In February 2016, World Health Organization declared the Zika outbreak a Public Health Emergency of International Concern. With developing evidence it can cause birth defects, and the Summer Olympics coming up in the worst affected country, Brazil, the virus caught fire on social media. In this work, use Zika as a case study in building a tool for tracking the misinformation around health concerns on Twitter. We collect more than 13 million tweets -spanning the initial reports in February 2016 and the Summer Olympics -regarding the Zika outbreak and track rumors outlined by the World Health Organization and Snopes fact checking website. The tool pipeline, which incorporates health professionals, crowdsourcing, and machine learning, allows us to capture health-related rumors around the world, as well as clarification campaigns by reputable health organizations. In the case of Zika, we discover an extremely bursty behavior of rumor-related topics, and show that, once the questionable topic is detected, it is possible to identify rumor-bearing tweets using automated techniques. Thus, we illustrate insights the proposed tools provide into potentially harmful information on social media, allowing public health researchers and practitioners to respond with a targeted and timely action.

People regularly use web search engines to investigate the efficacy of medical treatments. Search results can contain documents that present incorrect information that contradicts current established medical understanding on whether a treatment is helpful or not for a health issue. If people are influenced by the incorrect information found in search results, they can make harmful decisions about the appropriate treatment. To determine the extent to which people can be influenced by search engine results, we conducted a controlled laboratory study that biased search results towards correct or incorrect information for 10 different medical treatments. We found that search engine results can significantly influence people both positively and negatively. Importantly, study participants made more incorrect decisions when they interacted with search results biased towards incorrect information than when they had no interaction with search results at all. For search domains such as health information, search engine designers and researchers must recognize that not all non-relevant information is the same. Some non-relevant information is incorrect and potentially harmful when people use it to make decisions that may negatively impact their lives.

Social media's unfettered access has made it an important venue for health discussion and a resource for patients and their loved ones. However, the quality of the information available, as well as the motivations of its posters, has been questioned. This work examines the individuals on social media that are posting questionable health-related information, and in particular promoting cancer treatments which have been shown to be ineffective (making it a kind of misinformation, willful or not). Using a multi-stage user selection process, we study 4,212 Twitter users who have posted about one of 139 such "treatments", and compare them to a baseline of users generally interested in cancer. Considering features capturing user attributes, writing style, and sentiment, we build a classifier which is able to identify users prone to propagate such misinformation at an accuracy of over 90%, providing a potential tool for public health officials to identify such individuals for preventive intervention. A. Ghenai & Y. Mejovaimposed on often profit-seeking websites, social media provides a dynamic forum for propagating possible medical misinformation [31]. Recent rise in vaccine hesitancy has been linked to an active movement on Twitter, promoting conspiratorial thinking and mistrust in the government [65]. Image sharing platforms such as Flickr and Instagram have become battlegrounds between the pro-anorexia movement and physicians attempting to intervene [13,92]. Uncertainty surrounding infectious disease outbreaks, such as the Zika epidemic of 2016, yielded rumors and speculations about its causes, preventive measures, and consequences [23,34].In this study we turn to the individuals sharing questionable medical information on Twitter, in particular cancer treatments which have been medically proven to be ineffective. Having around 336 million monthly active users in the first quarter of 2018 1 , Twitter is one of the largest social media websites expressly dedicated to the sharing of information, including that on cancer. Compiling hundreds of thousands of tweets on 139 queries spanning acupuncture, cinnamon, reflexology, and vitamin C, we apply strict selective criteria employing human/organization classification [61], name dictionaries, usage thresholds, and crowdsourced relevance refinement resulting in 4,212 users, which we then compare to those mentioning cancer in general from a previous study [70]. Employing previous research on rumor detection, we characterize these users in multi-faceted feature spaces, encompassing user attributes, linguistic style, sentiment, and post timing. We find users who have a more sophisticated language, who are interested in cancer, but who are not personally involved with the illness. We build a logistic regression model which, out of Twitter users mentioning cancer, is able to identify those who will eventually post a piece of misinformation with a high level of accuracy.Misinformation on social media is an urgent issue, and even more so in the health field. This paper is one of th...

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Personal user groups. For each characteristic a box plot (excluding outliers outside 90th percentile) is shown with median values under the title. Differences in medians are tested using Mann-Whitney U test, for which p-values, Bonferroni adjusted for multiple hypothesis testing, are shown on the corresponding lines spanning the two variables being compared: p < 0.