Generic Multilayer Network Data Analysis with the Fusion of Content and Structure
This addresses the problem of efficient and flexible multi-feature data analysis for social media researchers, but it appears incremental as it adapts an existing nontraditional approach.
The paper tackles the challenge of analyzing multi-feature data from platforms like Facebook by adapting multilayer network analysis to model datasets, integrate content, and answer application-based queries, showing flexibility and efficiency in experiments.
Multi-feature data analysis (e.g., on Facebook, LinkedIn) is challenging especially if one wants to do it efficiently and retain the flexibility by choosing features of interest for analysis. Features (e.g., age, gender, relationship, political view etc.) can be explicitly given from datasets, but also can be derived from content (e.g., political view based on Facebook posts). Analysis from multiple perspectives is needed to understand the datasets (or subsets of it) and to infer meaningful knowledge. For example, the influence of age, location, and marital status on political views may need to be inferred separately (or in combination). In this paper, we adapt multilayer network (MLN) analysis, a nontraditional approach, to model the Facebook datasets, integrate content analysis, and conduct analysis, which is driven by a list of desired application based queries. Our experimental analysis shows the flexibility and efficiency of the proposed approach when modeling and analyzing datasets with multiple features.