TY - GEN
T1 - Empirical analysis of implicit brand networks on social media
AU - Zhang, Kunpeng
AU - Bhattacharyya, Siddhartha
AU - Ram, Sudha
PY - 2014
Y1 - 2014
N2 - This paper investigates characteristics of implicit brand networks extracted from a large dataset of user historical activities on a social media platform. To our knowledge, this is one of the first studies to comprehensively examine brands by incorporating user-generated social content and information about user interactions. This paper makes several important contributions. We build and normalize a weighted, undirected network representing interactions among users and brands. We then explore the structure of this network using modified network measures to understand its characteristics and implications. As a part of this exploration, we address three important research questions: (1) What is the structure of a brand-brand network? (2) Does an influential brand have a large number of fans? (3) Does an influential brand receive more positive or more negative comments from social users? Experiments conducted with Facebook data show that the influence of a brand has (a) high positive correlation with the size of a brand, meaning that an influential brand can attract more fans, and, (b) low negative correlation with the sentiment of comments made by users on that brand, which means that negative comments have a more powerful ability to generate awareness of a brand than positive comments. To process the large-scale datasets and networks, we implement MapReduce-based algorithms.
AB - This paper investigates characteristics of implicit brand networks extracted from a large dataset of user historical activities on a social media platform. To our knowledge, this is one of the first studies to comprehensively examine brands by incorporating user-generated social content and information about user interactions. This paper makes several important contributions. We build and normalize a weighted, undirected network representing interactions among users and brands. We then explore the structure of this network using modified network measures to understand its characteristics and implications. As a part of this exploration, we address three important research questions: (1) What is the structure of a brand-brand network? (2) Does an influential brand have a large number of fans? (3) Does an influential brand receive more positive or more negative comments from social users? Experiments conducted with Facebook data show that the influence of a brand has (a) high positive correlation with the size of a brand, meaning that an influential brand can attract more fans, and, (b) low negative correlation with the sentiment of comments made by users on that brand, which means that negative comments have a more powerful ability to generate awareness of a brand than positive comments. To process the large-scale datasets and networks, we implement MapReduce-based algorithms.
KW - mapreduce
KW - marketing intelligence
KW - network analysis
KW - sentiment identification
KW - social media
UR - http://www.scopus.com/inward/record.url?scp=84907403226&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84907403226&partnerID=8YFLogxK
U2 - 10.1145/2631775.2631806
DO - 10.1145/2631775.2631806
M3 - Conference contribution
AN - SCOPUS:84907403226
SN - 9781450329545
T3 - HT 2014 - Proceedings of the 25th ACM Conference on Hypertext and Social Media
SP - 190
EP - 199
BT - HT 2014 - Proceedings of the 25th ACM Conference on Hypertext and Social Media
PB - Association for Computing Machinery
T2 - 25th ACM Conference on Hypertext and Social Media, HT 2014
Y2 - 1 September 2014 through 4 September 2014
ER -