Detection of missing tweets based on browsing interval and topic granularity
Hiromitu Ohara, Yu Suzuki, Akiyo Nadamoto · 2015
Twitter users who browse tweets can follow other users in whom they are interested. They can obtain interesting information from other users' tweets on their timeline. If they follow many users, then they can expect numerous tweets on their timeline. However, if users do not browse their timeline for some time, they can lose interesting and important information. Therefore, a system that automatically presents a summary of lost information can be extremely beneficial. As described herein, we propose a method of extracting lost information automatically based on a user's browsing time interval and the topic structure of a followee's tweets. First, we classify a followee's tweets that contain the user's missing information, and assign topics to the groups. Next, we generate a topic graph based on the semantic structure from Wikipedia. We decide whether the tweet groups are missed using the followee's topic graph based on the browsing time interval. Finally, we extract missing information and present it to the user.