Mining Social Data to Extract Intellectual Knowledge

TL;DR

This paper proposes a systematic architecture for mining intellectual knowledge from social data, specifically Facebook, using advanced data mining techniques to support various applications like behavior prediction and decision making.

Contribution

It introduces a novel data mining architecture tailored for social data, integrating multiple attributes from Facebook for extracting valuable intellectual knowledge.

Findings

Successful extraction of social insights from Facebook data

Enhanced understanding of social behavior patterns

Potential applications in decision making and marketing

Abstract

Social data mining is an interesting phe-nomenon which colligates different sources of social data to extract information. This information can be used in relationship prediction, decision making, pat-tern recognition, social mapping, responsibility distri-bution and many other applications. This paper presents a systematical data mining architecture to mine intellectual knowledge from social data. In this research, we use social networking site facebook as primary data source. We collect different attributes such as about me, comments, wall post and age from facebook as raw data and use advanced data mining approaches to excavate intellectual knowledge. We also analyze our mined knowledge with comparison for possible usages like as human behavior prediction, pattern recognition, job responsibility distribution, decision making and product promoting.