近年來在線上社群媒體隱性關係分析的領域中，許多研究專注於使用者興趣偵測以及相似度評估。在先前的這些研究中，個人檔案資訊經常被當成分析的基礎，進而加以改良或取得其他分析資訊。然而，如此的分析基礎可能發生真實狀況與個人檔案資訊中的主觀資訊不一致的現象。或是個人檔案資訊中部分資訊是過去參與活動的紀錄資訊，例如Facebook中的頁面標籤，也可能產生與當下真實情況不一致的現象。為了避免這種不一致的情況，因此，我們希望透過客觀資訊進行研究分析。本篇研究中，我們提出一個新的共鳴關係概念，共鳴關係是藉由微網誌中使用者的互動行為以及意見來探討客觀資訊的分析，而不是以個人檔案資訊為基礎。我們利用這些會隨著時間改變的互動資訊分析後建立共鳴關係，並且模組化互動資訊。最後，呈現我們的觀察、實驗結果並且討論可能存在的問題與限制。簡言之，此篇研究我們提出一個新的方法架構分析及解決在線上社群關係研究中的潛在問題。

In the field of hidden relationship analysis of community for online social media, there are lots of researches focusing on interest detection and similarity evaluation. Among conventional studies, personal profile information (explicit data) is usually the main foundation to analyze. However, inconsistency between a real facts and subjective information written by users might occur. Thus, in order to avoid this kind of problem, we think leveraging objective data to analyze might be effective. In this paper, we proposed a new concept of resonance-relationship network by considering objective information about interactive behavior and coordinate opinion on microblogs among users in addition to user profiles. We leverage interactive and time-varying data to discover resonance-relationship, and model the distribution of interactions. Finally, we showed our observation and experiment results, and discussed some problems and restrictions. In summary, we proposed a novel model to analyze and solve potential problems for online social relationships.

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