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. imposed 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 the first to look into the characteristics of users propagating unverified "cures" of cancer on Twitter as a case study of tracking health misinformation outside crisis communication management domain. The identification of potential sources of such misinformation would allow public health officials to monitor social media discourse, characterize the deficiencies in current communication strategy, and detect new misinformation before it causes serious harm. The present work marries two burgeoning directions of social media research: the tracking of misinformation and the measurement of health-related attitudes and activities. Below we describe the latest developments in both topics, as well as the recent attempts at tracking health misinformation in particular. Misinformation tracking. In 2017, the term "fake news" has been named Collins' Word of the Year, referring to "false, often sensational, information" 2 . In the context of news, rumors and misinformation have been associated with political sphere, with the latest works proposing data mining solutions [81] which encompass opinionated language detection [17] , visual feature extraction [42] and user group characteristics [59] . In fact, users play an important role, as misinformation sometimes originates from automated accounts working in synchrony -bot nets -with [80] claiming millions of political tweets spread thusly during and following the 2016 U.S. Presidential election. Tools to detect such bots include Hoaxy [79] to track the spread of claims and Botometer [20] to detect social bots. Beyond the automated accounts, case studies of incidents such as the Ukrainian conflict [49] and the mass shootings in US [82] examine human reactions to questionable information online. The provenance and motivation behind such information has increasingly become a contentious issue, as speculations rose that important political decisions, including the United Kingdom vote to leave the European Union and the election of Donald Trump to the U.S. Presidency, have been potentially swayed by forces outside those nations [45] , posing a danger to democracy itself. Health-related attitudes on social media. Beyond politics, social media also provides ample resources for health-related decision making, capturing behaviors and attitudes impacting individual health. Automated methods have been devised for tracking marijuana use on Twitter [36] , and to capture attitudes toward legal drugs including Xanax and Adderall [78] . Forum threads have been analyzed by Wu et al. [90] to discover adverse drug effects and drug interactions, using association mining. Likewise, behaviors related to lifestyle diseases such as diabetes type 2 and obesity have been tracked using Twitter (a microblogging platform) [1], Instagram (a photo sharing platform) [64] , and Facebook (a social network) [5] , along with attitudes toward food and diet [62] . A study of a community promoting anorexia on Flickr (another photo sharing platform) [92] showed that attempts of the anti-anorexia programs to infiltrate the community with intervention messages tagged with pro-anorexia tags was counterproductive in the long run (with users exposed to such remaining in the group longer). Further uses of social media to gauge the efficacy of health communication includes a recent study of breast cancer mammography advisory on Twitter [67] which found many users to be confused by it than to approve of it. Finally, public awareness of health-related topics has been recently gauged through the advertising platforms provided by these social media -for instance using Facebook Advertising Manager to estimate the number of Facebook users interested in diabetes-related topics [6] . Thus, as social media adoptions increases, so does the health-related discussion and information seeking on these platforms, allowing for large-scale analysis and tracking. Health misinformation online. For over a decade, medical sociologists have studied Internet as a new component of health ecosystems, with especially cancer patients utilizing it to collect information and make treatment decisions [16] . The patient reliance on the Web has resulted in a patient-Web-physician "triangulation" [86] with benefits such as more efficient use of clinical time and additional support from online support groups, coupled with potential harms like the dangers posed by the variable quality of information, unnecessary visits to a physician, and exacerbating existing socioeconomic health disparities. Early on, the use of Internet by patients has been shown to be problematic [77] . For instance, [25] find that the quality of Web search results varies for differently worded medical queries. Democratization of content publishing may also be exacerbating quality concerns, as YouTube videos have been found to contain instances of public display of harmful or unhealthy behaviors, promotion of tobacco to consumers, and distorting policy and research funding agendas [54] . Moreover, concentrated efforts promoting doubt of medical establishment, such as the "anti-vaxxer" movement on Twitter, play into the larger skepticism of government and conspiratorial thinking [65] . Recent attempts to track health misinformation include "VAC Medi+ board", an online interactive visualization framework integrating heterogeneous real-time data streams with Twitter data [51] , which tracks the spread of vaccine related information on Twitter and the sources of information spread. Such social media content promoting vaccine hesitancy has been shown to impact future opinions of its users, and potentially their subsequent behaviors [24] . It is also possible to study the impact of the interventions designed to change such behaviors, such as in the previously mentioned study by [92] on the potentially unsuccessful attempts to infuse anti-anorexia content into pro-anorexia communities on Flickr. Dynamic nature of social media also allows for fast spread of misinformation during an ongoing epidemic, and machine learning techniques have been deployed to track such content, for instance during the 2016 Zika outbreak [23, 34] . To help social media users, tools are being developed to ease the verification of health claims via natural language processing and retrieval of necessary medical literature [75] .
The work in REF aimed to automatically identify Twitter users who post health misinformation by comparing 4,212 users who promote ineffective cancer treatments with the same number of general users interested in cancer.
Fake Cures: User-centric Modeling of Health Misinformation in Social Media
PACMHCI
Computer Science Psychology