Too Lazy to Click: How I Scraped a 400-Page Forum Thread with Python

I found myself in a familiar situation for anyone deep into a technical hobby. In my case, it was game reverse engineering, specifically for Valorant. The holy grail of information was a thread on unknowncheats.me titled "Valorant Reversal, Structs and Offsets." This thread is a goldmine, with community members constantly posting updated memory offsets, data structures, and code snippets.

The problem? The thread was, at the time of writing, well over 400 pages long.

My goal was simple: I wanted to search through all the collective knowledge in that thread for specific keywords. But the manual process was a nightmare. It involved:

Opening a page.
Hitting CTRL+F to search.
Clicking the "Next Page" button.
Repeating steps 2 and 3... hundreds of times.

My patience wore thin after about ten pages. I thought, "There has to be a better way." I'm a programmer, and tedious, repetitive tasks are the very things we build tools to destroy. I felt lazy, but it was the productive kind of lazy. I decided I wasn't going to read the forum; I was going to "reverse" it and make a script do the reading for me.

Step 1: "Reversing" the Website (aka Peeking at the HTML)

The term "reversing" here is a bit dramatic. I wasn't disassembling the website's backend. I was simply doing what every web developer does: I right-clicked on a forum post and hit "Inspect Element."

My mission was to find a predictable pattern. How does the website identify each individual message? If I could find a unique, repeating identifier for the posts, I could teach a script to find them all.

I looked at the HTML structure of a post. It was nested deep in a series of <div> and <table> tags, but I quickly struck gold. Each post's main content was wrapped in an element with an ID that looked like this:

<td id="post_message_5234981">
  <!-- The entire message content is in here -->
  ...
</td>

Bingo. The pattern was post_message_ followed by a unique post number. This was the key. I could now reliably target every single message on a page.

Next, I looked at the URL.

https://www.unknowncheats.me/forum/valorant/385792-valorant-reversal-structs-offsets-380.html

The page number was right there at the end. This meant I could easily loop from page 1 to page 400+ just by changing that number in the URL. The plan was coming together.

Step 2: Building the Scraper with Python

With a clear plan, I fired up my code editor and started building the script. I chose Python for its fantastic libraries for web scraping: requests to handle the web requests and BeautifulSoup to parse the messy HTML into something manageable.

Here's the script I wrote, broken down piece by piece:

import requests
import re
from bs4 import BeautifulSoup
import json
import time
import random

# 1. Setup
data = []
starting_page_number = 380
ending_page_number = 382 # I set a small range for testing

# 2. The Main Loop
for page_number in range(starting_page_number, ending_page_number):
  print(f'Loading page {page_number}')
  url = f'https://www.unknowncheats.me/forum/valorant/385792-valorant-reversal-structs-offsets-{page_number}.html'

  # These headers can be important to mimic a real browser session
  payload={}
  headers = {
    'Cookie': 'bblastactivity=0; bblastvisit=1691742053; bbsessionhash=23eb5d9818dd9fadf6102dba29534ba1'
  }

  # 3. Fetching the Page
  response = requests.request("GET", url, headers=headers, data=payload)

  # 4. Parsing with BeautifulSoup
  soup = BeautifulSoup(response.text, 'html.parser')

  # This is where we use the pattern we found!
  elementsWithPostMessageID = soup.find_all(id=re.compile("^post_message_"))

  # 5. Extracting the Text
  for element in elementsWithPostMessageID:
      # .text strips all HTML tags, leaving just the message
      data.append(element.text)

  # 6. Being a Good Citizen
  print(f'Finished page {page_number}. Pausing...')
  time.sleep(random.randint(1,10))


# 7. Saving the Results
with open('messages.json', 'w', encoding='utf-8') as outfile:
    json.dump(data, outfile, indent=2, ensure_ascii=False)

print("Scraping complete! Data saved to messages.json")

Let's walk through it:

Setup: I initialize an empty list called data to hold all the message texts. I also define my starting and ending page numbers so I can easily control how much of the forum I scrape at a time.
The Main Loop: The script iterates through each page number I defined. In each loop, it constructs the specific URL for that page using an f-string.
Fetching the Page: Using the requests library, I send a GET request to the URL. I included a Cookie in the headers. Sometimes, forums restrict access or have different layouts for guests versus logged-in users. By providing a session cookie (copied from my browser), I ensure the script sees the page exactly as I do.
Parsing with BeautifulSoup: This is where the magic happens. I pass the raw HTML text from the response to BeautifulSoup. Then, I use the find_all() method with a regular expression: re.compile("^post_message_"). This tells BeautifulSoup: "Find every single tag on this page whose ID starts with post_message_."
Extracting the Text: The script loops through the list of elements it just found. For each element, element.text conveniently strips away all the HTML formatting (<b>, <i>, <br>, etc.) and gives me just the clean, raw text of the message. I append this text to my data list.
Being a Good Citizen: This is critical. Hammering a website with rapid-fire requests is a great way to get your IP address banned. The line time.sleep(random.randint(1,10)) pauses the script for a random interval between 1 and 10 seconds between each page load. This mimics human browsing behavior and avoids overwhelming the server.
Saving the Results: After the loop finishes, all the scraped messages are in the data list. I use Python's json library to dump this list into a file named messages.json. This gives me a clean, structured, and machine-readable archive of the entire forum thread.

The Result

Instead of hundreds of browser tabs and endless scrolling, I now have a single messages.json file. I can open it, search it instantly, or even write other scripts to analyze the data for trends. The tedious manual labor was transformed into a 10-minute coding session.

This little project was a perfect reminder that sometimes, the "lazy" way is the smart way. By investing a little time upfront to automate a task, I saved myself hours of mind-numbing work and ended up with a far more useful result.

hex @hexcreator