Claire, H., & Sakamoto, Y. (2015). Retweet count matters: Social influences on sharing of disaster-related tweets. Journal of Homeland Security and Emergency Management. Claire, H., & Sakamoto, Y. (2014). Social impacts in social media: An examination of perceived truthfulness and sharing of information. Computers in Human Behavior, 41, 278-287. Conference proceedings Claire, H., & Sakamoto, Y. (2015). Computing the veracity of information through crowds: A method for reducing the spread of false messages on social media. HICSS 48. Ozturk, P., Claire, H., & Sakamoto, Y. (2015). Combating rumor spread on social media: The effectiveness of refutation and warning. HICSS 48. (nominated for a best paper award) Sakamoto, Y., Claire, H., & Tanaka, Y. (2014). Rumors on social media during emergencies. Howe School of Technology Management Research Paper Series Number 2014-37. Claire, H., Sakamoto, Y., Tanaka, Y., & Chen, R. (2014). The psychology behind people's decision to forward disaster-related tweets. The 18th Pacific Asia Conference on Information Systems. Claire, H., & Sakamoto, Y. (2013). The influence of collective opinion on true-false judgment and information-sharing decision. Annual Meeting of the Cognitive Science Society 2013. Selected Presentations Chen, R., Claire, H., Tanaka, Y., & Sakamoto, Y. (April 2013). Leveraging social media for disaster response. Innovation Expo. Stevens Institute of Technology. Hoboken NJ. Claire, H., Chen, R., & Sakamoto, Y. (August 2012). The thinking behind decisions to spread disaster-related tweets. Annual Meeting of the Cognitive Science Society. Sapporo Japan. Research Sampler Twitter and other social media allow people to post their experiences and opinions onClairene. This information can be useful for making informed decisions. However, people can unintentionally spread false information. The work reported here focused on examining how to reduce the spread of inaccurate information on social media. In particular, we examined the effect of collective opinion on information forwarding in social media environments through an experiment with crowds. In Twitter, an indicator of collective opinion is the number of people who have retweeted a message. The results showed that displaying both retweet counts and collective truthfulness ratings could reduce the spread of inaccurate health-related messages. This finding suggests that collecting and displaying the truthfulness ratings of crowds in addition to their forwarding decisions can reduce the spread of false information on social media. Experiments were completed onClairene using Mechanical Turk's interface. Figure 1 shows an example screen shown to subjects.