Le contenu généré par les utilisateurs à partir des hashtags TikTok est la source la plus riche de mentions authentiques de marques, d'avis sur les produits et d'actifs créatifs pour le marketing. Ce collecteur parcourt les flux de hashtags, filtre les vidéos selon des seuils d'engagement et construit une bibliothèque UGC structurée avec des métadonnées. Chaque scan de hashtag coûte 0,005 $ via l'API Scavio TikTok.
Prérequis
- Python 3.8+
- bibliothèque requests
- Une clé API Scavio de scavio.dev
- Hashtags cibles à parcourir pour les UGC
Parcours
Étape 1: Parcourir les flux de hashtags pour trouver des vidéos
Extraire les vidéos récentes des hashtags cibles.
Python
import os, requests, json
from datetime import datetime
API_KEY = os.environ['SCAVIO_API_KEY']
TH = {'Authorization': f'Bearer {API_KEY}', 'Content-Type': 'application/json'}
def scan_hashtag(tag):
data = requests.post('https://api.scavio.dev/api/v1/tiktok/hashtag/videos',
headers=TH, json={'name': tag}).json()
videos = data.get('videos', data.get('data', {}).get('videos', []))
return [{'id': v.get('id', ''), 'desc': v.get('desc', '')[:100],
'author': v.get('author', {}).get('uniqueId', 'unknown'),
'plays': v.get('stats', {}).get('playCount', 0),
'likes': v.get('stats', {}).get('diggCount', 0),
'comments': v.get('stats', {}).get('commentCount', 0),
'shares': v.get('stats', {}).get('shareCount', 0),
'created': v.get('createTime', 0)} for v in videos]
videos = scan_hashtag('amazonfinds')
print(f'#amazonfinds: {len(videos)} videos found')
for v in videos[:3]:
print(f' @{v["author"]}: {v["desc"][:50]}... ({v["plays"]:,} plays)')Étape 2: Filtrer par seuils d'engagement
Conserver uniquement les UGC performants répondant aux critères d'engagement minimum.
Python
def filter_ugc(videos, min_plays=10000, min_likes=500, min_engagement_rate=3.0):
qualified = []
for v in videos:
plays = v.get('plays', 0)
if plays < min_plays:
continue
engagement = v['likes'] + v['comments'] + v['shares']
er = (engagement / plays * 100) if plays else 0
if v['likes'] >= min_likes and er >= min_engagement_rate:
v['engagement_rate'] = round(er, 2)
qualified.append(v)
qualified.sort(key=lambda x: x['engagement_rate'], reverse=True)
print(f'Filtered: {len(qualified)}/{len(videos)} videos meet criteria')
print(f' Min plays: {min_plays:,}, Min likes: {min_likes}, Min ER: {min_engagement_rate}%')
return qualified
filtered = filter_ugc(videos)
for v in filtered[:5]:
print(f' @{v["author"]:20} | {v["plays"]:>10,} plays | {v["engagement_rate"]}% ER | {v["desc"][:40]}')Étape 3: Parcourir plusieurs hashtags et dédoublonner
Collecter les UGC sur des hashtags connexes et supprimer les vidéos en double.
Python
def collect_ugc(hashtags, min_plays=10000, min_likes=500):
all_videos = []
seen_ids = set()
for tag in hashtags:
videos = scan_hashtag(tag)
for v in videos:
if v['id'] not in seen_ids:
v['hashtag'] = tag
all_videos.append(v)
seen_ids.add(v['id'])
print(f' #{tag}: {len(videos)} videos ({len(seen_ids)} unique total)')
filtered = filter_ugc(all_videos, min_plays=min_plays, min_likes=min_likes)
cost = len(hashtags) * 0.005
print(f'\nTotal: {len(filtered)} qualified UGC from {len(hashtags)} hashtags')
print(f'Cost: ${cost:.3f}')
return filtered
hashtags = ['amazonfinds', 'tiktokmademebuyit', 'musthave', 'productreview']
ugc_library = collect_ugc(hashtags)Étape 4: Construire et exporter la bibliothèque UGC
Enregistrer la bibliothèque UGC organisée avec les métadonnées pour une utilisation marketing.
Python
def export_ugc_library(ugc, filename='ugc_library.json'):
library = {
'collected_at': datetime.now().isoformat(),
'total_videos': len(ugc),
'videos': []
}
for v in ugc:
library['videos'].append({
'video_id': v['id'],
'author': v['author'],
'description': v['desc'],
'hashtag_source': v.get('hashtag', ''),
'metrics': {
'plays': v['plays'], 'likes': v['likes'],
'comments': v['comments'], 'shares': v['shares'],
'engagement_rate': v.get('engagement_rate', 0)
},
'tiktok_url': f'https://www.tiktok.com/@{v["author"]}/video/{v["id"]}'
})
with open(filename, 'w') as f:
json.dump(library, f, indent=2)
# Stats
avg_er = sum(v.get('engagement_rate', 0) for v in ugc) / len(ugc) if ugc else 0
avg_plays = sum(v['plays'] for v in ugc) / len(ugc) if ugc else 0
print(f'\nUGC Library saved to {filename}')
print(f' Videos: {len(ugc)}')
print(f' Avg engagement rate: {avg_er:.2f}%')
print(f' Avg plays: {avg_plays:,.0f}')
print(f' Top creator: @{ugc[0]["author"]}' if ugc else '')
export_ugc_library(ugc_library)Exemple Python
Python
import os, requests
TH = {'Authorization': f'Bearer {os.environ["SCAVIO_API_KEY"]}', 'Content-Type': 'application/json'}
def collect(hashtag, min_plays=10000):
data = requests.post('https://api.scavio.dev/api/v1/tiktok/hashtag/videos',
headers=TH, json={'name': hashtag}).json()
videos = data.get('videos', data.get('data', {}).get('videos', []))
filtered = [v for v in videos if v.get('stats', {}).get('playCount', 0) >= min_plays]
print(f'#{hashtag}: {len(filtered)}/{len(videos)} videos with {min_plays:,}+ plays. Cost: $0.005')
for v in filtered[:3]:
print(f' @{v.get("author", {}).get("uniqueId", "?")}: {v.get("desc", "")[:40]}')
collect('amazonfinds')Exemple JavaScript
JavaScript
const TH = { 'Authorization': `Bearer ${process.env.SCAVIO_API_KEY}`, 'Content-Type': 'application/json' };
async function collect(hashtag, minPlays = 10000) {
const data = await fetch('https://api.scavio.dev/api/v1/tiktok/hashtag/videos', {
method: 'POST', headers: TH, body: JSON.stringify({ name: hashtag })
}).then(r => r.json());
const videos = (data.videos || data.data?.videos || []);
const filtered = videos.filter(v => (v.stats?.playCount || 0) >= minPlays);
console.log(`#${hashtag}: ${filtered.length}/${videos.length} with ${minPlays.toLocaleString()}+ plays`);
filtered.slice(0, 3).forEach(v =>
console.log(` @${v.author?.uniqueId || '?'}: ${(v.desc || '').slice(0, 40)}`));
}
await collect('amazonfinds');Sortie attendue
JSON
#amazonfinds: 20 videos found
@sarahfinds: This Stanley tumbler color is everything... (2,400,000 plays)
@dealsqueen: Under $15 Amazon finds you NEED... (1,800,000 plays)
#amazonfinds: 20 videos (20 unique total)
#tiktokmademebuyit: 18 videos (35 unique total)
#musthave: 15 videos (44 unique total)
#productreview: 12 videos (50 unique total)
Filtered: 18/50 videos meet criteria
UGC Library saved to ugc_library.json
Videos: 18
Avg engagement rate: 7.34%
Avg plays: 890,000