Beliefs that are not the result of science-based interpretation of evidence (e.g., belief in ghosts or belief that prayer is effective) are extremely common. Science enthusiasts have expressed interest in automatic detection of non-science-based claims. This thesis intends to provide some first steps toward a solution, specifically aimed at detecting Twitter users who are likely or unlikely to take a science-based perspective on all topics. As part of this thesis, a set a Twitter users was labeled as being either "pro-science" (i.e. as having the view that beliefs are rational if and only if they are in accord with science-based reasoning) or "non-pro-science" (i.e. as having the view that beliefs may be reasonable even if they are not in accord with science-based reasoning). Word frequency ratios relative to a neutral dataset, and a simple topic alignment technique, suggest considerable linguistic divergence between the pro-science and non-pro-science users. High accuracy logistic regression classification using linguistic features of users' recent tweets support that idea. Supervised classification experiments suggest that the pro-science and non-pro-science perspectives are not only detectable from linguistic features, but that they can be abstracted away from particular topics (i.e. that the pro-science and non-pro-science perspectives are not inherently topic-specific). Results from distantly supervised classification suggest that using easily acquired, weakly labeled data may be preferable to the much slower process of individually labeling data for some applications, despite the pronounced inferiority to the fully supervised approach in terms of accuracy. The best classifier obtained in this thesis has an accuracy of 93.9%. / text

Objective: The European League Against Rheumatism and the American College of Rheumatology jointly embarked on a new classification criteria for systemic lupus erythematosus (SLE) project. Its first phase involved generation of a broad set of items potentially useful for classification of SLE. This study was undertaken to add the patient perspective to an expert Delphi approach and an early patient cohort study. Methods: A national cross-sectional study was conducted. A self-report questionnaire was published in the ‘‘Schmetterling’’ (Butterfly), the quarterly journal of the German SLE patient association. Individuals with SLE were asked to anonymously complete the questionnaire, which asked for demographic details, organ manifestations, autoantibodies and symptoms. Results: A total of 339 completed questionnaires out of 2498 were returned, a response rate of 13.6%; 83.2% reported they were ANA positive and 81.7% reported joint, 66.1% skin and 33.0% renal involvement. For the time before and in the first year after their SLE diagnosis, the majority reported fatigue (89.4%), joint pain (86.7%), photosensitivity (79.4%) and myalgia (76.1%). Of interest, more than half of the patients reported fever as an early symptom (53.7%). Conclusion: For a Caucasian European SLE patient population, the overall characteristics suggest meaningful representation. While many symptoms were reported as expected, the high percentage of patients reporting fever and the significant number of patients with unexpected gastrointestinal complaints are of particular interest. These data add to the information on early SLE symptoms informing the development process of new SLE classification criteria.