Name
Abstract | ||
|---|---|---|
On Twitter, vast numbers of tweets have been written about news articles. These tweets include not only opinions and sentiments, but also comments related to the news articles. However, tweets that include comments about news article are believed by people even if their credibility is not clear. In this way, these tweets are sometimes spread by others. Therefore, we consider the importance of raising an alarm about tweets for which the credibility is not clear. As described in this paper, as a first step of extracting tweets with unclear credibility, we propose a method to extract tweets that include commentary about news articles. In this paper, we designate the tweets as "commentary tweets". Our proposed method consists of a rule-based component and a machine learning component. We also conducted our experiments to measure the suitability of our proposed method for extracting commentary tweets. |
Year | DOI | Venue |
|---|---|---|
2017 | 10.1145/3151759.3151841 | 19TH INTERNATIONAL CONFERENCE ON INFORMATION INTEGRATION AND WEB-BASED APPLICATIONS & SERVICES (IIWAS2017) |
Keywords | Field | DocType |
Twitter, News, commentary Tweets, Clustering, SVM | Data mining,Credibility,Information retrieval,Computer science,Support vector machine,Cluster analysis | Conference |
Citations | PageRank | References |
1 | 0.36 | 7 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Keiichi Mizuka | 1 | 2 | 1.07 |
| Yu Suzuki | 2 | 4 | 4.21 |
| Akiyo Nadamoto | 3 | 189 | 34.24 |