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Identifying interesting Twitter contents using topical analysis
Min Chul Yang
, Hae Chang Rim
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Corresponding author for this work
Research output
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Contribution to journal
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Article
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peer-review
88
Citations (Scopus)
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Keyphrases
Twitter
100%
Tweets
100%
Twitter Content
100%
Topical Analysis
100%
Latent Topics
66%
Retweet
33%
Wide Audience
33%
Temporal Trends
33%
Social Media
33%
Forwarding Scheme
33%
Information Propagation
33%
Topic Identification
33%
User-generated Information
33%
Personal Interest
33%
Personal Satisfaction
33%
Retweet Count
33%
Interestingness
33%
Shared Content
33%
Social Media Platforms
33%
Information Overload
33%
Latent Dirichlet Allocation
33%
Text Stream
33%
Novel Topic
33%
Retweeting
33%
Computer Science
Experimental Result
100%
Baseline Method
100%
Temporal Modeling
100%
Social Medium Platform
100%
Latent Dirichlet Allocation
100%
Personal Interest
100%