Abstract
In this paper, we study topic-specific retweet count ranking problem in Weibo. Two challenges make this task nontrivial. Firstly, traditional methods cannot derive effective feature for tweets, because in topic-specific setting, tweets usually have too many shared contents to distinguish them. We propose a LSTM-embedded autoencoder to generate tweet features with the insight that any different prefixes of tweet text is a possible distinctive feature. Secondly, it is critical to fully catch the meaning of topic in topic-specific setting, but Weibo can provide little information about topic. We leverage real-time news information from Toutiao to enrich the meaning of topic, as more than 85% topics are headline news. We evaluate the proposed components based on ablation methods, and compare the overall solution with a recently-proposed tensor factorization model. Extensive experiments on real Weibo data show the effectiveness and flexibility of our methods.
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- 1.
We measure the popularity of a tweet by its retweet count. As pointed out by [15, 25, 27, 28], retweet is the key mechanism for information diffusion on micro-blogging services. A larger retweet count usually means that more users have seen, and will see, the corresponding tweet and topic, and that we will further get more benefits. In fact, researchers often use popular level as the synonym of retweet count [12, 14].
- 2.
Due to space limitation, we move the features used for building Candidate Tweet Filter into the supplemental material.
- 3.
Due to space limitation, we move the user features into the supplemental material.
- 4.
- 5.
It can be used to determine if two sets of data are significantly different from each other: https://en.wikipedia.org/wiki/Student%27s_t-test.
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Acknowledgement
The authors thank the anonymous reviewers for their comments. This work was supported by the National Natural Science Foundation of China under Grant No. 61572044.
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Mao, H., Xiao, Y., Wang, Y., Wang, J., Xiao, Z. (2018). Topic-Specific Retweet Count Ranking for Weibo. In: Phung, D., Tseng, V., Webb, G., Ho, B., Ganji, M., Rashidi, L. (eds) Advances in Knowledge Discovery and Data Mining. PAKDD 2018. Lecture Notes in Computer Science(), vol 10939. Springer, Cham. https://doi.org/10.1007/978-3-319-93040-4_49
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