# Scraping and Clustering Techniques for the Characterization of Linkedin Profiles

arXiv: https://arxiv.org/abs/1505.00989 · HTML: https://mlsanity.com/paper/1505.00989/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 5 May 2015.

## Built on by (most cited first, 1 of 1)

- [Multi-generational labour markets: data-driven discovery of multi-perspective system parameters using machine learning](https://mlsanity.com/paper/2302.10146/) (2023)

## Abstract (arXiv)

The socialization of the web has undertaken a new dimension after the emergence of the Online Social Networks (OSN) concept. The fact that each Internet user becomes a potential content creator entails managing a big amount of data. This paper explores the most popular professional OSN: LinkedIn. A scraping technique was implemented to get around 5 Million public profiles. The application of natural language processing techniques (NLP) to classify the educational background and to cluster the professional background of the collected profiles led us to provide some insights about this OSN's users and to evaluate the relationships between educational degrees and professional careers.