Name
Abstract | ||
|---|---|---|
Given the complexity of human minds and their behavioral flexibility, it requires sophisticated data analysis to sift through a large amount of human behavioral evidence to model human minds and to predict human behavior. People currently spend a significant amount of time on social media such as Twitter and Facebook. Thus many aspects of their lives and behaviors have been digitally captured and continuously archived on these platforms. This makes social media a great source of large, rich and diverse human behavioral evidence. In this paper, we survey the recent work on applying machine learning to infer human traits and behavior from social media data. We will also point out several future research directions. |
Year | Venue | Field |
|---|---|---|
2018 | arXiv: Social and Information Networks | Data science,Social media,Computer science,Artificial intelligence,Machine learning |
DocType | Volume | Citations |
Journal | abs/1804.04191 | 0 |
PageRank | References | Authors |
0.34 | 0 | 2 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Shimei Pan | 1 | 684 | 64.41 |
| Tao Ding | 2 | 15 | 8.48 |