News Monitoring Automation — Common Questions Answered

News monitoring automation lets you track mentions of brands, competitors, topics, or keywords across the web without manually checking dozens of sources every day. Whether you're a PR team tracking coverage, a trader watching market-moving headlines, or a researcher tracking industry trends, automated monitoring saves hours and catches what manual checks miss.

Key Takeaways

• Automated news monitoring uses web scraping and search APIs to continuously track mentions across news sites, blogs, and social media
• The core pipeline: define keywords, set up scheduled searches/scrapes, process and filter results, deliver alerts
• SearchHive's SwiftSearch and DeepDive APIs power monitoring pipelines with web search and content extraction
• Cost-effective monitoring can be built for under $20/mo using API-based approaches

---

What is news monitoring automation?

News monitoring automation is the process of using software to continuously scan news sources, blogs, forums, and social media for mentions of specific keywords, brands, or topics. Instead of manually checking Google News every morning, an automated pipeline runs 24/7 and alerts you when something relevant appears.

Typical monitoring pipeline:

1. Define tracking keywords — brand names, competitor names, industry terms
2. Query sources programmatically — via search APIs, RSS feeds, or direct scraping
3. Extract and normalize content — pull headlines, summaries, publish dates, source URLs
4. Filter and rank results — remove duplicates, score relevance, apply sentiment analysis
5. Deliver alerts — push notifications, email digests, Slack messages, database inserts

How do I build an automated news monitor?

Step 1: Use a search API for broad coverage

The fastest way to start monitoring is with a web search API that returns fresh results:

import requests, json, time

API_KEY = "your_api_key"
HEADERS = {"Authorization": f"Bearer {API_KEY}"}

def search_mentions(query, hours_back=24):
    response = requests.get(
        "https://api.searchhive.dev/v1/search",
        headers=HEADERS,
        params={
            "q": f"{query} news",
            "freshness": f"{hours_back}h",
            "num": 20
        }
    )
    return response.json().get("results", [])

results = search_mentions("SearchHive web scraping")
for r in results:
    print(f"[{r.get('date', 'N/A')}] {r.get('title', 'No title')}")
    print(f"  {r.get('url', '')}\n")

Step 2: Extract full article content

Search results give you headlines and snippets. For full content, scrape each article:

def get_article_content(url):
    response = requests.post(
        "https://api.searchhive.dev/v1/scrape",
        headers=HEADERS,
        json={
            "url": url,
            "extract": {
                "fields": [
                    {"name": "title", "selector": "h1"},
                    {"name": "body", "selector": "article p"},
                    {"name": "author", "selector": ".author-name"},
                    {"name": "date", "selector": "time", "attr": "datetime"}
                ]
            }
        }
    )
    return response.json()

# Chain: search then scrape
mentions = search_mentions("AI regulation 2026")
for mention in mentions[:5]:
    article = get_article_content(mention["url"])
    print(f"Title: {article.get('title', 'N/A')}")
    print(f"Length: {len(article.get('body', ''))} chars\n")

Step 3: Schedule and automate

Use a cron expression generator, GitHub Actions, or a scheduled cloud function to run your monitor at regular intervals:

# monitor.py — run via cron every 2 hours
import requests, json, smtplib
from datetime import datetime

TRACKING_QUERIES = ["SearchHive", "web scraping API", "ScrapeForge"]
SEEN_FILE = "seen_urls.json"

def load_seen():
    try:
        with open(SEEN_FILE) as f:
            return set(json.load(f))
    except FileNotFoundError:
        return set()

def save_seen(seen):
    with open(SEEN_FILE, "w") as f:
        json.dump(list(seen), f)

def run_monitor():
    seen = load_seen()
    new_mentions = []

    for query in TRACKING_QUERIES:
        results = search_mentions(query, hours_back=3)
        for r in results:
            url = r.get("url", "")
            if url and url not in seen:
                seen.add(url)
                new_mentions.append(r)

    save_seen(seen)

    if new_mentions:
        # Send alert via your preferred channel
        print(f"[{datetime.now()}] Found {len(new_mentions)} new mentions")
        for m in new_mentions:
            print(f"  - {m.get('title', 'N/A')} ({m.get('url', '')})")

if __name__ == "__main__":
    run_monitor()

What sources can I monitor?

News sites and aggregators

• Google News, Bing News, Reuters, AP News
• Industry-specific publications (TechCrunch for tech, HBR for business)
• Regional and local news outlets

Blogs and publications

• Medium, Substack, personal blogs
• Company blogs and press releases
• Industry analyst reports

Social media

• X/Twitter mentions and hashtags
• Reddit threads and comments
• LinkedIn posts

Forums and communities

• Hacker News, Product Hunt
• Industry-specific forums
• Discord/Slack communities

How much does news monitoring cost?

SearchHive at $9/mo (5K credits) can monitor 10-20 keywords with hourly checks. At $49/mo (100K credits), you can track hundreds of keywords across dozens of sources with full article extraction.

How do I filter noise and get relevant results?

Keyword-based monitoring produces noise. Here are filtering strategies:

Deduplication

Track URLs you've already seen and skip them in subsequent runs. The seen_urls.json approach shown above handles this.

Relevance scoring

Score results based on keyword proximity, source authority, and recency:

def score_relevance(result, keywords):
    title = (result.get("title") or "").lower()
    snippet = (result.get("snippet") or "").lower()
    text = f"{title} {snippet}"

    score = 0
    for kw in keywords:
        if kw.lower() in title:
            score += 10  # Title mention is high signal
        if kw.lower() in snippet:
            score += 5   # Snippet mention is moderate signal

    # Boost recent results
    if result.get("date"):
        score += 3

    return score

# Sort by relevance
ranked = sorted(new_mentions, key=lambda m: score_relevance(m, TRACKING_QUERIES), reverse=True)

Source whitelisting/blacklisting

Only include results from authoritative sources. Exclude content farms and aggregator sites that don't add value.

Can I add sentiment analysis?

Yes. Once you have article content, run it through a sentiment analysis model:

# After extracting article content
article = get_article_content(url)
body_text = article.get("body", "")

# Option 1: Use a simple keyword-based approach
negative_words = ["fail", "down", "loss", "decline", "crash", " breach"]
positive_words = ["launch", "grow", "profit", "rise", "success", "innovate"]

neg_count = sum(body_text.lower().count(w) for w in negative_words)
pos_count = sum(body_text.lower().count(w) for w in positive_words)
sentiment = "positive" if pos_count > neg_count else "negative" if neg_count > pos_count else "neutral"

# Option 2: Use an LLM for more nuanced analysis
# Send body_text to an LLM with a sentiment analysis prompt

What are the common pitfalls?

1. Over-monitoring — Too many keywords = too much noise. Start with 5-10 high-signal terms.
2. Not deduplicating — The same article appears in multiple sources. Always deduplicate by URL or content hash.
3. Ignoring freshness — Old articles resurface in search results. Filter by date.
4. No alerting discipline — If every alert is a false positive, you'll stop reading them. Tune your filters.
5. Not archiving — News articles get updated or deleted. Store content when you find it.

Summary

News monitoring automation is one of the highest-ROI automations you can build. With a search API (SwiftSearch) for discovery and a scraping API (ScrapeForge) for content extraction, you can build a custom monitor that covers exactly the sources and keywords you care about.

SearchHive combines both in one platform at a fraction of the cost of enterprise monitoring tools. Start with 500 free credits and have your first monitor running in under an hour.

Start monitoring automatically today. Get your free SearchHive API key and combine SwiftSearch + ScrapeForge into a custom news monitoring pipeline. No credit card required. Check the docs for examples.

See also: /compare/serpapi, /compare/tavily