DocumentCode :
3589412
Title :
Brand posts on Sina Weibo: Predicting popularity
Author :
Yi Xu ; Zhenya Tang
Author_Institution :
Dept. of Commun. Eng., Shanghai Univ., Shanghai, China
fYear :
2014
Firstpage :
1
Lastpage :
5
Abstract :
Sina Weibo has become one of the most popular sites in China. Attracted by the huge commercial potential on Sina Weibo, many companies create their own Weibo accounts to enhance their online influence. Companies deliver information and interact with customers through placing brand posts. In this paper, some features are extracted from properties of brand posts and evaluate the roles of these features in predicting the popularity of brand posts. The reposts count of a brand post is regarded as an important indicator of online popularity because it reveals both the public attention and the spread of a post. Linear regression is used to figure out the relationship between the selected features and the reposts counts. In our experiments, it is possible to predict ranges of popularity on Sina Weibo with an overall 81% accuracy.
Keywords :
feature extraction; marketing data processing; regression analysis; social networking (online); Sina Weibo; brand posts; feature extraction; linear regression; online popularity indicator; Advertising; Companies; Feature extraction; Linear regression; Media; Mobile handsets; Prediction algorithms; brand posts; enterprise account; micro-blogging;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Information Technology and Electronic Commerce (ICITEC), 2014 2nd International Conference on
Print_ISBN :
978-1-4799-5298-4
Type :
conf
DOI :
10.1109/ICITEC.2014.7105559
Filename :
7105559
Link To Document :
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