今日，了解每天發生的新聞已經成為我們生活中重要的事情。而新聞報紙或是網站的功能就是幫助使用者了解每天發生的新聞，而新聞編輯者會經過挑選將重要的新聞放在頭版上。但是每天的新聞數量往往很龐大，但僅僅有少數的重要新聞能被放到頭版上。所以說如何使用適當的資訊和方法讓電腦如新聞編輯者去自動判斷重要的新聞，更進一步能幫助讀者了解當天重要的新聞，這個議題就顯得頗為重要。我們研究的目標是如何讓系統自動為各個類別挑選當天重要的新聞並加以排名。在這篇論文中，我們利用專業衡量新聞價值的標準來進行我們的研究，將這些標準套用至社群、新聞與類別之間互相影響的關係中。過往的研究對於判斷各個類別底下的重要新聞，僅僅只用分類器將新聞分類而並未有更多的探討，但是我們認為新聞與類別之間不僅僅只是單純分類的關係，就算是屬於同類別的新聞，應該要能夠區分他們對類別重要性的不同。在我們的研究觀察下，更進一步發現不同類別之間的新聞可以透過部落格社群的表現而彼此之間也會受到相互影響。為此我們提出三個重要特徵值類別相關度(category relevance)、部落格關注力(bloggers’ attention)以及突發影響力(bursty influence)，並建立一個社群、新聞與分類相互影響傳遞之模組,並且提出一個跨類別社群影響傳遞 (Cross-Category Social Influence Propagation, C-SIP) 方法將模組裡各個互相影響傳遞的關係結合，替各個類別找尋可能為重要的新聞並加以排名。最後我們方法的驗證方式採用TREC 2010 中Blog Track的資料，實驗顯示我們的方法在不同類別中均有較佳的表現，並且比TREC 2010 Blog Track 最佳表現的參賽者高出了9.94%的效能表現。

Nowadays, people receive information of the news stories not only from newspapers but also from online news websites. They search important news stories in order to know what happen today. However, it is hard to browse all the news stories published on a day. It is necessary to identify which news stories are more newsworthy on the specific day. In this paper, we investigate how to automatically identify the importance of news stories for different categories on a specific day by utilizing the influence propagation among communities and categories. In particular, we build an influence propagation model which consists of three features: category relevance, bloggers’ attention and bursty influence. Based on this influence propagation model, we propose a Cross-Category Social Influence Propagation (C-SIP) approach for scoring the importance of news stories on a specific day. For the feature category relevance, we measure the strength of relation between the categories and the news stories. We utilize category relevance to indicate the importance of the news stories for the categories. For the feature bloggers’ attention, the blogosphere are specific communities in which users express their attention on various news stories. We utilize the knowledge of the bloggers from the blogosphere to indicate the importance of the news stories on a specific day. Furthermore, we utilize the feature bursty influence to enhance our scoring approach. We evaluate our approach by using the judgment of Story Ranking Task in TREC 2010 Blog Track. The experiment shows our approach attains a prominent performance in the retrieval of important news stories and gets 9.94% improvement over the best performance of participating systems in TREC 2010 Blog Track.

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