TY - GEN
T1 - Unsupervised streaming feature selection in social media
AU - Li, Jundong
AU - Hu, Xia
AU - Tang, Jiliang
AU - Liu, Huan
N1 - Publisher Copyright: © 2015 ACM.
PY - 2015/10/17
Y1 - 2015/10/17
N2 - The explosive growth of social media sites brings about massive amounts of high-dimensional data. Feature selection is effective in preparing high-dimensional data for data analytics. The characteristics of social media present novel challenges for feature selection. First, social media data is not fully structured and its features are usually not predefined, but are generated dynamically. For example, in Twitter, slang words (features) are created everyday and quickly become popular within a short period of time. It is hard to directly apply traditional batch-mode feature selection methods to find such features. Second, given the nature of social media, label information is costly to collect. It exacerbates the problem of feature selection without knowing feature relevance. On the other hand, opportunities are also unequivocally present with additional data sources; for example, link information is ubiquitous in social media and could be helpful in selecting relevant features. In this paper, we study a novel problem to conduct unsupervised streaming feature selection for social media data. We investigate how to exploit link information in streaming feature selection, resulting in a novel unsupervised streaming feature selection framework USFS. Experimental results on two real-world social media datasets show the effectiveness and efficiency of the proposed framework comparing with the state-of-the-art unsupervised feature selection algorithms.
AB - The explosive growth of social media sites brings about massive amounts of high-dimensional data. Feature selection is effective in preparing high-dimensional data for data analytics. The characteristics of social media present novel challenges for feature selection. First, social media data is not fully structured and its features are usually not predefined, but are generated dynamically. For example, in Twitter, slang words (features) are created everyday and quickly become popular within a short period of time. It is hard to directly apply traditional batch-mode feature selection methods to find such features. Second, given the nature of social media, label information is costly to collect. It exacerbates the problem of feature selection without knowing feature relevance. On the other hand, opportunities are also unequivocally present with additional data sources; for example, link information is ubiquitous in social media and could be helpful in selecting relevant features. In this paper, we study a novel problem to conduct unsupervised streaming feature selection for social media data. We investigate how to exploit link information in streaming feature selection, resulting in a novel unsupervised streaming feature selection framework USFS. Experimental results on two real-world social media datasets show the effectiveness and efficiency of the proposed framework comparing with the state-of-the-art unsupervised feature selection algorithms.
KW - Social media data
KW - Streaming features
KW - Unsupervised feature selection
UR - http://www.scopus.com/inward/record.url?scp=84958245370&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84958245370&partnerID=8YFLogxK
U2 - 10.1145/2806416.2806501
DO - 10.1145/2806416.2806501
M3 - Conference contribution
T3 - International Conference on Information and Knowledge Management, Proceedings
SP - 1041
EP - 1050
BT - CIKM 2015 - Proceedings of the 24th ACM International Conference on Information and Knowledge Management
PB - Association for Computing Machinery
T2 - 24th ACM International Conference on Information and Knowledge Management, CIKM 2015
Y2 - 19 October 2015 through 23 October 2015
ER -