"\"\"\n    cluster_id: str\n    keywords: List[str]\n    model_path: str\n    threshold: float = 0.85\n    _pipeline: Optional[pipeline] = None\n\n    def load_model(self) -> None:\n        \"\"\"Lazy load the BERT model for the cluster.\"\"\"\n        if self._pipeline is None:\n            try:\n                tokenizer = AutoTokenizer.from_pretrained(self.model_path)\n                model = AutoModelForSequenceClassification.from_pretrained(self.model_path)\n                self._pipeline = pipeline(\n                    \"text-classification\",\n                    model=model,\n                    tokenizer=tokenizer,\n                    device=0 if torch.cuda.is_available() else -1\n                )\n                logger.info(f\"Loaded model for cluster {self.cluster_id}\")\n            except Exception as e:\n                logger.error(f\"Failed to load model for cluster {self.cluster_id}: {str(e)}\")\n                raise\n\n    def matches(self, text: str) -> Tuple[bool, float]:\n        \"\"\"Check if text matches the cluster with confidence score.\"\"\"\n        if self._pipeline is None:\n            self.load_model()\n        try:\n            result = self._pipeline(text[:512])  # Truncate to BERT max length\n            score = result[0][\"score\"]\n            return (score >= self.threshold, score)\n        except Exception as e:\n            logger.error(f\"Cluster {self.cluster_id} inference failed: {str(e)}\")\n            return (False, 0.0)\n\nclass SentimentClusterDetector:\n    \"\"\"Detects which viral sentiment cluster (if any) a post belongs to.\"\"\"\n    def __init__(self, x_bearer_token: str, cluster_config_path: str):\n        self.x_client = tweepy.Client(bearer_token=x_bearer_token)\n        self.clusters = self._load_clusters(cluster_config_path)\n        logger.info(f\"Initialized detector with {len(self.clusters)} clusters\")\n\n    def _load_clusters(self, config_path: str) -> List[SentimentCluster]:\n        \"\"\"Load cluster configurations from JSON.\"\"\"\n        try:\n            with open(config_path, \"r\") as f:\n                config = json.load(f)\n            clusters = []\n            for item in config.get(\"clusters\", []):\n                clusters.append(SentimentCluster(\n                    cluster_id=item[\"id\"],\n                    keywords=item[\"keywords\"],\n                    model_path=item[\"model_path\"],\n                    threshold=item.get(\"threshold\", 0.85)\n                ))\n            return clusters\n        except Exception as e:\n            logger.error(f\"Failed to load cluster config: {str(e)}\")\n            raise\n\n    def detect_for_post(self, post_id: str) -> Optional[Dict]:\n        \"\"\"Run detection for a single X post.\"\"\"\n        post = fetch_x_post(post_id, self.x_client)\n        if not post:\n            return None\n        text = post.get(\"text\", \"\")\n        if not text:\n            logger.warning(f\"Post {post_id} has no text\")\n            return None\n        # Check all clusters\n        matches = []\n        for cluster in self.clusters:\n            is_match, score = cluster.matches(text)\n            if is_match:\n                matches.append({\n                    \"cluster_id\": cluster.cluster_id,\n                    \"score\": score,\n                    \"keywords\": cluster.keywords\n                })\n        return {\n            \"post_id\": post_id,\n            \"text\": text[:200] + \"...\" if len(text) > 200 else text,\n            \"matches\": matches,\n            \"public_metrics\": post.get(\"public_metrics\", {})\n        }\n\nif __name__ == \"__main__\":\n    # Example usage (replace with actual bearer token and config path)\n    detector = SentimentClusterDetector(\n        x_bearer_token=os.getenv(\"X_BEARER_TOKEN\"),\n        cluster_config_path=\"cluster_config.json\"\n    )\n    result = detector.detect_for_post(\"1234567890\")  # Replace with real post ID\n    print(json.dumps(result, indent=2))\n

import os\nimport json\nimport pickle\nimport logging\nfrom typing import List, Dict, Optional\nfrom dataclasses import dataclass\nimport pandas as pd\nimport numpy as np\nfrom sklearn.ensemble import GradientBoostingRegressor\nfrom sklearn.model_selection import train_test_split\nfrom sklearn.metrics import mean_absolute_error\nimport boto3\nfrom botocore.exceptions import ClientError\n\n# Configure logging\nlogging.basicConfig(\n    level=logging.INFO,\n    format=\"%(asctime)s - %(name)s - %(levelname)s - %(message)s\"\n)\nlogger = logging.getLogger(__name__)\n\n@dataclass\nclass ViralPostFeatures:\n    \"\"\"Feature set for viral spread prediction.\"\"\"\n    post_id: str\n    cluster_match_count: int\n    max_cluster_score: float\n    retweet_count: int\n    reply_count: int\n    like_count: int\n    author_follower_count: int\n    post_age_hours: float\n    has_media: bool\n    cluster_ids: List[str]\n\nclass ViralSpreadPredictor:\n    \"\"\"Predicts the viral reach of an X post based on cluster matches and engagement metrics.\"\"\"\n    def __init__(\n        self,\n        model_path: str = \"viral_predictor.pkl\",\n        s3_bucket: Optional[str] = None,\n        s3_key: Optional[str] = None\n    ):\n        self.model_path = model_path\n        self.s3_bucket = s3_bucket\n        self.s3_key = s3_key\n        self.model = None\n        self.feature_columns = [\n            \"cluster_match_count\",\n            \"max_cluster_score\",\n            \"retweet_count\",\n            \"reply_count\",\n            \"like_count\",\n            \"author_follower_count\",\n            \"post_age_hours\",\n            \"has_media\"\n        ]\n        self._load_model()\n\n    def _load_model(self) -> None:\n        \"\"\"Load trained model from local path or S3.\"\"\"\n        try:\n            if self.s3_bucket and self.s3_key:\n                s3 = boto3.client(\"s3\")\n                response = s3.get_object(Bucket=self.s3_bucket, Key=self.s3_key)\n                self.model = pickle.loads(response[\"Body\"].read())\n                logger.info(f\"Loaded model from s3://{self.s3_bucket}/{self.s3_key}\")\n            elif os.path.exists(self.model_path):\n                with open(self.model_path, \"rb\") as f:\n                    self.model = pickle.load(f)\n                logger.info(f\"Loaded model from {self.model_path}\")\n            else:\n                logger.warning(\"No pre-trained model found, initializing new GradientBoostingRegressor\")\n                self.model = GradientBoostingRegressor(\n                    n_estimators=150,\n                    learning_rate=0.05,\n                    max_depth=5,\n                    random_state=42\n                )\n        except ClientError as e:\n            logger.error(f\"S3 error loading model: {str(e)}\")\n            raise\n        except Exception as e:\n            logger.error(f\"Failed to load model: {str(e)}\")\n            raise\n\n    def _extract_features(self, post_data: Dict) -> ViralPostFeatures:\n        \"\"\"Extract features from post detection and X API data.\"\"\"\n        try:\n            cluster_matches = post_data.get(\"matches\", [])\n            max_score = max([m[\"score\"] for m in cluster_matches], default=0.0)\n            public_metrics = post_data.get(\"public_metrics\", {})\n            # Assume author data is fetched separately (simplified here)\n            author_followers = post_data.get(\"author_follower_count\", 0)\n            created_at = pd.to_datetime(post_data.get(\"created_at\"))\n            now = pd.Timestamp.now()\n            post_age_hours = (now - created_at).total_seconds() / 3600\n            return ViralPostFeatures(\n                post_id=post_data.get(\"post_id\"),\n                cluster_match_count=len(cluster_matches),\n                max_cluster_score=max_score,\n                retweet_count=public_metrics.get(\"retweet_count\", 0),\n                reply_count=public_metrics.get(\"reply_count\", 0),\n                like_count=public_metrics.get(\"like_count\", 0),\n                author_follower_count=author_followers,\n                post_age_hours=post_age_hours,\n                has_media=post_data.get(\"has_media\", False),\n                cluster_ids=[m[\"cluster_id\"] for m in cluster_matches]\n            )\n        except Exception as e:\n            logger.error(f\"Feature extraction failed: {str(e)}\")\n            raise\n\n    def predict_reach(self, post_data: Dict) -> Optional[Dict]:\n        \"\"\"Predict expected viral reach (total impressions) for a post.\"\"\"\n        if not self.model:\n            logger.error(\"Model not loaded, cannot predict\")\n            return None\n        try:\n            features = self._extract_features(post_data)\n            # Convert to DataFrame for prediction\n            feature_df = pd.DataFrame([{\n                \"cluster_match_count\": features.cluster_match_count,\n                \"max_cluster_score\": features.max_cluster_score,\n                \"retweet_count\": features.retweet_count,\n                \"reply_count\": features.reply_count,\n                \"like_count\": features.like_count,\n                \"author_follower_count\": features.author_follower_count,\n                \"post_age_hours\": features.post_age_hours,\n                \"has_media\": int(features.has_media)\n            }])[self.feature_columns]\n            # Predict log-transformed reach (common for skewed social media metrics)\n            log_reach = self.model.predict(feature_df)[0]\n            predicted_reach = int(np.expm1(log_reach))  # Reverse log1p transform\n            return {\n                \"post_id\": features.post_id,\n                \"predicted_reach\": predicted_reach,\n                \"cluster_match_count\": features.cluster_match_count,\n                \"matched_clusters\": features.cluster_ids,\n                \"confidence\": \"high\" if features.max_cluster_score >= 0.9 else \"medium\"\n            }\n        except Exception as e:\n            logger.error(f\"Prediction failed: {str(e)}\")\n            return None\n\n    def train(self, training_data_path: str) -> None:\n        \"\"\"Train the model on historical viral post data.\"\"\"\n        try:\n            df = pd.read_csv(training_data_path)\n            # Preprocess: log transform target (total_impressions)\n            df[\"log_impressions\"] = np.log1p(df[\"total_impressions\"])\n            X = df[self.feature_columns]\n            y = df[\"log_impressions\"]\n            X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n            self.model.fit(X_train, y_train)\n            y_pred = self.model.predict(X_test)\n            mae = mean_absolute_error(y_test, y_pred)\n            logger.info(f\"Model trained. Test MAE (log impressions): {mae:.4f}\")\n            # Save model\n            with open(self.model_path, \"wb\") as f:\n                pickle.dump(self.model, f)\n            logger.info(f\"Model saved to {self.model_path}\")\n        except Exception as e:\n            logger.error(f\"Training failed: {str(e)}\")\n            raise\n\nif __name__ == \"__main__\":\n    predictor = ViralSpreadPredictor(\n        model_path=\"viral_predictor.pkl\",\n        s3_bucket=os.getenv(\"S3_BUCKET\"),\n        s3_key=os.getenv(\"S3_MODEL_KEY\")\n    )\n    # Example: load post data from detector output\n    with open(\"post_detection.json\", \"r\") as f:\n        post_data = json.load(f)\n    prediction = predictor.predict_reach(post_data)\n    print(json.dumps(prediction, indent=2))\n

import json\nimport logging\nfrom typing import Dict, List, Optional\nfrom dataclasses import dataclass\nimport boto3\nfrom botocore.exceptions import ClientError\nimport pandas as pd\n\n# Configure logging\nlogging.basicConfig(\n    level=logging.INFO,\n    format=\"%(asctime)s - %(name)s - %(levelname)s - %(message)s\"\n)\nlogger = logging.getLogger(__name__)\n\n@dataclass\nclass InferenceCostConfig:\n    \"\"\"Cost configuration for inference components.\"\"\"\n    bert_gpu_hourly: float = 3.24  # USD per hour for A10G GPU\n    bert_cpu_hourly: float = 0.12   # USD per hour for 4 vCPU\n    x_api_request: float = 0.0001   # USD per request (X Enterprise tier)\n    s3_storage_gb: float = 0.023    # USD per GB/month\n    dynamodb_read: float = 0.0004    # USD per read request unit\n    dynamodb_write: float = 0.0008   # USD per write request unit\n\n@dataclass\nclass UsageMetrics:\n    \"\"\"Monthly usage metrics for the architecture.\"\"\"\n    bert_inference_hours_gpu: float\n    bert_inference_hours_cpu: float\n    x_api_requests: int\n    s3_storage_gb: float\n    dynamodb_reads: int\n    dynamodb_writes: int\n    cluster_count: int\n\nclass ArchitectureCostCalculator:\n    \"\"\"Calculates monthly and annual costs for the viral opinion architecture.\"\"\"\n    def __init__(self, config_path: Optional[str] = None):\n        self.config = InferenceCostConfig()\n        if config_path:\n            self._load_config(config_path)\n        logger.info(\"Initialized cost calculator\")\n\n    def _load_config(self, config_path: str) -> None:\n        \"\"\"Load custom cost config from JSON.\"\"\"\n        try:\n            with open(config_path, \"r\") as f:\n                config_data = json.load(f)\n            self.config = InferenceCostConfig(**config_data)\n            logger.info(f\"Loaded custom config from {config_path}\")\n        except Exception as e:\n            logger.error(f\"Failed to load config: {str(e)}\")\n            raise\n\n    def calculate_monthly_cost(self, usage: UsageMetrics) -> Dict:\n        \"\"\"Calculate total monthly cost broken down by component.\"\"\"\n        try:\n            costs = {}\n            # BERT inference costs\n            costs[\"bert_gpu\"] = usage.bert_inference_hours_gpu * self.config.bert_gpu_hourly\n            costs[\"bert_cpu\"] = usage.bert_inference_hours_cpu * self.config.bert_cpu_hourly\n            # X API costs\n            costs[\"x_api\"] = usage.x_api_requests * self.config.x_api_request\n            # Storage costs\n            costs[\"s3_storage\"] = usage.s3_storage_gb * self.config.s3_storage_gb\n            # Database costs\n            costs[\"dynamodb_reads\"] = usage.dynamodb_reads * self.config.dynamodb_read\n            costs[\"dynamodb_writes\"] = usage.dynamodb_writes * self.config.dynamodb_write\n            # Cluster fixed costs (licensing, maintenance)\n            costs[\"cluster_maintenance\"] = usage.cluster_count * 1200  # $1200 per cluster/month\n            # Total\n            total = sum(costs.values())\n            costs[\"total_monthly\"] = round(total, 2)\n            costs[\"total_annual\"] = round(total * 12, 2)\n            logger.info(f\"Calculated monthly cost: ${costs['total_monthly']:.2f}\")\n            return costs\n        except Exception as e:\n            logger.error(f\"Cost calculation failed: {str(e)}\")\n            raise\n\n    def generate_optimization_recommendations(self, usage: UsageMetrics, costs: Dict) -> List[str]:\n        \"\"\"Generate cost optimization tips based on usage.\"\"\"\n        recommendations = []\n        # Check GPU vs CPU usage\n        gpu_cost = costs.get(\"bert_gpu\", 0)\n        cpu_cost = costs.get(\"bert_cpu\", 0)\n        if gpu_cost > cpu_cost * 4:\n            recommendations.append(\n                f\"GPU inference costs (${gpu_cost:.2f}) are 4x CPU costs (${cpu_cost:.2f}). \"\n                \"Consider moving low-priority cluster inference to CPU.\"\n            )\n        # Check X API costs\n        x_api_cost = costs.get(\"x_api\", 0)\n        if x_api_cost > 5000:\n            recommendations.append(\n                f\"X API costs (${x_api_cost:.2f}) exceed $5k/month. \"\n                \"Use bulk API endpoints for batch post fetching to reduce request count.\"\n            )\n        # Check DynamoDB costs\n        dynamodb_total = costs.get(\"dynamodb_reads\", 0) + costs.get(\"dynamodb_writes\", 0)\n        if dynamodb_total > 3000:\n            recommendations.append(\n                f\"DynamoDB costs (${dynamodb_total:.2f}) exceed $3k/month. \"\n                \"Switch to on-demand capacity mode if traffic is spiky, or use DAX caching.\"\n            )\n        # Cluster maintenance\n        cluster_cost = costs.get(\"cluster_maintenance\", 0)\n        if cluster_cost > 15000:\n            recommendations.append(\n                f\"Cluster maintenance costs (${cluster_cost:.2f}) exceed $15k/month. \"\n                \"Consolidate overlapping sentiment clusters to reduce total cluster count.\"\n            )\n        return recommendations\n\n    def export_to_csv(self, costs: Dict, output_path: str) -> None:\n        \"\"\"Export cost breakdown to CSV for reporting.\"\"\"\n        try:\n            df = pd.DataFrame([costs])\n            df.to_csv(output_path, index=False)\n            logger.info(f\"Exported cost report to {output_path}\")\n        except Exception as e:\n            logger.error(f\"Export failed: {str(e)}\")\n            raise\n\nif __name__ == \"__main__\":\n    calculator = ArchitectureCostCalculator(config_path=\"cost_config.json\")\n    # Example usage metrics (average for mid-sized team)\n    usage = UsageMetrics(\n        bert_inference_hours_gpu=720,  # 24/7 GPU usage\n        bert_inference_hours_cpu=1440,  # 24/7 2x CPU instances\n        x_api_requests=1200000,\n        s3_storage_gb=5000,\n        dynamodb_reads=5000000,\n        dynamodb_writes=2000000,\n        cluster_count=14\n    )\n    costs = calculator.calculate_monthly_cost(usage)\n    print(\"Monthly Cost Breakdown:\")\n    for k, v in costs.items():\n        print(f\"{k}: ${v:.2f}\" if isinstance(v, float) else f\"{k}: {v}\")\n    recommendations = calculator.generate_optimization_recommendations(usage, costs)\n    print(\"\\nOptimization Recommendations:\")\n    for rec in recommendations:\n        print(f\"- {rec}\")\n

# Snippet: Check cluster overlap using cosine similarity\nfrom sentence_transformers import SentenceTransformer, util\n\nmodel = SentenceTransformer(\"all-MiniLM-L6-v2\")\ncluster_keywords = [[\"kubernetes\", \"cloud native\"], [\"k8s\", \"container orchestration\"]]\nembeddings = model.encode([\", \".join(k) for k in cluster_keywords])\nsimilarity = util.cos_sim(embeddings[0], embeddings[1])\nprint(f\"Cluster overlap similarity: {similarity.item():.2f}\")  # >0.8 means merge

# Snippet: Route inference to GPU/CPU based on cluster traffic\ndef route_inference(cluster_id: str, daily_requests: int):\n    gpu_threshold = 10000\n    if daily_requests >= gpu_threshold:\n        return \"gpu\"\n    return \"cpu\"\n# Example: \"rust-web-frameworks\" cluster has 1200 daily requests\nprint(route_inference(\"rust-web-frameworks\", 1200))  # Output: cpu

# Snippet: Flag high-impact cluster matches for human review\nHIGH_IMPACT_CLUSTERS = {\"security-vuln\", \"oss-license\", \"layoff-rumors\"}\ndef flag_for_review(cluster_ids: list, predicted_reach: int):\n    if any(c in HIGH_IMPACT_CLUSTERS for c in cluster_ids) and predicted_reach > 100000:\n        return True\n    return False\n# Example: post matches \"security-vuln\" cluster, predicted reach 250k\nprint(flag_for_review([\"security-vuln\"], 250000))  # Output: True