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Table 1 Characteristics of Tweets about Ebola

From: Misinformation and the US Ebola communication crisis: analyzing the veracity and content of social media messages related to a fear-inducing infectious disease outbreak

 Full Data SetData Set Without Jokes
Descriptive QualitiesFrequency (N)Frequency (N)
Tweet Interpreted as a joke21% (653)N/A
Tweet Contains News Headline7% (204)8% (204)
Tweet Shares True Information31% (953)38% (941)
Tweet Shares Half-true Information/ Misrepresents the truth4% (128)5% (120)
Tweet Shares False Information4% (134)5% (125)
Unable to ascertain the Truth in Tweet12% (365)15% (363)
Tweet Shares an Opinion42% (1318)52% (1286)
Tweet Designed to Promote Discord/ Evoke a Response22% (696)28% (689)
Political Content
 Content of Tweet Political in Nature21% (644)25% (625)
 Sentiments in Support of Gov<  1% (11)< 1% (11)
 Sentiments in Opposition of Gov11% (352)14% (343)
Risk Frames
 Tweet Contains Risk Elevating Message35% (1077)42% (1045)
 Tweet Contains Risk Minimizing Message12% (365)14% (355)
Ebola Specific Content
 Tweet Shares Sentiments Related to Health60% (1863)72% (1768)
 Tweet Mentions Medical Counter Measures2% (71)3% (64)
 Tweet Mentions Fatal Nature of Ebola7% (213)8% (200)
 Tweet Mentions the Spread of the Outbreak30% (929)35% (854)
 Tweet Mentions the Reduction of the Outbreak4% (109)4% (107)
 Tweet Mentions Travel Ban/Closing Border2% (70)3% (70)
 Tweet Mentioned Quarantine/Isolation3% (104)4% (102)
 Tweet Mentioned Screen/ Fever Check at Airports1% (31)1% (30)
 Tweet Mentioned Public Health Monitoring1% (38)2% (38)
 Percentage of Tweets Mentioning at Least One of Prior Categories44% (1365)61% (1267)
Ebola Rumors
 Tweets that Mention a Rumor7% (227)8% (205)
 Tweets that Refute a Rumor1% (45)2% (43)
Number of Tweets31132460
  1. Table 1: The full dataset (n = 3113 tweets) contained all included tweets related to Ebola. The dataset without jokes (n = 2460) excluded all tweets coded as jokes to further focus analysis on Ebola-specific tweet content.