Topic-Specific Retweet Count Ranking for Weibo
| Autor: | Yang Xiao, Jiakang Wang, Hangyu Mao, Yuan Wang, Zhen Xiao |
|---|---|
| Rok vydání: | 2018 |
| Předmět: |
Information retrieval
Tensor factorization Computer science Microblogging Headline 02 engineering and technology Distinctive feature Autoencoder Prefix 020204 information systems 0202 electrical engineering electronic engineering information engineering Leverage (statistics) 020201 artificial intelligence & image processing Social media |
| Zdroj: | Advances in Knowledge Discovery and Data Mining ISBN: 9783319930398 PAKDD (3) |
| DOI: | 10.1007/978-3-319-93040-4_49 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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