Update Linkedin_Data_API_Calls.py

Linkedin_Data_API_Calls.py

@@ -115,7 +115,7 @@ def fetch_linkedin_posts_core(comm_client_id, community_token, org_urn, count):
             "published_at_iso": published_at_iso,
             # These are placeholders for actual fields from LinkedIn API response. Verify field names.
             "organization_urn": p.get("author", "urn:li:unknown"), # e.g., "urn:li:person:xxxx" or "urn:li:organization:xxxx"
-            "is_ad": p.get("is_ad", False) # LinkedIn might use a different field like 'sponsored' or 'promoted'
+            "is_ad": 'adContext' in p
             #"media_type": p.get("mediaCategory", "NONE") # e.g., ARTICLE, IMAGE, VIDEO, NONE
         })
     logging.info(f"Processed {len(processed_raw_posts)} posts with core data.")
@@ -290,7 +290,7 @@ def compile_detailed_posts(processed_raw_posts, stats_map, sentiments_per_post):
             "summary": proc_post["summary"],
             "category": proc_post["category"],
             "organization_urn": proc_post["organization_urn"],
-            "is_ad": proc_post["is_ad"],
+            "is_ad": 'adContext' in proc_post,
             #"media_type": proc_post["media_type"],
             "published_at": proc_post["published_at_iso"] # ISO format datetime string
         })
@@ -314,7 +314,7 @@ def prepare_data_for_bubble(detailed_posts, all_actual_comments_data):
         li_posts.append({
             "organization_urn": post_data["organization_urn"],
             "id": post_data["id"], # Post URN
-            "is_ad": post_data["is_ad"],
+            "is_ad": 'adContext' in post_data,
             #"media_type": post_data["media_type"],
             "published_at": post_data["published_at"], # ISO datetime string
             "sentiment": post_data["sentiment"], # Overall sentiment of the post based on its comments