Name
Abstract | ||
|---|---|---|
There are two key issues for information diffusion in blogosphere: (1) blog posts are usually short, noisy and contain multiple themes, (2) information diffusion through blogosphere is primarily driven by the "word-of-mouth" effect, thus making topics evolve very fast. This paper presents a novel topic tracking approach to deal with these issues by modeling a topic as a semantic graph, in which the semantic relatedness between terms are learned from Wikipedia. For a given topic/post, the name entities, Wikipedia concepts, and the semantic relatedness are extracted to generate the graph model. Noises are filtered out through the graph clustering algorithm. To handle topic evolution, the topic model is enriched by using Wikipedia as background knowledge. Furthermore, graph edit distance is used to measure the similarity between a topic and its posts. The proposed method is tested by using the real-world blog data. Experimental results show the advantage of the proposed method on tracking the topic in short, noisy texts. |
Year | DOI | Venue |
|---|---|---|
2011 | 10.5591/978-1-57735-516-8/IJCAI11-389 | IJCAI |
Keywords | Field | DocType |
graph model,semantic graph model,wikipedia concept,semantic graph,information diffusion,blog post,topic model,novel topic tracking approach,semantic relatedness,topic evolution | Semantic similarity,Graph,Information retrieval,Computer science,Artificial intelligence,Natural language processing,Blogosphere,Topic model,Clustering coefficient,Graph model,Graph edit distance | Conference |
Citations | PageRank | References |
7 | 0.52 | 17 |
Authors | ||
5 | ||