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| Content Provider | ACM Digital Library |
|---|---|
| Author | Kim, Daehoon Kim, Daeyong Jo, Minho Hwang, Eenjun Kim, Siwan |
| Abstract | Due to the unprecedented popularity of social network services (SNSs), such as Twitter and Facebook, means that a huge number of user documents are created and shared constantly via SNSs. Given the volume of user documents, browsing documents in a selective manner based on personal interests is a time-consuming and laborious task. Therefore, in the case of Twitter, trend keyword lists are provided for the user's convenience. However, it is still not easy to determine the details based on a few simple keywords. The keywords usually relate to the hot issues at any time so many documents will contain pertinent details, such as news on the Internet. Thus, to provide detailed information about an issue, it is necessary to identify relationships among them. In this study, we developed a SNS-based issue detection and related news summarization scheme. To evaluate the effectiveness of our scheme, we implemented a prototype system and performed various experiments. We present some of the results. |
| Starting Page | 1 |
| Ending Page | 7 |
| Page Count | 7 |
| File Format | |
| ISBN | 9781450326445 |
| DOI | 10.1145/2557977.2558089 |
| Language | English |
| Publisher | Association for Computing Machinery (ACM) |
| Publisher Date | 2014-01-09 |
| Publisher Place | New York |
| Access Restriction | Subscribed |
| Subject Keyword | News summarization Issue summarization Trending issue News trend Sns analysis |
| Content Type | Text |
| Resource Type | Article |
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