Knowledge Acquisition from Social Platforms Based on Network Distributions Fitting

TL;DR

This paper proposes a network measure distribution-based sampling method for social platforms to improve research representativeness and applicability across various online research and learning scenarios.

Contribution

It introduces a novel survey sampling approach that considers network measure distributions, enhancing representativeness beyond demographic attributes.

Findings

Effective network measure distribution fitting method

Improved sample representativeness in social network research

Potential applications in collaborative learning and candidate selection

Abstract

The uniqueness of online social networks makes it possible to implement new methods that increase the quality and effectiveness of research processes. While surveys are one of the most important tools for research, the representativeness of selected online samples is often a challenge and the results are hardly generalizable. An approach based on surveys with representativeness targeted at network measure distributions is proposed and analysed in this paper. Its main goal is to focus not only on sample representativeness in terms of demographic attributes, but also to follow the measures distributions within main network. The approach presented has many application areas related to online research, sampling a network for the evaluation of collaborative learning processes, and candidate selection for training purposes with the ability to distribute information within a social network.