Name
Abstract | ||
|---|---|---|
Online trending topics represent the most popular topics among users in certain online community, such as a country community. Trending topics in one community are different from others since the users in the community may discuss different topics from other communities. Surprisingly, almost 90 % of trending topics are diffused among multiple online communities, so it shows peoples interests in a certain community can be shared to others in another community. The aim of this research is to predict the scale of trending topic diffusion among different online communities. The scale of diffusion represents the number of online communities that a trending topic diffuses. We proposed a diffusion scale prediction model for trending topics with the following four features, including community innovation feature, context feature, topic feature, and rank feature. We examined the proposed model with four different machine learning in predicting the scale of diffusion in Twitter Trending Topics among 8 English-speaking countries. Our model achieved the highest prediction accuracy (80.80 %) with C4.5 decision tree. |
Year | Venue | Field |
|---|---|---|
2016 | PKAW | Data science,Decision tree,Data mining,World Wide Web,Online community,Computer science |
DocType | Citations | PageRank |
Conference | 0 | 0.34 |
References | Authors | |
9 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Do Hyeong Kim | 1 | 3 | 1.42 |
| Soyeon Caren Han | 2 | 30 | 11.67 |
| Sungyoung Lee | 3 | 2932 | 279.41 |
| Byeong Ho Kang | 4 | 541 | 72.76 |