Table 1 Characteristics of Tweets about Ebola
Full Data Set | Data Set Without Jokes | |
|---|---|---|
Descriptive Qualities | Frequency (N) | Frequency (N) |
Tweet Interpreted as a joke | 21% (653) | N/A |
Tweet Contains News Headline | 7% (204) | 8% (204) |
Tweet Shares True Information | 31% (953) | 38% (941) |
Tweet Shares Half-true Information/ Misrepresents the truth | 4% (128) | 5% (120) |
Tweet Shares False Information | 4% (134) | 5% (125) |
Unable to ascertain the Truth in Tweet | 12% (365) | 15% (363) |
Tweet Shares an Opinion | 42% (1318) | 52% (1286) |
Tweet Designed to Promote Discord/ Evoke a Response | 22% (696) | 28% (689) |
Political Content | ||
Content of Tweet Political in Nature | 21% (644) | 25% (625) |
Sentiments in Support of Gov | < 1% (11) | < 1% (11) |
Sentiments in Opposition of Gov | 11% (352) | 14% (343) |
Risk Frames | ||
Tweet Contains Risk Elevating Message | 35% (1077) | 42% (1045) |
Tweet Contains Risk Minimizing Message | 12% (365) | 14% (355) |
Ebola Specific Content | ||
Tweet Shares Sentiments Related to Health | 60% (1863) | 72% (1768) |
Tweet Mentions Medical Counter Measures | 2% (71) | 3% (64) |
Tweet Mentions Fatal Nature of Ebola | 7% (213) | 8% (200) |
Tweet Mentions the Spread of the Outbreak | 30% (929) | 35% (854) |
Tweet Mentions the Reduction of the Outbreak | 4% (109) | 4% (107) |
Tweet Mentions Travel Ban/Closing Border | 2% (70) | 3% (70) |
Tweet Mentioned Quarantine/Isolation | 3% (104) | 4% (102) |
Tweet Mentioned Screen/ Fever Check at Airports | 1% (31) | 1% (30) |
Tweet Mentioned Public Health Monitoring | 1% (38) | 2% (38) |
Percentage of Tweets Mentioning at Least One of Prior Categories | 44% (1365) | 61% (1267) |
Ebola Rumors | ||
Tweets that Mention a Rumor | 7% (227) | 8% (205) |
Tweets that Refute a Rumor | 1% (45) | 2% (43) |
Number of Tweets | 3113 | 2460 |