Abstract
Finding high-quality users to follow is essential for acquiring information in microblogging systems. Measuring user’s quality according to its published posts is effective but also needs a large computation considering the volume and the diversity of the posts. In this paper, we explore using only the posts with URLs, i.e., a subset (\(\sim \)20 %) of the whole posts, for ranking microblog users and propose an iterative graph based ranking algorithm called UBRank to simultaneously rank users and URLs with the assumption that the importance of users and URLs can be mutually boosted. Experiments based on a Chinese microblog corpus demonstrate the effectiveness of the proposed approach.
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Notes
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Sina Weibo is one of the most popular microblogging service in China.
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Acknowledgments
This work is supported by the National Natural Science Foundation of China (grant No. 61402466 and 61572494) and the Strategic Priority Research Program of the Chinese Academy of Sciences (grant No. XDA06030200).
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Ye, Y., Li, P., Li, R., Zhou, M., Wan, Y., Wang, B. (2016). Ranking Microblog Users via URL Biased Posts. In: Cellary, W., Mokbel, M., Wang, J., Wang, H., Zhou, R., Zhang, Y. (eds) Web Information Systems Engineering – WISE 2016. WISE 2016. Lecture Notes in Computer Science(), vol 10042. Springer, Cham. https://doi.org/10.1007/978-3-319-48743-4_7
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