Knowledge acquisition from social platforms based on network distributions fitting. (October 2015)

• Record Type: Journal Article
• Authors: Jankowski, Jarosław; Michalski, Radosław; Bródka, Piotr; Kazienko, Przemysław; Utz, Sonja
• Is Part Of: Computers in human behavior. Volume 51:Part B(2015)
• Page Start: 685
• Page End: 693
• Publication Date: 2015-10
• Subjects: Social network analysis -- Network sampling -- Collaborative learning -- Adaptive surveys; Interactive computer systems -- Periodicals; Man-machine systems -- Periodicals; 004.019
• DOI: 10.1016/j.chb.2014.12.015
• Languages: English
• ISSNs: 0747-5632

Abstract

Highlights: New approach to knowledge acquisition based on social network distributions. Increased the representativeness of samples using distance from distributions. Applications in the area of collaborative learning and knowledge diffusion. Improved adaptive selection processes based on given characteristics. New algorithms following presented approach can be implemented. 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 analyzed 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.