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The internet is an important source of health information, and yet the quality of the resources that patients' access can vary widely. Previous research has evaluated the quality of information for several types of cancer; however, this has not yet been done for cervical cancer beyond treatment information. The goal of this project was to systematically evaluate the quality of resources for cervical cancer information available against a range of metrics, including content breadth and accuracy, readability, and accountability. Methods An internet search was performed using the term "cervical cancer" using Google and two metasearch engines, Dogpile and Yippy. The top-100 websites returned across all three engines were evaluated using a validated structured rating tool. Results Only 32% of websites disclosed their author and only 38% used citations, while 64% of websites had been updated in the last two years. Readability was at university-level or higher for 19% of websites, and high-school level for 78%. Coverage was highest for etiology and risk factors (93% of websites) and prevention strategies such as pap smears and vaccines (92%); coverage was lowest for prognosis (49%), staging (52%), side effects (47%), and follow-up (25%). When a topic was covered the information was predominantly accurate, and few websites had inaccurate information. At least one social-media platform was linked to by 79% of websites. Conclusions This project highlights the strengths and limitations in the quality of the top-100 informational cervical cancer websites. These findings can inform the dialogue between health care providers and patients around selecting and evaluating information resources. These findings can also inform specific improvements to make online resources for cervical cancer more accessible, comprehensive, and relevant to patients.

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We present a predictive model of human behaviour when tracing paths through a node-link graph, a low-level abstract task that feeds into many other visual data analysis tasks that require understanding topological structure. We introduce the idea of a search set, namely, the set of paths that users are most likely to search, as a useful intermediate level for analysis that lies between the global level of the full graph and the local level of the shortest path between two nodes. We present potential practical applications of a predicted search set in the design of visual encoding and interaction techniques for graphs. Our predictive model is based on extensive qualitative analysis from an observational study, resulting in a detailed characterization of common path-tracing behaviours. These include the conditions under which people stop following paths, the likely directions for the first hop people follow, the tendency to revisit previously followed paths and the tendency to mistakenly follow apparent paths in addition to true topological paths. The algorithmic implementation of our predictive model is robust to a broad range of parameter settings. We provide a preliminary validation of the model through a hierarchical multiple regression analysis comparing graph readability factors computed on the predicted search set to factors computed at the global level and the local shortest path solution. The tested factors included edge-edge crossings, node-edge crossings, path continuity and path length. Our approach provides modest improvements for predictions of RT and error using search-set factors.