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題名:利用社群數據建構電視收視率預測模型
作者:程美華
作者(外文):CHENG, MEI-HUA
校院名稱:輔仁大學
系所名稱:商學研究所博士班
指導教授:吳宜蓁教授
陳銘芷教授
學位類別:博士
出版日期:2017
主題關鍵詞:社群媒體電視收視率臉書TV ratingAudience measurementSocial mediaData miningFacebook
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新媒體環境興起導致電視觀眾收視行為碎片化,同時透過社群媒體評論節目內容,即時互動,加上電視收視族群高齡化;因此,傳統以樣本為基礎電視收視率調查無法涵蓋整體觀眾收視行為的調查。近年,不少學術機構或調查公司透過大數據分析工具的應用,利用社群媒體數據建構電視收視率預測模型。
本研究目的在於探索社群媒體應用於電視收視率預測模型建構的可行性。基於台灣地區民眾以臉書為主要使用之社群媒體,遂利用文字探勘技術挖掘並分析臉書數據,經由逐步迴歸,主成分分析等方式比較臉書不同數據對於電視收視率的影響,並且探索出臉書各項數據與電視節目類型和播出時間之間關連性,並建構出影響電視收視率的三項構面:觀眾參與度、觀眾評價與節目排程,其中以觀眾參與度的影響度最高,觀眾主動對於節目參與與關注,確實有助於電視收視率的提升。
本研究利用社群媒體數據建構之電視收視率模型可做為傳統電視收視率調查的互補資料,研究結果也可提供做為電視節目或內容經營策略的參考。此外,咸信本研究是第一個將社群媒體數據解析成數個構面探討電視收視率之研究,未來將進一步架構修正該模型。
Digitalization, media convergence and audience fragmentation have dramatically disrupted the business if audience measurement. In recent years, social media has become ubiquitous and important for social networking and content sharing. Television shows are now instigating social interacting online between viewers by requesting audience and engaging to simultaneous discussions about the show. Therefore, via social media, new metrics and analytical system have been developed. This study investigated the effect of social media measures in relation to the accuracy of television show’s ratings forecasts.
Based on context of Taiwan television network programs, this study collected measures for Facebook likes, shares, comments, posts for three genres of television shows (drama, political talk-shows and entertainment shows) and their Nielsen ratings over a period of twelve weeks. This social media analytics also examined the measures of App and Facebook for the television program Mission of the Queen, the first reality game show with a mobile app in Taiwan. This study applied Pearson product-moment correlation coefficient, multiple regression and sentiment analysis.
The results indicate that key social media measures positively affect TV ratings. The measures of Facebook, such as likes, shares, comments, sentiment scores, posts are positive related to TV ratings. It suggested that active audience engagement is positive correlated with TV ratings. Subsequently, this study constructed the framework of TVR which of three dimensions: engagement, audience appraisal and media scheduling. Also, based on data of Facebook, we made the TV ratings prediction model of SVR. The TV ratings prediction model are good forecasting which of MAPE were between 10 – 20 %. This implies that TV network should be motivated to invest in social media and engage their audience and analysts can use social media as a mechanism of ex ante forecasting.
This study has contributed to the research on social media and TV ratings as the first study exploring the correlations between app and Facebook measures and television ratings. This research also is the first study grouping the measures of Facebook into several factors as the predictors to TV ratings.
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