Activity-based Twitter sampling for content-based and user-centric prediction models
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- 作者：Somayyeh Aghababaei ; Masoud Makrehchi
- 关键词：Twitter sampling ; Temporal prediction models ; Historical timelines ; User activity ; Activity ; based sampling
- 刊名：Human-centric Computing and Information Sciences
- 出版年：2017
- 出版时间：December 2017
- 年：2017
- 卷：7
- 期：1
- 全文大小：1896KB
- 刊物主题：Computer Systems Organization and Communication Networks; Communications Engineering, Networks; Information Systems and Communication Service; Information Systems Applications (incl.Internet); User In
- 出版者：Springer Berlin Heidelberg
- ISSN：2192-1962
- 卷排序：7
文摘
Increasingly more applications rely on crowd-sourced data from social media. Some of these applications are concerned with real-time data streams, while others are more focused on acquiring temporal footprints from historical data. Nevertheless, determining the subset of “credible” users is crucial. While the majority of sampling approaches focus on individual static networks, dynamic user activity over time is usually not considered, which may result in activity gaps in the collected data. Models based on noisy and missing data can significantly degrade in performance. In this study, we demonstrate how to sample Twitter users in order to produce more credible data for temporal prediction models. We present an activity-based sampling approach where users are selected based on their historical activities in Twitter. The predictability of the collected content from activity-based and random sampling is compared in a content-based and user-centric temporal model. The results indicate the importance of an activity-oriented sampling method for the acquisition of more credible content for temporal models.