TY - JOUR
T1 - Using time-sensitive interactions to improve topic derivation in twitter
AU - Nugroho, Robertus
AU - Zhao, Weiliang
AU - Yang, Jian
AU - Paris, Cecile
AU - Nepal, Surya
PY - 2017/1/1
Y1 - 2017/1/1
N2 - Twitter has become one of the most popular social media platforms, widely used for discussion and information dissemination on all kinds of topics. As a result, both business and academics have researched methods to identify the topics being discussed on Twitter. Those methods can be employed for a number of applications, including emergency management, advertisements, and corporate/government communication. However, deriving topics from this short text based and highly dynamic environment remains a huge challenge. Most current methods use the content of tweets as the only source for topic derivation. Recently, tweet interactions have been considered for improving the quality of topic derivation. In this paper, we propose a method that considers both content and interactions with a temporal aspect to further improve the quality of topic derivation. The impact of the temporal aspect in user/tweet interactions is analyzed based on several Twitter datasets. The proposed method incorporates time when it clusters tweets and identifies representative terms for each topic. Experimental results show that the inclusion of the temporal aspect in the interactions results in a significant improvement in the quality of topic derivation comparing to existing baseline methods.
AB - Twitter has become one of the most popular social media platforms, widely used for discussion and information dissemination on all kinds of topics. As a result, both business and academics have researched methods to identify the topics being discussed on Twitter. Those methods can be employed for a number of applications, including emergency management, advertisements, and corporate/government communication. However, deriving topics from this short text based and highly dynamic environment remains a huge challenge. Most current methods use the content of tweets as the only source for topic derivation. Recently, tweet interactions have been considered for improving the quality of topic derivation. In this paper, we propose a method that considers both content and interactions with a temporal aspect to further improve the quality of topic derivation. The impact of the temporal aspect in user/tweet interactions is analyzed based on several Twitter datasets. The proposed method incorporates time when it clusters tweets and identifies representative terms for each topic. Experimental results show that the inclusion of the temporal aspect in the interactions results in a significant improvement in the quality of topic derivation comparing to existing baseline methods.
KW - topic derivation
KW - temporal aspect in twitter
KW - joint matrix factorization
UR - http://purl.org/au-research/grants/arc/LP120200231
UR - http://purl.org/au-research/grants/arc/DP140101369
U2 - 10.1007/s11280-016-0417-x
DO - 10.1007/s11280-016-0417-x
M3 - Article
AN - SCOPUS:84990855031
SN - 1573-1413
VL - 20
SP - 61
EP - 87
JO - World Wide Web
JF - World Wide Web
IS - 1
ER -