By analyzing this pushback, researchers have shown that the credibility of tweets could be predicted. Related data-driven analysis has also revealed that tweets with certain features are often false11. For example, the length of tweets, the sentiment of words used and the number of hashtags and emoticons used provide indicators of the likely credibility of the tweet’s messages. The same goes for tweets that include links to images and videos12 - the language contained in tweets that link to multimedia content can be used to determine whether that multimedia content is credible or not. Taken together, these data provide machines with the parameters and intelligence they need to begin predicting the accuracy of tweets and other social media content. This opens the door to a bigger role for automation in the verification process during disasters and other breaking news and emergency situations. In terms of practical applications, these findings are being used to develop a “Credibility Plugin” for Twitter13. This involves my team at the Qatar Computing Research Institute working in partnership with the Indraprastha Institute of Information Technology in Delhi, India. This plugin would rate individual tweets on a scale from 0 to 100 based on the probability that the content of a given tweet is considered credible. The plugin is expected to launch in early 2014. The main advantage of this machine computing solution is that it is fully automated, and thus more scalable than the human computing platform Verily. Hybrid computing The Artificial Intelligence for Disaster Response (AIDR) platform is a hybrid of the human and machine computing models. The platform combines human computing (microtasking) with machine computing (machine learning). Microtasking is taking a large task and splitting it into a series of smaller tasks. Machine learning involves teaching a computer to perform a specified task. AIDR enables users to teach an algorithm to find information of interest on Twitter. The teaching process is done using microtasking. For example, if the Red Cross were interested in monitoring Twitter for references to infrastructure damage following a disaster, then Red Cross staff would use AIDR’s microtasking interface to tag (select) individual tweets that refer to damage. The algorithm then would learn from this process and automatically find additional tweets that refer to damage. This hybrid computing approach can be used to automatically identify rumors based on an initial set of tweets referring to those rumors. Rapidly identifying rumors and their source is an important component of verifying user-generated content. It enables journalists and humanitarian professionals to track information back to its source, and to know whom to contact to take the next essential step in verifying the information. 81