在病毒行銷中尋找有影響力的節點

Abstract

在傳統的行銷行為中，高昂的成本和效果的不確定性是常見的問題。我們發現對於一般人來說，由顧客所撰寫的線上產品使用心得通常比廠商的廣告更可信，尤其當這些心得是由他們的朋友們所寫的時候。社群網路的力量也使得這些產品形象就像真實的病毒一樣以驚人的速度傳播。發現那些撰寫有價值的產品評論、並且擁有廣泛人際關係的特定評論者們會是一個解決行銷上不確定性問題的好方法。 在本研究中，我們使用兩個方式來衡量每位評論者的行銷價值：改良的PMI和RFM。PMI可以量化每篇評論探勘的結果，RFM則被用來把每位評論者的寫作情況納入影響力分數計算之中。人工智慧技術中的類神經網路被使用來為我們的模型訓練合適的網路架構。影響力的指標：信任機制被使用在模型的評估上。它包含了真實世界中數以萬計的人際關係網路。實驗結果顯示我們的模型在選擇具有影響力的評論者上比「人氣作者」和「評論分數」的排序方法有更佳的效果。本研究能指出哪些評論者在產品資訊的傳遞上是有效的，這份結果也對欲進行行銷活動的廠商具有參考的價值。

High cost and uncertain effects are main problems of traditional marketing behaviors. We discover online product reviews, which are written by customers are usually more trustworthy than firms’ advertisements for people, especially those written by their friends. The power of social network also makes these product impressions spread in amazing speed as real viruses. To discover influential reviewers who write valuable product reviews and have wide human relationships is a good way to solve the problem of marketing behaviors. In this research, we propose two methods to measure the marketing value of each reviewer: revised PMI and RFM. The modified PMI quantifies each review mining result and the RFM concept is used to take each reviewer’s writing status into consideration of influence calculating. The artificial neural network (ANN) is adopted to train an appropriate network structure for our model. The influence power indicator: trust is applied in the evaluation of our model and it considers thousands of human relationships among the real world. Experiment result shows that our model outperforms “popular author” and “review rating” methods in selecting influential reviewers. This research can point out which reviewers are really effective in product information spreading and the results will be valuable for companies to refer.