Why football fans follow football players on Twitter? This study base on Hyperpersonal( Walther , 1996 ) and Use and Gratifications theory to find out that why football fans want to follow football players on Twitter, how they follower football players on Twitter, include how often and which Twitter function they used. Also the research want to know that after following, how much gratifications do football fans get. In this paper, the survey has been completed online by 492 football fans. 275 of them have followed football players on Twitter. The research find out that football fans follow football player¡¦s Twitter may experience greater levels of intimacy, which higher than follow football players¡¦ new from news media. Besides, follow football players on Twitter could get more gratifications like instrumentalgratifications and entertaining gratifications those compare with follow football players¡¦ news from news media. And all of the gratifications have strong connection with information and topicalitymotive.

Facebook自從在台灣推出以來，已有超過一千三百萬的使用者帳號，是最熱門的社群網站，其中蘊含了龐大的使用者資料。從使用者學歷、工作經歷和喜歡的粉絲頁中可以一定程度上地判斷出使用者的背景與喜好，若能利用分析過的資訊將使用者分群，以供交友或導向到可能喜歡的粉絲頁，就能開發潛在客戶進而掌握商機。 本研究旨在完成一個線上系統，透過Facebook上可供擷取個人的資料：學歷、工作經歷以及喜歡的粉絲頁等資訊，針對這些量化過的資訊，經Kmeans將使用者分群分類，藉以作為協同過濾式推薦。目前實驗結果將有效個人資料4417筆進行分群，以使用者喜歡的粉絲頁比例(本研究整合成48種)加上工作經歷與學歷，最終分成10群，以作為交叉推薦之憑據和延伸研究。研究過程分實驗組與對照組，實驗組是本研究推薦的10筆粉絲頁，也就是使用者與所屬群集質心比例相差較多的粉絲頁類型；對照組則是選取使用者與母體中有較多比例差距的10筆，以證明本研究的推薦模型有效。 最後由使用者針對兩組推薦結果進行滿意度評分之比較，總共收回使用者回饋68筆，實驗組與對照組的平均推薦滿意度分數分別為0.5743、0.4268，對兩者作信心水準為95%的t檢定，結果為有充分證據支持實驗組大於對照組，可證明本研究對於推薦準確性的幫助，達成本研究目的。 由此實驗可以確定在Facebook上以使用者屬性為基礎的粉絲頁與人脈推薦是有意義與價值的，也說明真實數據能應用在社群網站的研究。希冀本研究的結果能帶動其他社群網站研究朝使用真實數據去分析佐證，讓社群網站的研究結果能更貼近使用者的真實行為。 / Facebook is one of the most popular social websites in Taiwan, and it has over 13 million accounts with lots of user data. One can tell a user’s background and preference by his education, work experience, and preferred fan pages. If we direct the right user to the right fan pages by analyzing information and clustering users through recommendation or personal connections, we will be able to reach potential customers and to further business opportunities. The goal of this study is to complete an online system to assume collaborative fan page recommendation. Base on users’ education degree, work experience and preferred fan pages, users’ background. Then use the Kmeans algorithm to cluster quantified personal information to recommend fan pages and social relationships. Currently, the result of the experiment shows 10 clusters, which contain 4417 users, and we use it as a foundation of crossing recommendation. To prove the effect of this study, we divide study into two groups, an experimental group and control group. The former one represents recommended top 10 fan pages that include the fan page types with highest difference of percentage between user’s attributes and cluster centroid; the latter one represents top 10 fan pages that include the fan page types with highest difference of percentage between users’ attributes and proportion respectively. Finally, we use users score satisfaction for each group to compare. There are 68 pieces of feedback, and the average satisfaction scores of the experimental group and the control group are 0.5743 and 0.4268, respectively. On both a confidence level of 95% for t-test, the result shows there is more sufficient evidence to support the satisfaction of experimental group than the control group. We can prove accuracy for recommendation to achieve the goal in this study. This experiment determines not only the fan page recommendation based on user attributes on Facebook is meaningful and valuable, but also shows real data can be used in social networking studies. We hope the results of this study can lead other social networking studies to analyze with real users’ data in order to make future study on social networking better reflect real users’ behavior.

近年來電腦科學的進步只能以一日千里來形容，不管在軟體或是硬體方面都有驚人的發展，軟體方面有網際網路Web 2.0技術的興盛及普及，使得人們在分享及交流資訊更加快速且便利，硬體方面則有數位相機和有照相功能智慧型手機的發明，造就了分享資訊很快的從文字模式演變成影音、相片等多媒體模式。Flickr社群網站為目前網路世界裡最重要的相片分享平台，每個人都可以將生活中擁有喜、怒、哀、樂情緒的相片上傳至該網站上與他人分享，而且此網站平台也提供下標籤功能，讓上傳者可以更正確的傳達要分享的情感。如當相片被加註上快樂的標籤，也就代表上傳者對這張相片當時的環境情緒反應為愉快、或甚至於興奮，相反地；當相片被加註上生氣的標籤，就表示該相片給上傳者的情緒反應是不愉快的、或甚至於憤怒。當同一區域(如城市)透過大量情感標籤的累積，自然而然就會呈現出該區域的情感輪廓。 　　情緒議題的研究近年來在各知識領域中已被廣泛的討論著，但針對區域性的情緒表現之研究探討似乎還不多。本研究藉由Flickr社群網站的全球性特質，結合Derudder and Taylor兩位學者於2005年提出的「The cliquishness of world cities」研究報告，定義出41個商務活動頻繁城市作為本研究的研究範圍，並應用Flickr社群網站上強大又完整的API介面功能，撰寫Client端程式擷取這些城市在Flickr網站上有加註情緒標籤的相片數共761,854張、其相關的標籤數有21,569,593個，再經由本研究提出的研究方法及步驟，逐一處理這些各城市相片上傳者所加註的大量標籤，就可以找出每個城市各情感象限數量最多的前30個標籤當作顯著標籤。 　　最後本研究綜合分析從Flickr網站上取得的大量城市、相片、及顯著標籤相關資料，分別計算出每個城市正負向情感象限的強度百分比，再以正向情感象限強度百分比為基準，定義出這些商務活動頻繁城市的「快樂指數」數值；並利用社會網絡分析軟體NodeXL來觀察各城市、情感性標籤與顯著標籤所呈現的網絡關係。 / In recent years, the computer science progress is extremely fast, whether in software or hardware has an alarming growth. The software aspect has the Internet Web 2.0 technology prosperity and popular, causes the people in share and exchange information are faster and convenient. The hardware aspect has the digital cameras and the smartphones invention, causes the share information from the writing pattern to the multimedia patterns very quickly. The Flickr social website is the most important of shared photograph in the network world for currently,everyone can shared the joy, anger, sadness, happy mood photograph by uploading to this website. This website platform also provides the tagging function, lets the uploader can more correct transmission their emotion. When the identical region (such as a city) through a large number of emotional labels cumulatively, naturally will be showing the emotional contours of the region. Emotional issues have been widespread discussion in various area of knowledge in recent years, but research the performance of emotion for region seems not much. This research because of Flickr social website global special characteristic, combined Derudder and Taylor two scholars to propose "The cliquishness of world cities" research reports in 2005, Defines 41 economics and trade activity frequent city to take this research the study scope. Finally, this research made a comprehensive analysis by a large number of cities, photos, and significant label information from the Flickr website, and calculates the percentage of each city to the strength of positive and negative emotions quadrant.Then the percentage of positive emotional intensity as a benchmark quadrant, Defines these economics and trade activity frequent city's "happiness index".