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Table 1 Results using k-means clustering with cosine similarity (S1)

From: Using semantic clustering to support situation awareness on Twitter: the case of world views

  S1: k-means clustering with cosine similarity
D1: Paddington C1.1.1: Run, Unable, Follow
C1.1.2: Station, Tube, @DailyMirror
C1.1.3: Closed, Breaking, Jump, Threatening
D2: Boston C1.2.1: Continued, Crossed, Finish, Line
C1.2.2: Arrested, @BostonGlobe, Terror
C1.2.3: Eludes, Shuts, Hunt
D3: Ivory Coast C1.3.1: Terrorist, @News_Executive, Seaside
C1.3.2: Guns, Machine, Gunmen, @DailyMirror
C1.3.3: Witnesses, Way, @AFP