隨著網路及行動裝置的普級，民眾可以隨時輕鬆地在網路上散佈與獲取資訊。不過網路上的資訊內容往往過於雜亂，導致我們花費太多時間在吸收瑣碎的訊息，反而太晚得知甚至錯過了重要的訊息。因此，我們希望訊息爆發前就能事先得知以便提早採取行動。此類的問題在生活中有許多相關的應用，如推薦系統、內容過濾、疾病預防、市場行銷等等，近年來也有越來越多的人員投入這塊領域的研究。在本論文，我們提出一個基於王等人設計的模型並進一步結合角色特徵的新方法，在Twitter資料集上預測熱門主題標籤，其中資料集包含595,460個使用者及14,607個最終大小不小於50的主題標籤。實驗結果顯示角色特徵的資訊對級聯預測問題是有幫助的。

With ubiquitous networks and mobile devices, people can easily spread and access information on the Internet at any time. But information on the Internet is often too messy, causing us to spend too much time on the absorption of trivial messages, yet too late to know or even missed the important messages. For this reason, we hope to know beforehand the outbreak of news in order to take early action. This kind of problem has many related applications in life, such as recommendation system, content filtering, disease prevention, marketing, etc. In recent years, more and more people have been involved in research in this field. In this paper, we propose a new method based on the model designed by Wang et al. and further combine the role features. We predict popular hashtags on a Twitter dataset that contains 595,460 users and 14,607 hashtags with final size no less than 50. The experiment results show that the role features information is helpful for the cascade prediction problems.

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