整合及呈現社群網絡平台上大量社群資訊之新穎視覺化技術

Abstract

隨著Facebook、Twitter等社群網站的盛行，越來越多的人習慣每天在社群網站上與朋友們互動並且閱讀最新的資訊，當使用者的朋友數量及訂閱的資訊越來越多時，人們每天會收到數以百計的資訊，並且淹沒在這些資訊海當中，使用者往往需要花費許多的時間及精神以消化這些大量的資訊，更嚴重的是，若使用者沒有閱讀完所有的訊息就可能會遺漏重要的資訊。 為了解決這個問題，我們提出了一個全新的視覺化技術，使用者可以任意的選擇欲瀏覽的時間區段，我們提供一個以使用者為中心的個人化社群網絡，並且以此社群網絡來視覺化的呈現出這個時間區段內的所有訊息，為了增進使用者的閱讀體驗，我們的系統提供了社群偵測、朋友互動分析、朋友重要性分析，使用者可以藉此了解社群網絡上的朋友結構、朋友們之間的互動情況及互動內容、以及優先的瀏覽較有興趣或重要的資訊，我們實做了一個Facebook的應用程式並利用真實的資料證明我們的系統是可行且實際的。

Online social network services such as Facebook and Twitter have become increasingly popular. More and more users are accustomed to regularly reading the latest news feeds and interacting with friends on these social websites. However, when the numbers of friends and subscribed pages increase to a large extent, users will receive hundreds of messages in a day and will be overwhelmed by the information overload. To alleviate this problem, we propose a novel visualization technique for social news feeds summarization on large-scale social web services. The proposed system SocFeedViewer can produce an egocentric network graph based on the news feeds generated in an arbitrary period of time. This graph provides an overview of those who have generated news feeds during this time period. To enhance the reading experience, we incorporate community detection, connectivity analysis, and importance analysis into our system to make users capable of preferentially surfing news feeds that are more significant and interesting. We implement a real-world application and use the real social data of several volunteers to verify the usefulness of SocFeedViewer.