Top 5 Tips for Smarter News Aggregation in 2025 with Python and RSS Feeds

Why Gathering and Deduplication Matter More Than Ever

In an age of information overload, building a smart news aggregation and deduplication system is no longer optional—it’s essential. With hundreds of news outlets pushing out real-time content, staying updated without redundancy requires a structured and automated approach.

This post explores how to leverage RSS feeds and Python to gather(News Aggregation) news efficiently and eliminate duplicates using string similarity. We’ll also discuss code-level implementation with feedparser and difflib, and explore future steps toward prioritizing and streamlining content.

What is RSS and Why Use It?

RSS: Really Simple Syndication

RSS (Really Simple Syndication) is an XML-based format that allows users to receive updates from their favorite websites automatically. Despite being a mature technology, RSS remains one of the most reliable, clean, and ad-free methods of aggregating content.

Key Features of RSS Feeds

• Automatic updates: New content gets pushed to your reader automatically.
• Centralized information: Collect articles from multiple sources into one unified feed.
• User-friendly: Easily managed through RSS readers like Feedly, Inoreader, and The Old Reader.
• Noise-free: Feeds deliver content without ads, banners, or tracking scripts.

From Crawling to RSS: A Smarter Approach

Initially, I considered using traditional web crawling, but quickly pivoted to RSS-based collection for its simplicity and structured data. RSS feeds are versatile, enabling you to parse:

• Remote RSS feed URLs
• Local .xml files
• Even raw XML strings

Parsing RSS with Python: Clean & Simple

The Python package feedparser is widely used for parsing RSS feeds. A typical feed contains fields like:

• title: The headline
• description: A short summary
• link: The article’s full URL
• published: The publication date

Example Code: RSS Parsing

import feedparser

def get_rss_news(url):
    feed = feedparser.parse(url)
    ret_lists = []
    for item in feed.entries:
        try:
            ret_lists.append([item.title, item.description])
        except:
            continue
    return ret_lists

Deduplication with difflib: A Practical Strategy

Once the news is gathered, the next step is deduplication—removing articles with similar headlines or content. We used Python’s difflib.SequenceMatcher with a threshold of 0.7 to filter out near-duplicates.

Deduplication Function

import difflib

def dup_delete(news_lists):
    new_index = -1
    for news in news_lists:
        new_index += 1
        diff_index = 0
        for diff_news in news_lists:
            diff_index += 1
            if new_index < diff_index:
                similarity = difflib.SequenceMatcher(None, news[0], diff_news[0]).ratio()
                if 0.7 < similarity:
                    del news_lists[diff_index - 1]
                    diff_index -= 1
    return news_lists

Sample Feeds and Test Results

RSS Sources

news_rss_site = [
    "https://rss.nytimes.com/services/xml/rss/nyt/World.xml",
    "https://rss.nytimes.com/services/xml/rss/nyt/US.xml",
    "http://rss.cnn.com/rss/edition.rss",
    "http://rss.cnn.com/rss/edition_world.rss"
]

Execution & Result

news_lists = []
for url in news_rss_site:
    news = get_rss_news(url)
    news_lists.extend(news)

print(len(news_lists))           # Original count
news_lists = dup_delete(news_lists)
print(len(news_lists))           # Deduplicated count
print(news_lists)

Output:

64
32
[['Video shows moment of deadl...', '...'], ...]

With this method, we reduced the dataset by roughly 50%, significantly cutting down on repetition and improving clarity.

Future Improvements: Prioritization & Ranking

While the above method removes duplicates, a more sophisticated system would:

1. Count duplicate hits
2. Assign weights or scores
3. Rank articles based on importance, recency, or frequency

Although our current implementation is still being refined, this logic paves the way for news prioritization engines—systems that surface the most relevant or widely reported stories first.

Summary: Benefits of RSS + Deduplication

News Aggregation Final Thoughts

In 2025, with the continued rise of misinformation and data overload, smart news aggregation tools are vital. By combining RSS parsing with deduplication logic, developers can build fast, efficient, and intelligent systems for content management.

The journey doesn’t stop here—next up, we’ll explore clustering, text summarization, and machine learning-based filtering to take news aggregation to the next level.

As the demand for intelligent content systems grows, combining automation with data quality becomes increasingly important. This post introduced a foundational approach using RSS and Python for gathering and deduplicating news articles.

But this is just the beginning. Future enhancements could include semantic clustering, keyword extraction, and even AI-driven summarization to build smarter, leaner, and more insightful platforms.

Whether you’re building a media monitoring tool, a market intelligence dashboard, or a custom RSS reader, the principles of clean data and clarity remain essential. Stay tuned—we’ll explore how to integrate NLP and ML next.