Build a News Aggregator with Google News (2026 Guide)

Thirdwatch's Google News Scraper makes news-aggregator building a structured workflow — topic queries, RSS-feed extraction, per-publisher attribution, entity-tagged headlines. Built for news-aggregator builders, content-curation platforms, industry-research dashboards, and brand-monitoring functions that need broad publisher coverage.

Why build a news aggregator with Google News

News aggregation is a perennial product category. According to Google's 2024 News statistics, Google News indexes 50K+ publishers across 35 languages, making it the broadest single source of editorial-quality content on the public web. For news-aggregator builders, content-curation platforms, and industry-research dashboards, Google News is the cheapest broad-publisher data layer available.

The job-to-be-done is structured. A news-aggregator startup ingests 100K+ articles daily across 200 topic queries. A content-curation SaaS surfaces niche-specific headlines for vertical communities. An industry-research dashboard shows daily news for 50 industry verticals. A B2B brand-monitoring function tracks press coverage on 100 client brands. All reduce to topic + entity query batches + per-article extraction + dedup clustering.

How does this compare to the alternatives?

Three options for news-aggregator data:

Per-publisher RSS aggregation is unscalable past 20-30 publishers. NewsAPI and NewsCatcher offer comparable coverage but at higher unit cost. The Google News Scraper actor page gives you the broadest cross-publisher coverage at the lowest unit cost.

How to build an aggregator in 4 steps

Step 1: How do I authenticate against Apify?

Sign in at apify.com (free tier, no credit card), open Settings → Integrations, and copy your personal API token:

export APIFY_TOKEN="apify_api_xxxxxxxxxxxxxxxx"

Step 2: How do I pull a topic-watchlist daily?

Pass topic + region queries.

import os, requests, datetime, json, pathlib

ACTOR = "thirdwatch~google-news-scraper"
TOKEN = os.environ["APIFY_TOKEN"]

TOPICS = ["payments fintech", "ai regulation", "climate policy",
          "biotech funding", "saas acquisition", "cybersecurity breach",
          "supply chain", "energy policy"]

resp = requests.post(
    f"https://api.apify.com/v2/acts/{ACTOR}/run-sync-get-dataset-items",
    params={"token": TOKEN},
    json={"queries": TOPICS, "country": "us", "language": "en",
          "maxResults": 50},
    timeout=900,
)
records = resp.json()
ts = datetime.datetime.utcnow().strftime("%Y%m%d-%H")
pathlib.Path(f"snapshots/news-{ts}.json").write_text(json.dumps(records))
print(f"{ts}: {len(records)} articles across {len(TOPICS)} topics")

8 topics × 50 articles = up to 400 articles hourly.

Step 3: How do I dedupe across publishers covering the same story?

Cluster by title-similarity + published-time proximity.

import pandas as pd
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.metrics.pairwise import cosine_similarity

df = pd.DataFrame(records)
df["published_at"] = pd.to_datetime(df.published_at)

# Window-bucket by 6-hour intervals
df["bucket"] = (df.published_at.astype(int) // (6 * 3600 * 1_000_000_000))

vectorizer = TfidfVectorizer(stop_words="english", ngram_range=(1, 2))
clusters = []
for bucket, grp in df.groupby("bucket"):
    if len(grp) < 2:
        clusters.extend([(i, idx) for i, idx in enumerate(grp.index)])
        continue
    X = vectorizer.fit_transform(grp.title.fillna(""))
    sim = cosine_similarity(X)
    seen = set()
    cluster_id = 0
    for i, idx_i in enumerate(grp.index):
        if idx_i in seen:
            continue
        cluster_id += 1
        clusters.append((cluster_id, idx_i))
        seen.add(idx_i)
        for j, idx_j in enumerate(grp.index):
            if i != j and sim[i, j] > 0.5 and idx_j not in seen:
                clusters.append((cluster_id, idx_j))
                seen.add(idx_j)

cluster_df = pd.DataFrame(clusters, columns=["cluster_id", "row_idx"])
df = df.merge(cluster_df, left_index=True, right_on="row_idx")
print(f"{df.cluster_id.nunique()} clusters from {len(df)} articles")

Cosine similarity above 0.5 with 6-hour temporal proximity catches most cross-publisher duplicates.

Step 4: How do I serve the aggregator via Postgres?

Persist clustered articles for downstream serving.

import psycopg2

with psycopg2.connect(...) as conn, conn.cursor() as cur:
    for _, a in df.iterrows():
        cur.execute(
            """INSERT INTO news_articles
                  (url, title, snippet, publisher, published_at,
                   topic, cluster_id, scraped_at)
               VALUES (%s,%s,%s,%s,%s,%s,%s, now())
               ON CONFLICT (url) DO UPDATE SET
                 cluster_id = EXCLUDED.cluster_id,
                 scraped_at = now()""",
            (a.url, a.title, a.snippet, a.publisher,
             a.published_at, a.searchString, int(a.cluster_id))
        )

print(f"Persisted {len(df)} articles into news_articles")

The (url) primary key keeps the table dedup-clean across snapshot runs.

Sample output

A single Google News article looks like this. Five rows weigh ~5 KB.

{
  "title": "Stripe acquires payments startup for $1.1B",
  "url": "https://www.bloomberg.com/news/articles/2026-04-27/...",
  "snippet": "Stripe announced today the acquisition...",
  "publisher": "Bloomberg",
  "published_at": "2026-04-27T13:30:00Z",
  "thumbnail_url": "https://lh3.googleusercontent.com/..."
}

url is the canonical natural key. publisher enables tier-segmentation. published_at enables time-bucket clustering. snippet provides ~200 chars of preview text.

Common pitfalls

Three things go wrong in news-aggregator pipelines. Republish-pollution — many low-quality aggregators republish wire-service content (AP, Reuters) under their own bylines; for tier-quality control, maintain a publisher allowlist or require domain-authority threshold. Time-zone drift — published_at may be in publisher's local timezone vs UTC; normalize to UTC before time-bucket clustering. Title-rewrites for SEO — different publishers rewrite the same story with different titles for SEO competition; this complicates cosine-similarity clustering at low thresholds.

Thirdwatch's actor uses a lightweight HTTP path so you pay only for the data, not for proxy or compute overhead. Pair Google News with Twitter Scraper to catch breaking-news signal before formal publication. A fourth subtle issue worth flagging: Google News' regional indexing varies — a story breaking in EU may take 2-4 hours to appear in US Google News index even though the story is globally relevant; for time-sensitive aggregators (especially financial-news), supplement Google News with publisher-direct RSS feeds for the top-20 priority publishers in each region. A fifth pattern unique to news-aggregator work: paywalled publishers (NYT, WSJ, FT, Bloomberg) appear in Google News with snippets but their full content requires subscription; for aggregator products with consumer audiences, label paywalled vs free articles explicitly so users don't click and hit paywalls without warning. A sixth and final pitfall: the same url occasionally changes over time (publishers add tracking parameters, migrate CMS) — for stable cross-snapshot dedup, normalize URLs by stripping query parameters before treating as primary key. A seventh and final pattern worth flagging for production teams: data-pipeline cost optimization. The actor's pricing scales linearly with record volume, so for high-cadence operations (hourly polling on large watchlists), the dominant cost driver is the size of the watchlist rather than the per-record fee. For cost-disciplined teams, tier the watchlist (Tier 1 hourly, Tier 2 daily, Tier 3 weekly) rather than running everything at the highest cadence — typical 60-80% cost reduction with minimal signal loss. Combine tiered cadence with explicit dedup keys and incremental snapshot diffing to keep storage and downstream-compute proportional to new signal rather than total watchlist size. This is the difference between a lean research pipeline and one that quietly burns ten times the budget for the same actionable output. An eighth subtle issue worth flagging: snapshot-storage strategy materially affects long-term pipeline economics. Raw JSON snapshots compressed with gzip typically run 4-8x smaller than uncompressed; for multi-year retention, always compress at write-time. For high-frequency snapshots, partition storage by date prefix (snapshots/YYYY/MM/DD/) to enable fast date-range queries and incremental processing rather than full-scan re-aggregation. Most production pipelines keep 90 days of raw snapshots at full fidelity + 12 months of derived per-record aggregates + indefinite retention of derived metric time-series — three retention tiers managed separately.

Related use cases

• Scrape Google News for brand monitoring
• Track industry news with Google News RSS
• Monitor competitor press coverage with Google News
• All Thirdwatch use-case guides