Name
Abstract | ||
|---|---|---|
This paper shows a topic trend on a P2P based Social Network Service. There is a text-based Social Network Service (SNS) named Mastodon. Mastodon is a peer-to-peer and open-source SNS. Many persons and companies run Mastodon instances. We consider that there is a topic trend for each node. In this paper, we collect text messages and infer topic trend on a Mastodon instance using Latent Dirichlet Allocation(LDA). The understanding a topic trend helps to choice an instance that a user should participate. |
Year | DOI | Venue |
|---|---|---|
2017 | 10.1007/978-3-319-65521-5_105 | ADVANCES IN NETWORK-BASED INFORMATION SYSTEMS, NBIS-2017 |
Field | DocType | Volume |
World Wide Web,Latent Dirichlet allocation,Social media,Computer science,Computer network,Social network service | Conference | 7 |
ISSN | Citations | PageRank |
2367-4512 | 0 | 0.34 |
References | Authors | |
3 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Masaki Kohana | 1 | 31 | 14.06 |
| Hiroki Sakaji | 2 | 30 | 17.97 |
| Akio Kobayashi | 3 | 4 | 5.73 |
| Shusuke Okamoto | 4 | 65 | 28.98 |