Paper Info
Title
Finding Missing Tweets Using Topic Structure And Browsing Time
Abstract
Microblogging services such as Twitter and Facebook become popular in recent years. In these services, many users post short messages which correspond to many topics such as daily activities, opinions, and new events. Therefore, users need a system to summarize messages if the users receive tons of messages. If the following users tweet about important things which the user does not know, these tweets should be noticed. However, which tweets should be noticed is one important problem. Users should need which topics are on their timeline. However, if the summarization method does not consider topics of tweets, the summarized tweets do not contain rarely tweeted topics. To solve this problem, we propose a method for automatically extracting missing tweets based on topic granularity and missing time of the users. In this study, we map the missing tweets to the Wikipedia category tree by considering topic structure granularity; then we present the topic structures of missing tweets using our proposed visualization interface. In our experiments, we confirmed the effectiveness of our proposed hierarchal topic structure.
Year
DOI
Venue
2017
10.1145/3151759.3151798
19TH INTERNATIONAL CONFERENCE ON INFORMATION INTEGRATION AND WEB-BASED APPLICATIONS & SERVICES (IIWAS2017)
Keywords
Field
DocType
twitter, browsing time, uninformed information
Topic structure,Data mining,Automatic summarization,Social media,Information retrieval,Computer science,Visualization,Microblogging,Timeline
Conference
Citations 
PageRank 
References 
0
0.34
9
Authors
3
Name
Order
Citations
PageRank
Yu Suzuki144.21
Hiromitsu Ohara200.68
Akiyo Nadamoto318934.24