{"title":"Beyond the tweets: Discovering factors that influence TV series preferences from ubiquitous social networks","authors":"Tipajin Thaipisutikul, Suppawong Tuarob","doi":"10.1109/UMEDIA.2017.8074106","DOIUrl":null,"url":null,"abstract":"This study examines the effects of audience messages on social media towards television series' popularity. Based on social media activities surrounding specific television programs, we investigate how audience messages in social media are associated with the popularity of selected TV series. We determine a set of influencing factors, by investigating the trend of aggregated social media data and then use Pearson correlation to find the strength in the relationship between key variables and television rating. Our analysis reveals the potential factors in predicting the trend of the TV series. Also, our findings provide new perspectives to understand the effect of emotional audience messages on TV popularity.","PeriodicalId":440018,"journal":{"name":"2017 10th International Conference on Ubi-media Computing and Workshops (Ubi-Media)","volume":"34 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2017-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2017 10th International Conference on Ubi-media Computing and Workshops (Ubi-Media)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/UMEDIA.2017.8074106","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 2
Abstract
This study examines the effects of audience messages on social media towards television series' popularity. Based on social media activities surrounding specific television programs, we investigate how audience messages in social media are associated with the popularity of selected TV series. We determine a set of influencing factors, by investigating the trend of aggregated social media data and then use Pearson correlation to find the strength in the relationship between key variables and television rating. Our analysis reveals the potential factors in predicting the trend of the TV series. Also, our findings provide new perspectives to understand the effect of emotional audience messages on TV popularity.