A Step-by-Step Guide to Scraping Bing(Beginner → Production)

Scraping search engines like Bing can unlock valuable insights for SEO, market research, and competitive analysis. While Google dominates the search landscape, Bing offers unique advantages—such as detailed image results, local business data, and less aggressive anti-scraping measures—making it an appealing target for web scrapers.

This guide explains how to scrape Bing SERPs, People Also Ask (PAA), rich snippets, images, Maps, and (carefully) chat outputs—with copy-paste scripts, proxy patterns, troubleshooting, and production guidance. First, run the organic SERP minimal example; then follow the relevant data-type section for more advanced needs.

Quick Legal Note: Always review Bing’s Terms of Service and local laws before scraping. Use scrapers responsibly, rate-limit requests, and avoid private/personal data.

How to Use This Guide

Beginner quick start: run the minimal examples under each data type (follow the numbered steps).

Intermediate: add headless rendering (Playwright) and GoProxy rotation for reliability.

Production: follow the production tweaks, monitoring thresholds, and bulk architecture notes.

Why Scrape Bing?

Bing complements Google: it often surfaces different images and local signals, returns alternative SERP features, and in many cases applies less aggressive blocking — useful for:

• Keyword & rank research (organic positions, PAA).
• Local business data (Maps: addresses, hours, ratings).
• Visual & media signals (images/video metadata).
• Data feeds for automation & ML (dashboards, trend detection).

If you only need titles/URLs → use simple requests. If you need PAA, Maps, or chat outputs → use headless browsers + geo-aware proxies.

Common Challenges & How Proxies Help

Bing's structure is scraper-friendly with consistent HTML classes like .b_algo for results. But challenges persist: IP blocking/rate limits, JS-rendered widgets, localized results, and CAPTCHAs. In 2025, AI-driven bot detection analyzes behavior beyond IPs.

Proxies mitigate these by:

Rotating IPs to spread requests and avoid per-IP throttles.

Geo-targeted IPs to retrieve local SERP/Maps variants.

Sticky sessions to preserve state for multi-step flows.

Residential pools to reduce detection risk (trade: cost and speed).

A managed rotating + geo-sticky proxy service (e.g., GoProxy) gives the reliability and geographic coverage most projects need.

Which Scraping Approach to Use?

Data type	Simple requests	Headless (Playwright/Puppeteer)	GoProxy mode	Difficulty
Organic SERP (titles/snippets)	✓	◼︎ if dynamic	rotate / geo_sticky	Low → Medium
Related queries / PAA	◼︎ sometimes	✓	geo_sticky	Medium
Rich snippets / JSON-LD	✓ (if present)	✓ if injected	rotate	Low → Medium
Images / Video	◼︎ thumbnails	✓ for lazy-load	rotate (residential recommended)	Medium
Bing Maps / Local data	◼︎ endpoint-based	✓ for UI flows	geo_sticky (local IPs)	Medium → Hard
Bing AI / Chat	✗	✓ (sticky sessions)	sticky	Hard
Bulk rank tracking	✓ (fast)	◼︎ only for features	rotate (large pool)	Medium → Hard

Legend: ✓recommended; ◼conditional.

Common Scraping Settings & YAML Config

Apply these as your baseline. Per-data-type sections override specifics.

Core checklist

User-Agent rotation (5–20 realistic UAs).

Accept-Language header matching target locale.

Proxy: use GoProxy — modes: rotate, sticky, geo_sticky.

Timeouts: connect 8–12s; read 15–25s.

Retries & backoff: 3 attempts; backoff [1s, 3s, 9s].

Rate limiting: jittered delays (random between min & max).

Cookie jar per session for stateful flows.

render_js flag: false by default; true when content depends on client JS.

CAPTCHA detection: search response for captcha|verify|are you human|recaptcha (case-insensitive).

Logging: record request URL, proxy IP, UA, response code; save raw HTML on error.

Proxy health: blacklist after 3 consecutive failures.

Geo-targeting: use proxies from the country/city for local SERPs/Maps.

Storage fields: query, page, position, title, url, snippet, raw_html_snapshot, fetched_at.

Reusable base config (YAML)

common:

  user_agents: ["UA1", "UA2", "..."]

  accept_language: "en-US,en;q=0.9"

  go_proxy:

    mode: "rotate"    # rotate | sticky | geo_sticky

    auth: "user:pass" # or token

    geo: null         # e.g. "US"

  timeouts:

    connect: 10

    read: 20

  retries:

    attempts: 3

    backoff: [1, 3, 9]

  concurrency:

    per_proxy: 1

    workers: 4

  delay:

    min: 1.2

    max: 3.5

  render_js: false

  captcha_detection: true

  logging:

    save_raw_on_error: true

Connectivity test (curl)

curl -x http://GP_USER:[email protected]:8000 'https://httpbin.org/ip' -I

Expected: 200 OK and a JSON body showing the proxy IP.

Quick Prerequisites

1. Install Python 3.10+ and pip.

2. Create virtualenv:

python -m venv venv

source venv/bin/activate   # macOS / Linux

venv\Scripts\activate      # Windows

3. Install core packages:

pip install requests beautifulsoup4 playwright

playwright install

4. Prepare GoProxy credentials: GP_USER, GP_PASS, GP_HOST. Choose rotate, sticky (e.g., ?session=abc), or geo_sticky. Just register, choose a rotating residential plan, and get it in your dashboard.

5. Create USER_AGENTS pool (file or array).

6. Place configs/common.yaml and update go_proxy.auth with your credentials or plan to paste PROXY directly into scripts for beginners.

Reusable Fetch Helper (Beginner-friendly)

Save as utils/fetcher.py. It sets up a requests.Session with retries and basic block detection. For no-proxy testing, set PROXY = None.

python

 

# utils/fetcher.py

import requests, random, logging

from requests.adapters import HTTPAdapter

from urllib3.util.retry import Retry

 

USER_AGENTS = ["Mozilla/5.0 (Windows NT 10.0; Win64; x64)...", "Mozilla/5.0 (Macintosh; Intel Mac OS X 10_15_7)..."]

# Set PROXY to None to test without proxy

PROXY = "http://GP_USER:[email protected]:8000"

 

def make_session(proxy_url=None):

    s = requests.Session()

    retries = Retry(total=3, backoff_factor=1, status_forcelist=(429, 500, 502, 503, 504))

    s.mount("https:/", HTTPAdapter(max_retries=retries))

    s.mount("http:/", HTTPAdapter(max_retries=retries))

    if proxy_url:

        s.proxies.update({"http": proxy_url, "https": proxy_url})

    return s

 

def is_blocked(resp):

    if resp.status_code in (403, 429):

        return True

    txt = resp.text.lower() if resp.text else ""

    return any(tok in txt for tok in ("captcha", "are you human", "verify", "recaptcha"))

 

def fetch_serp(session, query, first=1, timeout=(8,20)):

    headers = {

        "User-Agent": random.choice(USER_AGENTS),

        "Accept-Language": "en-US,en;q=0.9",

        "Referer": "https://www.bing.com"

    }

    url = f"https://www.bing.com/search?q={requests.utils.quote(query)}&first={first}"

    resp = session.get(url, headers=headers, timeout=timeout)

    if is_blocked(resp):

        logging.warning("Blocked or rate-limited: %s", resp.status_code)

        raise Exception("Blocked or rate-limited")

    resp.raise_for_status()

    return resp.text

Scraping Bing Organic SERP(Titles, URLs, Snippets)

Why / Expected output

Useful for rank tracking and competitor monitoring. Sample output:

[

  {"position":1,"title":"Best Wireless Earbuds 2025 - Review","url":"https://example.com/best-earbuds","snippet":"A quick review of top earbuds."},

  {"position":2,"title":"Top Earbuds for Running","url":"https://example.com/running-earbuds","snippet":"Lightweight earbuds for runners."}

]

Minimal beginner steps

1. Create scripts/serp_requests.py.

2. Paste:

python

 

from bs4 import BeautifulSoup

from utils.fetcher import make_session, fetch_serp, PROXY

 

session = make_session(PROXY)

html = fetch_serp(session, "best wireless earbuds 2025", first=1)

soup = BeautifulSoup(html, "html.parser")

results = []

for idx, li in enumerate(soup.select("li.b_algo"), start=1):

    a = li.select_one("h2 a")

    snippet = li.select_one("div.b_caption, p")

    results.append({

        "position": idx,

        "title": a.get_text(strip=True) if a else "",

        "url": a["href"] if a and a.has_attr("href") else "",

        "snippet": snippet.get_text(" ", strip=True) if snippet else ""

    })

print(results)

3. Run:

python scripts/serp_requests.py

4. Validate output matches the sample JSON..

Production tweaks

Use per-proxy concurrency = 1; queue jobs (Redis/Celery).

Save raw HTML snapshots for failed cases (≥1% sampling).

Track metrics: 403_ratio, avg_latency_ms, proxy_failure_rate.

Troubleshooting

1. 403/429: Rotate proxy, change UA, add 5–15s sleep.

2. No li.b_algo: Set render_js=true and try Playwright.

3. Locale differences: Use geo-located proxies.

Scraping Bing PAA

Why / Expected output

PAA gives keyword ideas and user intent signals. Output is a list of Q&A snippets.

Minimal steps (Playwright)

1. Create scripts/paa_playwright.py.

2. Paste:

from playwright.sync_api import sync_playwright

 

PROXY = {"server":"http://proxy.goproxy.example.com:8000","username":"GP_USER","password":"GP_PASS"}

 

def extract_paa(query):

    with sync_playwright() as p:

        browser = p.chromium.launch(proxy=PROXY, headless=True)

        page = browser.new_page()

        page.goto(f"https://www.bing.com/search?q={query}", timeout=30000)

        page.wait_for_timeout(2000)

        nodes = page.query_selector_all("div.rwrl div, div.b_accordion div")

        items = [n.inner_text().strip() for n in nodes if n.inner_text().strip()]

        browser.close()

        return items

 

print(extract_paa("best coffee shops near me"))

3. Run:

python scripts/paa_playwright.py

Production tweaks

Use geo-sticky proxies to get localized suggestions.

Cache related queries per locale; de-duplicate.

Troubleshooting

1. Empty results: Run headful mode (headless=False) to visually inspect and increase wait time.

2. If blocked: reduce rate and rotate proxies; add small random mouse movements.

Scraping Bing Rich snippets & JSON-LD

Why / Expected output

Structured data (JSON-LD) gives product, review, FAQ details directly.

Steps

1. Try requests first and extract <script type="application/ld+json">.

2. If empty but the snippet appears visually, use Playwright to render.

Beginner snippet

python

 

from bs4 import BeautifulSoup

import requests, json

PROXY = "http://GP_USER:[email protected]:8000"

 

r = requests.get("https://www.bing.com/search?q=site:example.com", proxies={"http":PROXY,"https":PROXY}, timeout=15)

soup = BeautifulSoup(r.text, "html.parser")

for s in soup.find_all("script", {"type":"application/ld+json"}):

    try:

        print(json.loads(s.string))

    except Exception:

        continue

Troubleshooting

1. If invalid JSON: log the string for manual inspection.

2. If missing: render with Playwright.

Scraping Bing Images & Video SERPs

Why / Expected output

Lists of image thumbnails, alt text, and source pages — useful for visual competitive research.

Minimal Playwright example

python

 

# scripts/images_playwright.py

from playwright.sync_api import sync_playwright

PROXY = {"server":"http://proxy.goproxy.example.com:8000","username":"GP_USER","password":"GP_PASS"}

 

with sync_playwright() as p:

    browser = p.chromium.launch(proxy=PROXY, headless=True)

    page = browser.new_page()

    page.goto("https://www.bing.com/images/search?q=coffee", timeout=30000)

    page.wait_for_selector("img.mimg", timeout=10000)

    imgs = page.query_selector_all("img.mimg")

    for im in imgs[:20]:

        print(im.get_attribute("src"), im.get_attribute("alt"))

    browser.close()

Production tips

Image downloads are bandwidth-heavy — set per-proxy bandwidth caps.

Prefer collecting metadata vs. bulk downloading full images (copyright).

Troubleshooting

If direct image fetch returns 403, request the image via the page (Playwright) so the Referer is present.

Scraping Bing Maps / Local Business Data

Why / Expected output

Place cards with name, address, phone, hours, rating.

Discovery (manual once)

1. Open bing.com/maps and search for a query + city.

2. DevTools → Network → filter XHR → find endpoints like /maps/overlaybfpr.

3. Copy the request URL and params (q, count, first, cp lat~lon).

Minimal reproduction

python

 

import requests

PROXY = "http://GP_USER:[email protected]:8000"

url = "https://www.bing.com/maps/overlaybfpr?q=coffee&count=18&first=0&cp=40.7128~-74.0060"

r = requests.get(url, proxies={"http":PROXY,"https":PROXY}, timeout=15)

print(r.status_code)

print(r.text[:1000])

Parse returned HTML fragment/JSON for place cards.

Production tips

Use geo-sticky proxies (IPs in the target city) for accurate local results.

Keep rate conservative (≤ 20 requests/IP/hour).

Troubleshooting

If empty: update cp coordinates or test from a local proxy in the target city.

Scraping Bing AI / Chat(Use Cautiously)

Important: Only for internal research or if you have explicit permission. Chat interfaces are stateful and can trigger detection quickly.

Safe workflow

1. Use Playwright with sticky session proxies.

2. Manually log in once with a persistent browser profile and save cookies.

3. Script must load cookies, keep same IP, submit prompt, wait for response, and extract results.

Troubleshooting

If session breaks: ensure sticky session + persisted cookies.

Add human-like delays and limited mouse movements to reduce detection.

Bulk Bing Rank Tracking

Work flow

1. Acquire proxy token/endpoint.

2. Create session or Playwright context with proxy.

3. Fetch page(s) → parse → save.

4. On 403/429 → mark proxy unhealthy, requeue job with exponential backoff.

Starting caps

Per-proxy concurrency = 1. Start ≤ 30 requests/IP/day for residential proxies; tune from monitoring.

Monitor

403_ratio, avg_latency_ms, proxy_failure_rate, and cost_per_1k_requests

Testing & Monitoring

Testing

1. Proxy connectivity:

curl -x http://GP_USER:[email protected]:8000 'https://httpbin.org/ip'

2. SERP smoke: python scripts/serp_requests.py (compare to sample JSON).

3. Playwright smoke: python scripts/paa_playwright.py (expect PAA items).

Metrics & Suggested Starting Points

403_ratio > 0.02 → throttle and investigate

proxy_failure_rate > 0.05 → remove proxy from pool

avg_latency_ms above baseline → check network/proxy health

Automated selector repair (advanced)

You can use an LLM to propose selector fixes, but always validate generated selectors on multiple pages before auto-applying. Example LLM prompt:

“Given this HTML fragment: <paste HTML>, suggest 2 robust CSS selectors (with confidence) to extract the result title and URL.”

Checklist Before Scaling

Proxy pool reachable and credentials validated.

UA pool rotates and Accept-Language set.

Cookie handling / sticky session for stateful flows.

Raw HTML snapshot & logging enabled.

Proxy health-check + auto-blacklist implemented.

Monitoring & alerting for block spikes in place.

Final Thoughts

Beginners get copy-paste scripts and validation tests; professionals get production thresholds, architecture, and monitoring advice. Start small, verify selectors and proxy health, then scale carefully.

Ready to try? Sign up and get a free trial of rotating residential proxies. Start your first Bing scraping today!