Objectives. To use crowdfunding campaigns to better understand how cannabidiol (CBD) is represented (and misrepresented) as cancer-related care.Methods. We analyzed CBD-related crowdfunding campaigns (n = 155) created between January 2017 and May 2019 in multiple countries on GoFundme.com.Results. More than 81.9% of campaigns fundraised CBD for curative or life-prolonging reasons, and 25.2% fundraised for pain management.Conclusions. Most campaigns seeking funds for CBD for cancer-related care on GoFundMe are for curative or life-prolonging purposes and present CBD definitively as an effective treatment option. In general, campaigners supported their funding requests with anecdotal claims of efficacy and referenced sources of information that were either not evidence-based or that misrepresented existing evidence.Public Health Implications. Misinformation around CBD for cancer is widespread on medical crowdfunding campaigns. Given the potential adverse impact, crowdfunding platforms, like GoFundMe, must take steps to address their role in enabling and spreading this misinformation.Objectives. To provide a comprehensive workflow to identify top influential health misinformation about Zika on Twitter in 2016, reconstruct information dissemination networks of retweeting, contrast mis- from real information on various metrics, and investigate how Zika misinformation proliferated on social media during the Zika epidemic.Methods. We systematically reviewed the top 5000 English-language Zika tweets, established an evidence-based definition of “misinformation,” identified misinformation tweets, and matched a comparable group of real-information tweets. We developed an algorithm to reconstruct retweeting networks for 266 misinformation and 458 comparable real-information tweets. We computed and compared 9 network metrics characterizing network structure across various levels between the 2 groups.Results. There were statistically significant differences in all 9 network metrics between real and misinformation groups. Misinformation network structures were generally more sophisticated than those in the real-information group. There was substantial within-group variability, too.Conclusions. Dissemination networks of Zika misinformation differed substantially from real information on Twitter, indicating that misinformation utilized distinct dissemination mechanisms from real information. Our study will lead to a more holistic understanding of health misinformation challenges on social media.Objectives. To compare how human papillomavirus (HPV) vaccination was portrayed on Pinterest before and after the platform acted to moderate vaccine-related search results to understand (1) what the information environment looked like previously and (2) whether Pinterest’s policy decisions improved this environment in terms of sources and content.Methods. In this quantitative content analysis, we compared 2 samples of 500 HPV vaccine-focused Pinterest posts (“pins”) collected before and after Pinterest’s actions to provide more reliable vaccine-related information. Pins were based on search results and were analyzed using the Health Belief Model.Results. The majority of preaction search results leaned toward vaccine skepticism, specifically focused on perceived vaccine barriers. Few pins were published by public health-related Pinterest accounts. Postaction search results showed a significant shift to HPV vaccination benefits, and the number of pins by government or medical accounts increased. However, the proportion of pins in search results containing HPV content of any type was significantly lower.Conclusions. Pinterest’s efforts to moderate vaccination discussions were largely successful. Rapamycin cost However, the ban also appeared to limit HPV vaccination search results overall, which may contribute to confusion or an information vacuum.Objectives. To examine the role that bots play in spreading vaccine information on Twitter by measuring exposure and engagement among active users from the United States.Methods. We sampled 53 188 US Twitter users and examined who they follow and retweet across 21 million vaccine-related tweets (January 12, 2017-December 3, 2019). Our analyses compared bots to human-operated accounts and vaccine-critical tweets to other vaccine-related tweets.Results. The median number of potential exposures to vaccine-related tweets per user was 757 (interquartile range [IQR] = 168-4435), of which 27 (IQR = 6-169) were vaccine critical, and 0 (IQR = 0-12) originated from bots. We found that 36.7% of users retweeted vaccine-related content, 4.5% retweeted vaccine-critical content, and 2.1% retweeted vaccine content from bots. Compared with other users, the 5.8% for whom vaccine-critical tweets made up most exposures more often retweeted vaccine content (62.9%; odds ratio [OR] = 2.9; 95% confidence interval [CI] = 2.7, 3.1), vaccine-critical content (35.0%; OR = 19.0; 95% CI = 17.3, 20.9), and bots (8.8%; OR = 5.4; 95% CI = 4.7, 6.3).Conclusions. A small proportion of vaccine-critical information that reaches active US Twitter users comes from bots.Objectives. To understand changes in how Facebook pages frame vaccine opposition.Methods. We categorized 204 Facebook pages expressing vaccine opposition, extracting public posts through November 20, 2019. We analyzed posts from October 2009 through October 2019 to examine if pages’ content was coalescing.Results. Activity in pages promoting vaccine choice as a civil liberty increased in January 2015, April 2016, and January 2019 (t[76] = 11.33 [P  less then  .001]; t[46] = 7.88 [P  less then  .001]; and t[41] = 17.27 [P  less then  .001], respectively). The 2019 increase was strongest in pages mentioning US states (t[41] = 19.06; P  less then  .001). Discussion about vaccine safety decreased (r s [119] = -0.61; P  less then  .001) while discussion about civil liberties increased (r s [119] = 0.33; Py  less then  .001]). Page categories increasingly resembled one another (civil liberties r s [119] = -0.50 [P  less then  .001]; alternative medicine r s [84] = -0.77 [P  less then  .001]; conspiracy theories r s [119] = -0.