TY - GEN
T1 - On predicting Twitter trend
T2 - 2013 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2013
AU - Zhang, Peng
AU - Wang, Xufei
AU - Li, Baoxin
PY - 2013
Y1 - 2013
N2 - In this paper, we predict hashtag trend in Twitter network with two basic issues under investigation, i.e. trend factors and prediction models. To address the first issue, we consider different content and context factors by designing features from tweet messages, network topology, user behavior, etc. To address the second issue, we adopt prediction models that have different combinations of the two basic model properties, i.e. linearity and state-space. Experiments on large Twitter dataset show that both content and context factors can help trend prediction. However, the most relevant factors are derived from user behaviors on the specific trend. Non-linear models are significantly better than their linear counterparts, which can be further slightly improved by the adoption of state-space models.
AB - In this paper, we predict hashtag trend in Twitter network with two basic issues under investigation, i.e. trend factors and prediction models. To address the first issue, we consider different content and context factors by designing features from tweet messages, network topology, user behavior, etc. To address the second issue, we adopt prediction models that have different combinations of the two basic model properties, i.e. linearity and state-space. Experiments on large Twitter dataset show that both content and context factors can help trend prediction. However, the most relevant factors are derived from user behaviors on the specific trend. Non-linear models are significantly better than their linear counterparts, which can be further slightly improved by the adoption of state-space models.
KW - Information diffusion
KW - Trend prediction
KW - Twitter
UR - http://www.scopus.com/inward/record.url?scp=84893260877&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84893260877&partnerID=8YFLogxK
U2 - 10.1145/2492517.2492576
DO - 10.1145/2492517.2492576
M3 - Conference contribution
AN - SCOPUS:84893260877
SN - 9781450322409
T3 - Proceedings of the 2013 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2013
SP - 1427
EP - 1429
BT - Proceedings of the 2013 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2013
PB - Association for Computing Machinery
Y2 - 25 August 2013 through 28 August 2013
ER -