Name
Abstract | ||
|---|---|---|
An important issue that has been neglected so far is the identification of diversionary comments. Diversionary comments under political blog posts are defined as comments that deliberately twist the bloggers' intention and divert the topic to another one. The purpose is to distract readers from the original topic and draw attention to a new topic. Given that political blogs have significant impact on the society, we believe it is imperative to identify such comments. We then categorize diversionary comments into 5 types, and propose an effective technique to rank comments in descending order of being diversionary. To the best of our knowledge, the problem of detecting diversionary comments has not been studied so far. Our evaluation on 2,109 comments under 20 different blog posts from Digg.com shows that the proposed method achieves the high mean average precision (MAP) of 92.6%. Sensitivity analysis indicates that the effectiveness of the method is stable under different parameter settings. |
Year | DOI | Venue |
|---|---|---|
2012 | 10.1145/2396761.2398518 | CIKM |
Keywords | Field | DocType |
diversionary comment,new topic,effective technique,political blog post,different blog post,different parameter setting,political blogs,original topic,categorize diversionary comment,spam,topic model | Data mining,Categorization,World Wide Web,Computer science,Topic model,Politics | Conference |
Citations | PageRank | References |
6 | 0.46 | 10 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Jing Wang | 1 | 19 | 2.06 |
| Clement T. Yu | 2 | 3171 | 1419.96 |
| Philip S. Yu | 3 | 30670 | 3474.16 |
| Bing Liu | 4 | 14486 | 811.80 |
| Weiyi Meng | 5 | 2722 | 514.77 |