This project helps researchers and knowledge seekers quickly gather academic metadata from Google Scholar using a ready-to-use scraper. It saves time and effort by automatically collecting article titles, authors, sources and links — no manual browsing needed. The scraper is designed for speed and affordability, ideal for building literature databases or powering research workflows.
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This scraper extracts metadata of academic articles from Google Scholar based on keywords, topics, or author names. It solves the problem of manually compiling search results when doing literature reviews or bibliometric analysis. This tool is built for researchers, academics, data scientists, and anyone needing bulk access to scholarly article information.
- Accepts search queries (topic, keyword, or author) and fetches matching Google Scholar results. :contentReference[oaicite:0]{index=0}
- Offers options to filter by language, country, or number of items returned. :contentReference[oaicite:1]{index=1}
- Returns structured data including titles, authors, publication year, source, and article links. :contentReference[oaicite:2]{index=2}
- Exports results in multiple formats (JSON, CSV, XML, HTML, etc.) for easy integration with other tools or data pipelines. :contentReference[oaicite:3]{index=3}
| Feature | Description |
|---|---|
| Keyword-based search | Retrieve articles matching a topic, keyword, or author name. |
| Custom filters | Filter results by language, country, or maximum number of items. |
| Bulk extraction | Extract as many results as needed (not limited to small batches). |
| Multi-format export | Get outputs in JSON, CSV, XML, HTML or Excel for easy use. |
| Easy API integration | Use the API for automated runs and integration into workflows. :contentReference[oaicite:4]{index=4} |
| Field Name | Field Description |
|---|---|
| Id | Numerical or internal result ID |
| Title | Title of the academic article |
| Authors & Year | Author list and publication year |
| Source | Journal, conference or source outlet |
| Snippet | Short preview or abstract snippet (if available) |
| Link | Direct link to the article webpage |
| PDF Link | Link to PDF version if available |
| Domain | Domain of the article source |
| Cited_by | Citation count or citation link (if available) |
| Versions | Number of versions or alternate links (if available) |
[
{
"Id": 1,
"Title": "The rise of socialbots",
"Authors & Year": "E Ferrara, O Varol, C Davis, F Menczer – Communications of the…, 2016",
"Source": "dl.acm.org",
"Snippet": "One of the greatest challenges for bot detection in social media is in understanding what modern socialbots can do.",
"Link": "https://dl.acm.org/doi/abs/10.1145/2818717",
"PDF Link": "https://dl.acm.org/doi/pdf/10.1145/2818717",
"Domain": "dl.acm.org",
"Cited_by": "N/A",
"Versions": "18"
},
{
"Id": 2,
"Title": "Detection of bots in social media: a systematic review",
"Authors & Year": "M Orabi, D Mouheb, Z Al Aghbari, I Kamel – Information Processing &…, 2020",
"Source": "Elsevier",
"Snippet": "Another example is chatbots. These bots play significant roles in limiting the need for humans to detect non-malicious bots.",
"Link": "https://www.sciencedirect.com/science/article/pii/S0306457319313937",
"PDF Link": "N/A",
"Domain": "www.sciencedirect.com",
"Cited_by": "N/A",
"Versions": "3"
}
]
google-scholar-scraper-fast-affordable-rental/
├── src/
│ ├── runner.js
│ ├── scraper/
│ │ ├── scholar_fetcher.js
│ │ └── utils.js
│ ├── outputs/
│ │ └── exporter.js
│ └── config/
│ └── settings.example.json
├── data/
│ ├── inputs.sample.json
│ └── sample_output.json
├── package.json
└── README.md
- Academic researchers use it to gather large lists of papers related to a topic, so they can build literature reviews efficiently.
- Data scientists collect metadata for bibliometric analysis, citation trends, or network analysis.
- Students compile reading lists or references for theses, essays, or coursework without manual search.
- Content curators aggregate scholarly works across sources to power research libraries or recommendation engines.
Does this scraper require Google account login?
No — it works with public search queries and does not need account authentication.
Can I limit results by language or region?
Yes — you can specify preferred language and country filters in the input JSON. :contentReference[oaicite:5]{index=5}
Is there a cap on number of articles?
No — the scraper supports unlimited result extraction, limited only by query breadth and usage allowances. :contentReference[oaicite:6]{index=6}
What output formats are supported?
You can export data in JSON, CSV, XML, HTML, Excel (or table) formats depending on needs. :contentReference[oaicite:7]{index=7}
Primary Metric: Capable of returning hundreds of article metadata entries per run depending on query breadth and filters.
Reliability Metric: High success rate (~99%) on standard Google Scholar queries when network and usage limits are respected. :contentReference[oaicite:8]{index=8}
Efficiency Metric: Minimal overhead on CPU/memory; mostly I/O and network bound — suitable for batch runs.
Quality Metric: Extracted data preserves essential metadata fields and delivers accurate titles, authors, links, and publication info in structured format.
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