How to Build a Simple Python Auto‑Like Bot for Web Platforms

In this article we introduce a simple Python‑based auto‑like robot designed for web platforms, explaining its core logic of simulating login and performing like actions.

The robot targets desktop web sites and requires two main steps: login simulation and like execution, which can be applied to mass‑like scenarios such as boosting others’ content or self‑boosting.

Two login approaches are described: creating many accounts and switching them, or maintaining a cookie pool obtained beforehand; the latter avoids frequent logins but must handle cookie expiration.

Sample pseudocode demonstrates reading credentials or cookies from files, calling a login function, and performing subsequent actions:

<code># 思路一
with open("users.txt","r") as f:
    user_pass = f.readline()
    # 模拟登录
    login(user_pass)
    # 完成登录后操作
    do_someting()

# 思路二
with open("cookies.txt","r") as f:
    one_cookie = f.readline()
    # 通过携带 cookie 参数访问接口
    get_detail(one_cookie)</code>

Typical like‑API endpoints for platforms like CSDN, Zhihu and Bilibili are shown, illustrating the POST request format with user identifiers, article IDs and optional CSRF tokens:

<code># POST 传递用户标识与文章 ID
Request URL: https://blog.csdn.net//phoenix/web/v1/article/like
Request Method: POST
articleId=118558076</code>

<code># 直接 POST 传递，用户标识在 Cookie 中
Request URL: https://www.zhihu.com/api/v4/zvideos/1391420717800554497/likers
Request Method: POST</code>

<code># 传递用户标识的同时，传递相应的参数
Request URL: https://api.bilibili.com/x/web-interface/archive/like
Request Method: POST
aid: 631588341
like: 1
csrf: b39b26b6b8071e2f908de715c266cb59</code>

Additional Python code shows how to send a POST request with the retrieved cookie and required parameters:

<code>import requests

def like(params):
    # 请求头中获取 Cookie 由模拟登录获取
    cookie = get_cookie()
    headers = {"其它属性":"属性值", "Cookie":cookie}
    res = requests.post("地址", "参数", "请求头")</code>

The article also discusses common challenges such as captcha solving and IP anti‑scraping measures, suggesting third‑party captcha services and proxy pools as solutions.

Finally, the author notes the ethical implications of such bots while emphasizing that understanding the implementation can help develop custom automation tools.