Hypothesis generation, a crucial initial step for making scientific discoveries, relies on prior knowledge, experience, and intuition. Chance connections made between seemingly distinct subareas sometimes turn out to be fruitful. The goal in text mining is to assist in this process by automatically discovering a small set of interesting hypotheses from a suitable text collection. In this report, we present open and closed text mining algorithms that are built within the discovery framework established by Swanson and Smalheiser. Our algorithms represent topics using metadata profiles. When applied to MEDLINE, these are MeSH based profiles. We present experiments that demonstrate the effectiveness of our algorithms. Specifically, our algorithms successfully generate ranked term lists where the key terms representing novel relationships between topics are ranked high.

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BackgroundWe report the Gene Normalization (GN) challenge in BioCreative III where participating teams were asked to return a ranked list of identifiers of the genes detected in full-text articles. For training, 32 fully and 500 partially annotated articles were prepared. A total of 507 articles were selected as the test set. Due to the high annotation cost, it was not feasible to obtain gold-standard human annotations for all test articles. Instead, we developed an Expectation Maximization (EM) algorithm approach for choosing a small number of test articles for manual annotation that were most capable of differentiating team performance. Moreover, the same algorithm was subsequently used for inferring ground truth based solely on team submissions. We report team performance on both gold standard and inferred ground truth using a newly proposed metric called Threshold Average Precision (TAP-k).ResultsWe received a total of 37 runs from 14 different teams for the task. When evaluated using the gold-standard annotations of the 50 articles, the highest TAP-k scores were 0.3297 (k=5), 0.3538 (k=10), and 0.3535 (k=20), respectively. Higher TAP-k scores of 0.4916 (k=5, 10, 20) were observed when evaluated using the inferred ground truth over the full test set. When combining team results using machine learning, the best composite system achieved TAP-k scores of 0.3707 (k=5), 0.4311 (k=10), and 0.4477 (k=20) on the gold standard, representing improvements of 12.4%, 21.8%, and 26.6% over the best team results, respectively.ConclusionsBy using full text and being species non-specific, the GN task in BioCreative III has moved closer to a real literature curation task than similar tasks in the past and presents additional challenges for the text mining community, as revealed in the overall team results. By evaluating teams using the gold standard, we show that the EM algorithm allows team submissions to be differentiated while keeping the manual annotation effort feasible. Using the inferred ground truth we show measures of comparative performance between teams. Finally, by comparing team rankings on gold standard vs. inferred ground truth, we further demonstrate that the inferred ground truth is as effective as the gold standard for detecting good team performance.

Recent advances in text mining have provided new methods for capitalizing on the voluminous natural language text data created by organizations, their employees, and their customers. Although often overlooked, decisions made during text preprocessing affect whether the content and/or style of language are captured, the statistical power of subsequent analyses, and the validity of insights derived from text mining. Past methodological articles have described the general process of obtaining and analyzing text data, but recommendations for preprocessing text data were inconsistent. Furthermore, primary studies use and report different preprocessing techniques. To address this, we conduct two complementary reviews of computational linguistics and organizational text mining research to provide empirically grounded text preprocessing decision-making recommendations that account for the type of text mining conducted (i.e., open or closed vocabulary), the research question under investigation, and the data set’s characteristics (i.e., corpus size and average document length). Notably, deviations from these recommendations will be appropriate and, at times, necessary due to the unique characteristics of one’s text data. We also provide recommendations for reporting text mining to promote transparency and reproducibility.

BackgroundIt is becoming increasingly common for individuals and organizations to use social media platforms such as Facebook. These are being used for a wide variety of purposes including disseminating, discussing and seeking health related information. U.S. Federal health agencies are leveraging these platforms to ‘engage’ social media users to read, spread, promote and encourage health related discussions. However, different agencies and their communications get varying levels of engagement. In this study we use statistical models to identify factors that associate with engagement.MethodsWe analyze over 45,000 Facebook posts from 72 Facebook accounts belonging to 24 health agencies. Account usage, user activity, sentiment and content of these posts are studied. We use the hurdle regression model to identify factors associated with the level of engagement and Cox proportional hazards model to identify factors associated with duration of engagement.ResultsIn our analysis we find that agencies and accounts vary widely in their usage of social media and activity they generate. Statistical analysis shows, for instance, that Facebook posts with more visual cues such as photos or videos or those which express positive sentiment generate more engagement. We further find that posts on certain topics such as occupation or organizations negatively affect the duration of engagement.ConclusionsWe present the first comprehensive analyses of engagement with U.S. Federal health agencies on Facebook. In addition, we briefly compare and contrast findings from this study to our earlier study with similar focus but on Twitter to show the robustness of our methods.