Use cases

Recent posts snapshot

Fetch a company’s most recent social posts with their content type and top-line engagement.

cURL

curl -G "https://api.akta.pro/api/v1/company/posts/" \
  --data-urlencode "company=https://canva.com" \
  --data-urlencode "limit=10" \
  -H "x-api-key: your api key"

import requests

url = "https://api.akta.pro/api/v1/company/posts"

headers = {"x-api-key": "your api key"}
params = {"company": "https://canva.com", "limit": 10}

response = requests.get(url, headers=headers, params=params)
print(response.text)

Sample response:

{
  "count": 406,
  "limit": 10,
  "offset": 0,
  "data": {
    "uuid": "00000jw-canva",
    "posts": [
      {
        "id": "WXFWCQ1N6K",
        "content_type": "image",
        "link": "https://www.linkedin.com/feed/update/urn:li:activity:7478764514239315969/",
        "posted_date": "2026-07-03 11:00:08.920000+00:00",
        "is_paid": false,
        "reposted": false,
        "engagement_metrics": {
          "total_reaction_count": 1315,
          "like_count": 1029,
          "empathy_count": 214,
          "praise_count": 16,
          "appreciation_count": 0,
          "interest_count": 2,
          "funny_count": 40,
          "comments_count": 0,
          "reposts_Count": 65
        },
        "text_content": "The growth? We love to see it. 🤩 What did your early designs look like?",
        "post_classification": "Other",
        "author": {},
        "mentions": []
      }
    ]
  },
  "credits_consumed": 1
}

Content mix by classification and format

Break down what a company posts about (post_classification) and in what format (content_type) to profile its content strategy.

import requests
from collections import Counter

HEADERS = {"x-api-key": "your_api_key"}
BASE_URL = "https://api.akta.pro/api/v1/company/posts"
PAGE_SIZE = 30

def fetch_all_posts(company):
    all_posts = []
    offset = 0

    while True:
        resp = requests.get(
            BASE_URL,
            headers=HEADERS,
            params={"company": company, "limit": PAGE_SIZE, "offset": offset}
        )
        resp.raise_for_status()
        payload = resp.json()

        posts = payload["data"]["posts"]
        all_posts.extend(posts)

        total = payload["count"]
        offset += len(posts)

        if offset >= total or not posts:
            break

    return all_posts

posts = fetch_all_posts("canva.com")

by_theme  = Counter(p["post_classification"] for p in posts)
by_format = Counter(p["content_type"] for p in posts)

print(f"Total posts analyzed: {len(posts)}\n")

print("Content by theme:")
for theme, n in by_theme.most_common():
    print(f"  {theme:<24} {n}")

print("\nContent by format:")
for fmt, n in by_format.most_common():
    print(f"  {fmt:<18} {n}")

Engagement performance analysis

Rank posts by engagement to find what resonates, and compare average engagement across themes or formats.

import requests
from collections import defaultdict

HEADERS = {"x-api-key": "your_api_key"}
BASE_URL = "https://api.akta.pro/api/v1/company/posts"
PAGE_SIZE = 30

def fetch_all_posts(company):
    all_posts = []
    offset = 0

    while True:
        resp = requests.get(
            BASE_URL,
            headers=HEADERS,
            params={"company": company, "limit": PAGE_SIZE, "offset": offset}
        )
        resp.raise_for_status()
        payload = resp.json()

        posts = payload["data"]["posts"]
        all_posts.extend(posts)

        total = payload["count"]
        offset += len(posts)

        if offset >= total or not posts:
            break

    return all_posts

def total_engagement(p):
    m = p["engagement_metrics"]
    return m["total_reaction_count"] + m["comments_count"] + m["reposts_Count"]

posts = fetch_all_posts("canva.com")
print(f"Total posts analyzed: {len(posts)}\n")

# Top posts overall
print("Top posts by engagement:")
for p in sorted(posts, key=total_engagement, reverse=True)[:5]:
    print(f"  {total_engagement(p):>6}  [{p['content_type']}]  {p['text_content'][:50]}")

# Average engagement per theme
sums, counts = defaultdict(int), defaultdict(int)
for p in posts:
    sums[p["post_classification"]] += total_engagement(p)
    counts[p["post_classification"]] += 1

print("\nAverage engagement by theme:")
for theme in sorted(sums, key=lambda t: sums[t]/counts[t], reverse=True):
    print(f"  {theme:<24} {sums[theme]/counts[theme]:,.0f}")

Reaction sentiment mix

Beyond raw reaction counts, the breakdown by reaction type hints at how an audience responds — celebratory (praise), supportive (empathy), amused (funny), or simply approving (like).

import requests

HEADERS = {"x-api-key": "<YOUR_API_KEY>"}

resp = requests.get(
    "https://api.akta.pro/api/v1/company/posts",
    headers=HEADERS,
    params={"company": "canva.com", "limit": 50}
).json()

for p in resp["data"]["posts"][:5]:
    m = p["engagement_metrics"]
    total = m["total_reaction_count"] or 1
    funny_share = m["funny_count"] / total * 100
    print(f"{p['text_content'][:45]:<48} funny: {funny_share:4.1f}%  total: {m['total_reaction_count']}")

Posting cadence over time

Use posted_date to measure how frequently a company posts — a proxy for marketing activity and consistency.

import requests
from collections import Counter

HEADERS = {"x-api-key": "your_api_key"}

resp = requests.get(
    "https://api.akta.pro/api/v1/company/posts",
    headers=HEADERS,
    params={"company": "canva.com", "limit": 30}
).json()

posts = resp["data"]["posts"]

# Posts per month (YYYY-MM from the ISO date)
by_month = Counter(p["posted_date"][:7] for p in posts)

print(f"{'Month':<10} {'Posts'}")
print("-" * 18)
for month in sorted(by_month):
    print(f"{month:<10} {by_month[month]}")