Name
Abstract | ||
|---|---|---|
Users in social networks utilize hashtags for a variety of reasons. In many cases, hashtags serve retrieval purposes by labeling the content they accompany. More often than not, hashtags are used to promote content, ideas, or conversations producing viral memes. This paper addresses a specific case of hashtag classification: meme-filtering. We argue that hashtags that are correlated with memes may hinder many valuable social media algorithms like trend detection and event identification. We propose and evaluate a set of language-agnostic features that aid the separation of these two classes: meme-hashtags and event-hashtags. The proposed approach is evaluated on two large datasets of Twitter messages written in English and German. A proof-of-concept application of the meme-filtering approach to the problem of event detection is presented. |
Year | DOI | Venue |
|---|---|---|
2015 | 10.1007/s13278-015-0271-3 | Social Network Analysis and Mining |
Keywords | Field | DocType |
Social Medium, Event Detection, Social Network Analysis, Random Forest Classifier, Twitter User | World Wide Web,Social media,Social network,Information retrieval,Computer science,Trend detection,Social network analysis,Filter (signal processing),Random forest,German | Journal |
Volume | Issue | ISSN |
5 | 1 | 1869-5469 |
Citations | PageRank | References |
1 | 0.34 | 23 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Dimitrios Kotsakos | 1 | 8 | 0.84 |
| Panos Sakkos | 2 | 9 | 2.31 |
| Ioannis Katakis | 3 | 23 | 4.24 |
| Dimitrios Gunopulos | 4 | 7171 | 715.85 |