# Globalness Detection in Online Social Network

## TL;DR

This paper introduces a novel framework for detecting globalness in online social networks, demonstrated on Facebook data, achieving high accuracy in distinguishing local from global pages and revealing insights about global node distribution.

## Contribution

It proposes a four-stage operational flow for globalness detection and applies it to Facebook data, providing a new method for understanding global versus local nodes in OSNs.

## Key findings

- High precision (89%) and recall (88%) in classifying local pages.
- Global node ratios are higher in states with large international cities.
- Several global nodes are identified and analyzed.

## Abstract

Classification problems have made significant progress due to the maturity of artificial intelligence (AI). However, differentiating items from categories without noticeable boundaries is still a huge challenge for machines -- which is also crucial for machines to be intelligent. In order to study the fuzzy concept on classification, we define and propose a globalness detection with the four-stage operational flow. We then demonstrate our framework on Facebook public pages inter-like graph with their geo-location. Our prediction algorithm achieves high precision (89%) and recall (88%) of local pages. We evaluate the results on both states and countries level, finding that the global node ratios are relatively high in those states (NY, CA) having large and international cities. Several global nodes examples have also been shown and studied in this paper. It is our hope that our results unveil the perfect value from every classification problem and provide a better understanding of global and local nodes in Online Social Networks (OSNs).