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2018 | OriginalPaper | Buchkapitel

Topic-Specific Retweet Count Ranking for Weibo

verfasst von : Hangyu Mao, Yang Xiao, Yuan Wang, Jiakang Wang, Zhen Xiao

Erschienen in: Advances in Knowledge Discovery and Data Mining

Verlag: Springer International Publishing

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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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Vorheriges Kapitel Personalized Item-of-Interest Recommendation on Storage Constrained Smartphone Based on Word Embedding Quantization

Nächstes Kapitel Motif-Aware Diffusion Network Inference

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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].

Due to space limitation, we move the features used for building Candidate Tweet Filter into the supplemental material.

Due to space limitation, we move the user features into the supplemental material.

http://www.paddlepaddle.org/.

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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Titel: Topic-Specific Retweet Count Ranking for Weibo
verfasst von: Hangyu Mao
Yang Xiao
Yuan Wang
Jiakang Wang
Zhen Xiao
Verlag: Springer International Publishing
Buch: Advances in Knowledge Discovery and Data Mining
Print ISBN: 978-3-319-93039-8

Electronic ISBN: 978-3-319-93040-4

Copyright-Jahr: 2018
DOI: https://doi.org/10.1007/978-3-319-93040-4_49

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