3 research outputs found
Data-driven Computational Social Science: A Survey
Social science concerns issues on individuals, relationships, and the whole
society. The complexity of research topics in social science makes it the
amalgamation of multiple disciplines, such as economics, political science, and
sociology, etc. For centuries, scientists have conducted many studies to
understand the mechanisms of the society. However, due to the limitations of
traditional research methods, there exist many critical social issues to be
explored. To solve those issues, computational social science emerges due to
the rapid advancements of computation technologies and the profound studies on
social science. With the aids of the advanced research techniques, various
kinds of data from diverse areas can be acquired nowadays, and they can help us
look into social problems with a new eye. As a result, utilizing various data
to reveal issues derived from computational social science area has attracted
more and more attentions. In this paper, to the best of our knowledge, we
present a survey on data-driven computational social science for the first time
which primarily focuses on reviewing application domains involving human
dynamics. The state-of-the-art research on human dynamics is reviewed from
three aspects: individuals, relationships, and collectives. Specifically, the
research methodologies used to address research challenges in aforementioned
application domains are summarized. In addition, some important open challenges
with respect to both emerging research topics and research methods are
discussed.Comment: 28 pages, 8 figure
Information Fusion in a Multi-Source Incomplete Information System Based on Information Entropy
As we move into the information age, the amount of data in various fields has increased dramatically, and data sources have become increasingly widely distributed. The corresponding phenomenon of missing data is increasingly common, and it leads to the generation of incomplete multi-source information systems. In this context, this paper’s proposal aims to address the limitations of rough set theory. We study the method of multi-source fusion in incomplete multi-source systems. This paper presents a method for fusing incomplete multi-source systems based on information entropy; in particular, by comparison with another method, our fusion method is validated. Furthermore, extensive experiments are conducted on six UCI data sets to verify the performance of the proposed method. Additionally, the experimental results indicate that multi-source information fusion approaches significantly outperform other approaches to fusion