Tarpelite

Tarpelite / WhoCitesMe

Public

Find the bigshots who cite me

19
1
100% credibility
Found Mar 12, 2026 at 18 stars -- GitGems finds repos before they trend. Get early access to the next one.
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AI Analysis
Python
AI Summary

WhoCitesMe fetches academic paper citations from Semantic Scholar, identifies prominent senior authors, analyzes citation contexts with AI, and produces detailed Excel reports.

How It Works

1
🔍 Discover WhoCitesMe

You find this helpful tool on GitHub that shows researchers who is citing their papers and spots famous experts among them.

2
📥 Bring it home

Download the files to your computer and make copies of the simple setup guides.

3
✏️ List your papers

Jot down the titles, short names, conferences, and years of the papers you want to check.

4
🔗 Connect a smart helper

Link up an affordable AI service so it can recognize big-name researchers and understand how your work is mentioned.

5
🚀 Launch the scan

Start the process with one simple command, and it gathers all citations, identifies key people, and digs into what they say.

6
⏳ Let it work

Sit back as it pulls data step by step, saving everything safely so you can stop and restart anytime without losing progress.

📊 Enjoy your insights

Open the colorful Excel report with sheets full of citations, notable experts, summaries, and stats on your paper's impact.

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Star Growth

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AI-Generated Review

What is WhoCitesMe?

WhoCitesMe is a Python script that pulls all citations for your papers from Semantic Scholar, spots bigshots like IEEE Fellows and academicians among the citers, and uses LLMs to analyze citation contexts for positive takes or baselines. Feed it a YAML list of your publications with titles and venues, tweak an LLM key for DeepSeek or OpenAI, then run CLI steps like `python scout.py --step all` to get a four-sheet Excel report: full citations, notable ones, paper summaries, and PI directory. It turns vague "someone cited me" into structured insights on impact.

Why is it gaining traction?

Full scans beyond top-N citations, resumable caching for safe reruns, and LLM batching keep costs under $0.15 while tagging senior authors by h-index thresholds. Color-coded Excel with fellow checkmarks and quote extracts beats manual Google Scholar dives. Python devs dig the YAML configs and OpenAI-compatible flexibility for local models.

Who should use this?

ML/AI researchers checking if bigshots cite their NeurIPS/ICML papers, profs compiling citation stats for grants, or PhDs quantifying "notable" impact via PI h-indices. Ideal for anyone with 5-20 papers indexed on Semantic Scholar needing quick, exportable analysis without custom scrapers.

Verdict

With 18 stars and 1.0% credibility score, it's raw but punches above via thorough docs, MIT license, and interrupt-proof design—prototype-worthy for academics, but watch for edge cases in non-CS fields. Run a test paper first.

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