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Filtering spam in Weibo using ensemble imbalanced classification and knowledge expansion | IEEE Conference Publication | IEEE Xplore

Filtering spam in Weibo using ensemble imbalanced classification and knowledge expansion


Abstract:

Weibo has become an important information sharing platform in our daily life in China. Many applications utilize Weibo data to analyze hot topic and opinion evolution pat...Show More

Abstract:

Weibo has become an important information sharing platform in our daily life in China. Many applications utilize Weibo data to analyze hot topic and opinion evolution patterns to gain insights into user behavior. However, various spam messages degrade the performance of these applications and thus are essential to be filtered. In this paper, we propose a unified spam detection approach, which utilizes external knowledge sources to expand keywords features and applies an ensemble under-sampling based strategy to handle the class-imbalance problem. The experimental results show the effectiveness and robustness of our approach in Weibo data.
Date of Conference: 27-29 May 2015
Date Added to IEEE Xplore: 27 July 2015
ISBN Information:
Conference Location: Baltimore, MD, USA

References

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