Paper Info
Title
Towards Understanding the Effectiveness of Election Related Images in Social Media
Abstract
In recent years, political campaigns have paid increasing attention to social media. During the election period, numerous election related images are posted. However, not all the images have the same effectiveness, and researchers have not investigated the intrinsic relationship between the effectiveness and the high-level visual features of social images. In this paper, we present a new study to analyze the effectiveness of election related images in social media. We first compute three semantic visual attributes for election related images: 1) face attribute, which indicates the presence of a political candidate, 2) text attribute, which describes the area of text information, 3) logo attribute, which denotes whether an image contains a campaign logo. Next, we consider the effectiveness in terms of the number of views and comments, and employ analysis of variance and association analysis to understand the importance of visual attributes in affecting the effectiveness of election related images. In addition, visual attributes distribution analysis reveals Obama campaign's deliberate effort targeting social media. The experiments on the 2012 US presidential election related images provide interesting insight that can be exploited in similar scenarios.
Year
DOI
Venue
2013
10.1109/ICDMW.2013.112
ICDM Workshops
Keywords
Field
DocType
visual attributes distribution analysis,us presidential election,social media,election period,election related images,association analysis,social image,high-level visual feature,visual attribute,semantic visual attribute,numerous election,feature extraction,election,statistical analysis
Data science,Internet privacy,Presidential election,Computer science,Obama,Artificial intelligence,Facial recognition system,Social media,Visualization,Logo,Semantics,Instrumental and intrinsic value,Machine learning
Conference
ISSN
Citations 
PageRank 
2375-9232
4
0.41
References 
Authors
18
4
Name
Order
Citations
PageRank
Junhuan Zhu1162.32
Jiebo Luo26314374.00
Quanzeng You368225.68
John R. Smith44939487.88