Desearch’s ‘Arxiv Chatter Digest’ Helps Researchers Find Papers Getting Attention

Hundreds of new research papers appear on arXiv every week, making it difficult for researchers and developers to keep up.

Finding recently published papers is easy, but knowing which ones are attracting attention and discussion takes more effort.

Desearch (SN22) developed Arxiv Chatter Digest, a tool that finds recent research papers on a chosen topic and checks what people are saying about them on X.

It brings together paper details, abstracts, and relevant social media posts into a single research digest. Desearch (SN22) gives developers and AI agents access to web searches, content extraction, and social media information through Bittensor.

How Arxiv Chatter Digest Works

Using Arxiv Chatter Digest, users start by entering a research topic, such as graph neural networks or large language models.

It then uses Desearch’s search API to discover relevant papers published on arXiv, following the steps below:

1. Find Papers: Search arXiv for recent publications matching the selected topic.

2. Verify Details: Check each paper’s title, authors, and publication date using the official arXiv API.

3. Retrieve Abstracts: Extract the full abstract so users can understand what each paper covers.

4. Find X Discussions: Search for posts mentioning the papers by their IDs or titles.

5. Generate a Digest: Organize the results into a CSV spreadsheet and a shorter Markdown report.

The finished report highlights relevant papers, their abstracts, the number of matching X posts found, and links to those conversations.

This helps researchers identify which papers are generating attention without manually searching through academic archives and social media.

An Example Using Graph Neural Networks

In a simulation, Arxiv Chatter Digest searched for papers about graph neural networks. The tool verified 15 papers, retrieved their abstracts, and searched X for discussions about each one. It then generated a digest highlighting the top 10 results, with publication details and links to matching posts.

The entire process cost approximately $0.06. Desearch uses a usage-based pricing model, with published reference rates of $0.40 per 1,000 AI search items, $0.50 per 1,000 page extractions, and $0.15 per 1,000 X posts.

Developers Can Run It Themselves

Arxiv Chatter Digest is available as a public Python project. Developers can install the required dependencies, connect their Desearch API key, and generate reports for their preferred research topics.

The tool searches papers from the previous 30 days by default, processes up to 15 papers, and requests up to 20 X posts per paper. These limits can be adjusted.

The project also includes sample outputs and a feature for regenerating reports from saved results without making additional API calls. 

What This Means for Desearch

Desearch (SN22) provides search infrastructure that allows developers and AI agents to retrieve information from the web and X. Arxiv Chatter Digest puts these capabilities to use by helping researchers discover new papers and follow the conversations around them.

The tool can also be used with AI agents through Desearch’s MCP integration, allowing agents to find papers, retrieve abstracts, and gather related discussions on demand.

While discussions on X do not necessarily reflect the scientific importance of a paper, they can help researchers identify emerging areas of interest. The tool shows how Desearch’s infrastructure can make research discovery easier and less time-consuming.

Useful Resources

Explore Desearch’s Ecosystem Here:

1. Desearch: https://www.desearch.ai

2. X: https://x.com/desearch_ai 

3. LinkedIn: https://www.linkedin.com/company/desearch-ai/ 

4. Arxiv Chatter Digest: https://github.com/desearch-ai/desearch-py-examples/tree/main/arxiv-chatter-digest