Abstract
Our social media experience is no longer limited to a single site. We use different social media sites for different purposes and our information on each site is often partial. By collecting complementary information for the same individual across sites, one can better profile users. These profiles can help improve online services such as advertising or recommendation across sites. To combine complementary information across sites, it is critical to understand how information for the same individual varies across sites. In this study, we aim to understand how two fundamental properties of users vary across social media sites. First, we study how user friendship behavior varies across sites. Our findings show how friend distributions for individuals change as they join new sites. Next, we analyze how user popularity changes across sites as individuals join different sites. We evaluate our findings and demonstrate how our findings can be employed to predict how popular users are likely to be on new sites they join.
Original language | English (US) |
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Pages (from-to) | 83-89 |
Number of pages | 7 |
Journal | Information Fusion |
Volume | 28 |
DOIs | |
State | Published - Mar 1 2016 |
Keywords
- Cross-media study
- Cross-site information fusion
- Cross-site user analysis
- Friendship analysis
- Popularity analysis
ASJC Scopus subject areas
- Software
- Information Systems
- Signal Processing
- Hardware and Architecture