  Microblogging websites like Twitter have been shown to be immensely useful
for spreading information on a global scale within seconds. The detrimental
effect, however, of such platforms is that misinformation and rumors are also
as likely to spread on the network as credible, verified information. From a
public health standpoint, the spread of misinformation creates unnecessary
panic for the public. We recently witnessed several such scenarios during the
outbreak of Ebola in 2014 [14, 1]. In order to effectively counter the medical
misinformation in a timely manner, our goal here is to study the nature of such
misinformation and rumors in the United States during fall 2014 when a handful
of Ebola cases were confirmed in North America. It is a well known convention
on Twitter to use hashtags to give context to a Twitter message (a tweet). In
this study, we collected approximately 47M tweets from the Twitter streaming
API related to Ebola. Based on hashtags, we propose a method to classify the
tweets into two sets: credible and speculative. We analyze these two sets and
study how they differ in terms of a number of features extracted from the
Twitter API. In conclusion, we infer several interesting differences between
the two sets. We outline further potential directions to using this material
for monitoring and separating speculative tweets from credible ones, to enable
improved public health information.
