Telegram Scraping Guide with Python and Proxies

Telegram is a goldmine of public data: channels, groups, and bots buzz with news, market insights, and community chatter in real time. Whether you’re a researcher, marketer, or developer, scraping Telegram can unlock valuable datasets—think message histories, member lists, or media files. But challenges like API rate limits, geo-blocks, and anti-scraping measures can trip you up. This guide shows you how to scrape Telegram effectively using Python’s Telethon library and GoProxy’s rotating residential proxies, helping you sidestep hurdles while staying compliant.

Why Scrape Telegram？

Telegram scraping involves extracting data—messages, usernames, timestamps, or reactions—from public channels and groups using automated tools.

Use Cases

Market Research & Sentiment Analysis: Track discussion trends in niche communities (e.g., crypto, e-commerce). Extract post volume over time to gauge engagement spikes.

Ad Verification & Competitive Monitoring: Audit how ads appear across regions—detect discrepancies. Scrape media (images, videos) attached to sponsored posts.

Academic & Social Research: Collect public discussion data for network analysis or content studies. Monitor misinformation spread in public channels.

Common User Concerns

Geo-blocks: Telegram is restricted in some regions, blocking direct access.

Rate Limits & Bans: Too many requests from one IP can trigger throttling or temporary bans.

Data Completeness: Private group members might be hidden, or youmu you might miss media files.

Compliance: Staying within Telegram’s terms and local privacy laws (e.g., GDPR, CCPA).

Overview of the Solution

1. Telethon Library: A robust Python client for Telegram’s MTProto API.  For non-coders, GUI-based tools exist, but they lack the depth Telethon offers, so we’ll stick with it for its control and scalability.

2. GoProxy Rotating Residential Proxies:  Automatically rotates real residential IPs to bypass geo-blocks and rate limits, with sticky sessions up to 60 minutes for stable connections.

3. Structured Storage: Export to Parquet for large-scale datasets or CSV for quick analysis.

4. Scalability & Reliability: Automatic proxy rotation, flood-wait handling, and optional multi-account sessions.

Prerequisites

Telegram Account: Register and obtain API ID & API Hash from my.telegram.org.

Python 3.8+ Environment: Installed on your machine or in a cloud notebook.

Libraries: Install with pip install telethon pandas pyarrow requests

Proxy Credentials: Access the dashboard to get your GoProxy rotating proxy endpoints (username, password, proxy list).

Step 1: Set Up Your Environment

1. Create & Activate a Python Virtual Environment

Keeps dependencies isolated so you don’t break other projects.

bash

 

python3 -m venv telegram-scraper-env

# macOS/Linux

source telegram-scraper-env/bin/activate

# Windows

telegram-scraper-env\Scripts\activate

2. Install Required Libraries

bash

 

pip install telethon pandas pyarrow requests asyncio

3. Prepare Your Telegram Credentials

a. Go to my.telegram.org → API development tools.

b. Copy your API ID and API Hash—you’ll need them in code

Step 2: Configure Your Telegram Client with GoProxy

GoProxy handles IP rotation automatically via a single endpoint, simplifying setup:

python

 

from telethon import TelegramClient

from telethon.network.connection.tcpbear import ConnectionTcpMTProxy

 

api_id, api_hash = YOUR_API_ID, 'YOUR_API_HASH'

 

proxy = {

    'addr':   'auto.goproxy.com',

    'port':   8000,

    'secret': b'YOUR_GO_PROXY_SECRET'

}

 

client = TelegramClient(

    'session_name', api_id, api_hash,

    connection=ConnectionTcpMTProxy,

    proxy=proxy

)

 

await client.start()

print("✅ Connected as", await client.get_me())

Tip for Beginners: The first start() will ask for your phone number and the code Telegram sends you. Later runs reuse session_name.session.

Step 3: Scrape Messages from a Channel

1. Define Your Target & Date Range

python

 

from datetime import datetime

 

channel    = 'https://t.me/example_channel'

start_date = datetime(2025, 1, 1)

end_date   = datetime(2025, 6, 17)

2. Fetch & Save in Batches

python

 

import pandas as pd

from telethon.tl.types import InputMessagesFilterEmpty

from telethon.errors import FloodWaitError

import asyncio

 

records = []

 

async def fetch_messages():

    async for msg in client.iter_messages(

            channel,

            offset_date=end_date,

            reverse=True,

            filter=InputMessagesFilterEmpty()

        ):

        if msg.date < start_date:

            break

        records.append({

            'id':     msg.id,

            'date':   msg.date.isoformat(),

            'sender': getattr(msg.sender, 'id', None),

            'text':   msg.message or '',

            'views':  msg.views or 0

        })

        # Save every 500 records

        if len(records) % 500 == 0:

            pd.DataFrame(records).to_parquet('messages.parquet')

    # Final save

    pd.DataFrame(records).to_parquet('messages.parquet')

    print(f"✅ Scraped {len(records)} messages")

 

try:

    await fetch_messages()

except FloodWaitError as e:

    wait = e.seconds + 5

    print(f"⏱ Flood wait—sleeping {wait}s")

    await asyncio.sleep(wait)

    await fetch_messages()

Beginner Checklist:

• Confirm messages.parquet opens in your analysis tool.
• Experiment with a small limit parameter to test.

Pro Tip: Swap InputMessagesFilterEmpty for InputMessagesFilterPhotos to pull only images..

Step 4: Scrape Group Members & Contacts

Retrieve Up to 10,000 Members:

python

 

from telethon.tl.functions.channels import GetParticipantsRequest

from telethon.tl.types import ChannelParticipantsRecent

import pandas as pd

 

all_users, offset, limit = [], 0, 200

 

while True:

    resp = await client(GetParticipantsRequest(

        channel='https://t.me/example_group',

        filter=ChannelParticipantsRecent(),

        offset=offset,

        limit=limit,

        hash=0

    ))

    if not resp.users:

        break

    for u in resp.users:

        all_users.append({

            'id':         u.id,

            'username':   u.username or '',

            'first_name': u.first_name or '',

            'last_name':  u.last_name or ''

        })

    offset += len(resp.users)

 

pd.DataFrame(all_users).to_csv('group_members.csv', index=False)

print(f"✅ Retrieved {len(all_users)} members")

Note: Private groups hide members. For larger cohorts, spin up multiple Telegram sessions (different phone numbers) to aggregate hidden participants.

Step 5: Handle Geo-Blocks and Rate Limits

1. Geo-Blocks: GoProxy automatically routes through unrestricted regions.

2. Flood-Wait Handling

python

 

from telethon.errors import FloodWaitError

import asyncio

 

async def safe_call(coro):

    try:

        return await coro

    except FloodWaitError as e:

        wait = e.seconds + 5

        print(f"⏳ Sleeping {wait}s for rate limit")

        await asyncio.sleep(wait)

        return await coro

 

# Example usage

users = await safe_call(

    client(GetParticipantsRequest(...))

)

3. Advanced Throttling

Insert await asyncio.sleep(1) between heavy loops to mimic human pace.

Rotate between multiple .session files when bans persist.

Beginner Checklist vs. Pro Tips

For Beginners	For Professionals
Virtualenv & dependencies	Containerize in Docker/Kubernetes
Follow each code block end-to-end	Use async job queues (Celery, RQ)
Test with small limits (10–50 msgs)	Stream directly into data warehouses (Redshift)
Verify output files	Automate flood-wait handling & monitoring

Legal and Ethical Considerations

Users often worry about the legality of scraping. Here's how to approach it responsibly:

Is it legal to scrape Telegram?

Public channels and groups: Generally legal to scrape, just like reading a public blog or forum. Always double-check platform terms.

Private groups and chats: Off-limits unless you have explicit access and consent from participants.

Telegram’s Terms of Service: They prohibit abuse and spam, but don’t explicitly ban scraping public data. To stay compliant:

Avoid bulk scraping at high speed.

Use proxy rotation (like GoProxy) to avoid triggering Telegram’s limits.

What about GDPR/CCPA and data privacy?

Personal data laws apply if you're storing user identifiers (names, usernames, phone numbers).

Always anonymize or aggregate where possible.

If you're scraping for research or internal use, include a clear data retention policy.

Bottom Line

Scraping can be powerful, but use it ethically. Think about consent, purpose, and impact.

Tips for Effective Scraping

Whether you're a beginner or building a Telegram crawler for production, these tips will save you time and headaches.

1. Test Small: Start with a low limit (e.g., 10) to ensure the API, proxy, and message formats work. Then ramp up.

2. Rotate Proxies: GoProxy’s residential IP pool keeps your traffic looking human. Avoid blocks and get access even in restricted countries.

3. Handle Errors: Add try-except blocks for network hiccups:

python

 

try:

    async for msg in client.iter_messages(channel, limit=100):

        # process

except Exception as e:

    print("Scraping error:", e)

4. Monitor Usage: Use GoProxy’s dashboard to monitor bandwidth, request count, and geographic IP distribution—avoid overages.

5. Log Everything

Track:

• Channel scraped
• Number of messages
• Errors and retries

This helps debugging and audit trails.

Final Thoughts

By combining Telethon’s flexibility with GoProxy’s built-in IP rotation, you can scrape Telegram public channels and groups reliably—bypassing geo-blocks, dodging rate limits, and scaling seamlessly. Follow these steps, respect legal boundaries, and you’ll unlock the full potential of Telegram data ethically and efficiently.

Let GoProxy fuel your data projects. Try a 7-day free trial of rotating residential proxies, or scale up with unlimited traffic plans!