Open Access. Powered by Scholars. Published by Universities.®
Social and Behavioral Sciences Commons™
Open Access. Powered by Scholars. Published by Universities.®
- Discipline
Articles 1 - 2 of 2
Full-Text Articles in Social and Behavioral Sciences
Using Online Controlled Experiments To Examine Authority Effects On User Behavior In Email Campaigns, Lim Kwan Hui, Ee-Peng Lim, Binyan Jiang, Achananuparp Palakorn
Using Online Controlled Experiments To Examine Authority Effects On User Behavior In Email Campaigns, Lim Kwan Hui, Ee-Peng Lim, Binyan Jiang, Achananuparp Palakorn
Research Collection School Of Computing and Information Systems
Authority users often play important roles in a social system. They are expected to write good reviews at product review sites; provide high quality answers in question answering systems; and share interesting content in social networks. In the context of marketing and advertising, knowing how users react to (quails and messages from authority senders is important, given the prevalence of email in our everyday life. Using a real-life academic event, we designed and conducted an online controlled experiment to determine how email senders of different types of authority (department head, event organizer and a general email account) affect the range …
Using Support Vector Machine Ensembles For Target Audience Classification On Twitter, Siaw Ling Lo, Raymond Chiong, David Cornforth
Using Support Vector Machine Ensembles For Target Audience Classification On Twitter, Siaw Ling Lo, Raymond Chiong, David Cornforth
Research Collection School Of Computing and Information Systems
The vast amount and diversity of the content shared on social media can pose a challenge for any business wanting to use it to identify potential customers. In this paper, our aim is to investigate the use of both unsupervised and supervised learning methods for target audience classification on Twitter with minimal annotation efforts. Topic domains were automatically discovered from contents shared by followers of an account owner using Twitter Latent Dirichlet Allocation (LDA). A Support Vector Machine (SVM) ensemble was then trained using contents from different account owners of the various topic domains identified by Twitter LDA. Experimental results …