Create fake_news.py

fake_news.py

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+import json
+from faker import Faker
+import random
+from datetime import datetime, timedelta
+
+fake = Faker()
+
+def generate_fake_data(num_nodes=10, num_links=5):
+    nodes = []
+    links = []
+    topics = ["Environment", "Politics", "Technology", "Health", "Economy"]
+    emotions = ["trust", "joy", "fear", "sadness", "anger", "surprise"]
+    sentiments = ["positive", "negative", "neutral"]
+
+    # Generate nodes
+    for i in range(1, num_nodes + 1):
+        node = {
+            "id": i,
+            "headline": fake.sentence(nb_words=6),
+            "topic": random.choice(topics),
+            "emotion": random.choice(emotions),
+            "time": (datetime.now() - timedelta(days=random.randint(0, 365))).strftime("%Y-%m-%d"),
+            "sentiment": random.choice(sentiments)
+        }
+        nodes.append(node)
+
+    # Generate links
+    for _ in range(num_links):
+        source = random.randint(1, num_nodes)
+        target = random.randint(1, num_nodes)
+        while target == source:
+            target = random.randint(1, num_nodes)
+        
+        link = {
+            "source": source,
+            "target": target,
+            "semantic_sim": round(random.uniform(0.1, 1.0), 2),
+            "causal": random.choice([True, False]),
+            "causal_note": fake.sentence(nb_words=8) if random.random() > 0.5 else None
+        }
+        links.append(link)
+
+    return {"nodes": nodes, "links": links}
+
+def main(num_nodes=10, num_links=5):
+    data = generate_fake_data(num_nodes, num_links)
+    return json.dumps(data, indent=2)
+
+if __name__ == "__main__":
+    print(main())