社群媒體平台合作影片之競食效應預測

Abstract

傳統上，關於競食效應(Cannibalization)的討論通常局限於產品的範疇。 在本研究中，我們提出了一個新穎的研究任務，探討社群媒體網紅的合作影片中的競食效應預測。 本研究旨在預測社群媒體網紅邀請其他網紅嘉賓出現自己的影片中時，導致其自身未來影片的平均觀看量下降的情況，稱之為競食效應。 為了處理這個研究任務，我們提出一種新穎的深度神經網路預測模型Cannibalization Identification with Influencer Encoder (CIIE)，其利用網紅編碼器(Influencer Encoder)萃取主持網紅、特邀嘉賓網紅，以及他們過去創作內容的關鍵資訊。此外，我們也提出多種基準模型(Baseline Models)以綜合評估我們提出的CIIE 模型之整體表現，其中，包括先驗機率模型 (PPM)、有約束的先驗機率模型(CPPM)和隨機預測模型(RGM)。根據我們的實驗結果，我們提出的 CIIE 模型之在所有方法中表現最優，尤其對於預測少數類別方面表現尤其出眾，這也是我們研究的主要關注點。這項研究為我們對社群媒體網紅競食效應的理解做出了具體的貢獻，並證實我們提出的深度神經網路預測模型可以有效地預測可能發生競食效應之社群媒體網紅的合作影片。

Traditional discussion regarding cannibalization is restricted to the context of products. In this research, we present a novel research task which investigates the prediction of the cannibalization effect in the context of featured videos by social media influencers. This research aims to identify instances where the exposure of a social media influencer as a guest in another influencer’s video leads to a decline in their own video’s viewership, known as cannibalization. To address this research task, a novel deep neural network predictive model, referred to as Cannibalization Identification with Influencer Encoders (CIIE), is proposed, utilizing influencer encoders to capture essential information about both the host and guest influencers and their past video content. The model’s effectiveness is evaluated against various benchmark methods, including Prior Probabilistic Model (PPM), Constrained Prior Probabilistic Model (CPPM), and Random Guess Model (RGM). According to our evaluation results, our proposed CIIE model outperforms all benchmarks and is especially effective in the predict minority classes, which is the main focus of our study. This research contributes to a comprehensive understanding of cannibalization among social media influencers and underscores the potential of our proposed DNN model as a valuable tool for predicting possible cannibalization effects for featured videos.