@inproceedings{0e3f973fa8cb4517b70688a32ce5aff9,
title = "Toward predicting popularity of social marketing messages",
abstract = "Popularity of social marketing messages indicates the effectiveness of the corresponding marketing strategies. This research aims to discover the characteristics of social marketing messages that contribute to different level of popularity. Using messages posted by a sample of restaurants on Facebook as a case study, we measured the message popularity by the number of {"}likes{"} voted by fans, and examined the relationship between the message popularity and two properties of the messages: (1) content, and (2) media type. Combining a number of text mining and statistics methods, we have discovered some interesting patterns correlated to {"}more popular{"} and {"}less popular{"} social marketing messages. This work lays foundation for building computational models to predict the popularity of social marketing messages in the future.",
keywords = "marketing, media type, prediction, social media, text categorization",
author = "Bei Yu and Miao Chen and Linchi Kwok",
year = "2011",
doi = "10.1007/978-3-642-19656-0_44",
language = "English (US)",
isbn = "9783642196553",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
pages = "317--324",
booktitle = "Social Computing, Behavioral-Cultural Modeling and Prediction - 4th International Conference, SBP 2011, Proceedings",
note = "4th International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction, SBP 2011 ; Conference date: 29-03-2011 Through 31-03-2011",
}