近年來隨著社群網路服務的盛行，臉書已成為現代人最主要的社交工具，許多名人及公司企業也都搶搭著這股風潮，紛紛在臉書上建立起粉絲頁來和粉絲們互動，而在虛擬世界和現實社會之間，兩者所互相造成的影響帶動出許多新興研究議題，透過資訊技術收集虛擬世界裡的資料，能幫助人文學者與社會科學家探索出數位科技與人文社會間的新現象。 本研究針對臉書上的粉絲頁，設計建構出一套臉書資料抓取系統，以協助學者研究分析粉絲頁的動態消息資料，本系統可幫助研究者搜尋出相關粉絲頁，並依照按讚次數排列呈現，協助挑選受歡迎的粉絲頁；讓研究者能抓取特定的粉絲頁資料，抓取到的資料經過解析後分為文章訊息、留言訊息、按讚訊息，並將結果儲存至資料庫；針對已抓取的粉絲頁，自動定時更新至最新資料。 / With the popularity of social networking services in recent years, Facebook has become a major social tool for people. Many celebrities and companies have also gone with the tide to and established a fan page on Facebook to interact with fans. The mutual influence of the virtual world and the real world drives many emerging research agenda. Using information technology to collect data in the virtual world can help the humanities scholars and social scientists to explore new phenomena between digital technology and humanities community. In this thesis, we focus on Facebook fan page data. We design and construct a Facebook fan page crawler to help scholars get data for analysis. The crawler can help researchers find the relevant fan pages along with the numbers of thumbs up and it can help researchers select fan pages. The crawler can help researchers to get the fan page data which they want by extracting post messages, comment messages, and like messages from the data and then storing the results into the database. The crawler also can set update timer to help researchers get the latest information.

因大量臉書用戶留下不少行為紀錄，吸引研究人員蒐集臉書資料，從中挖掘具有價值的潛在資訊與研究成果，並用 Graph 點線關係圖視覺化呈現成果。然而透過臉書 Graph API 蒐集資料的方法，自 2.0 版起漸漸提高了限速限制，造成蒐集資料開始是一個問題。雖有研者人員提出應對方法，卻也產生用戶識別度弱化問題。故本研究動機是提出一套工具能有效協助研究人員進行臉書資料蒐集與建立關係圖以利進行分析與研究，在進行相關臉書蒐集資料與社會網絡與社群網絡分析文獻探討後，實作一種基於爬蟲技術，蒐集具用戶識別度之臉書資料；以及提出描述臉書互動行為的關係圖領域專屬語言；實作對應之使用者操作介面工具，讓使用者透過圖形化操作方式描述定義臉書粉絲頁、使用者、Hashtag、留言及回覆與貼文之間互動行為建立關係圖。經過本研究實驗設計與驗證，證明蒐集資料具識別度、產生的關係圖是正確以及問卷調查指出 75% 受試人員認同本系統可快速建立關係圖。未來可以結合爬蟲與關係圖領域專屬語言線上即時建立關係圖，以及導入更多社群分析功能，以利研究人員可以在本系統上進行更深入的分析與研究。 / The huge amount of digital footprints of Facebook users have become a good research resource and we have seen many good results developed by collecting data from Facebook and visualizing it to node-link diagram. However, there are more and more rate limits in Facebook Graph API since version 2.0. Although one could overcome the restriction of rate limits by creating more Facebook App resources, yet this makes user identification become cumbersome in analyzing the retrieved data. Therefore, the motivation of this research is to provide a set of tools to assist researchers in collecting recognizable Facebook user data with a crawler and propose a domain specific language (DSL) to build the relation graph by describing Facebook interactions. With our crawler and the DSL tool, we are able to gather unique user data successfully. The experimental results show that we can construct node-link diagram conveniently, and 75% of the surveyed subjects agreed that our tool could be helpful for building graph.