Mining, Modeling, and Leveraging Multidimensional Web Metrics to Support Scholarly Communities

The significant proliferation of scholarly output and the emergence of multidisciplinary research areas are rendering the research environment increasingly complex. In addition, an increasing number of researchers are using academic social networks to discover and store scholarly content. The spread of scientific discourse and research activities across the web, especially on social media platforms, suggests that far-reaching changes are taking place in scholarly communication and the geography of science. This dissertation provides integrated techniques and methods designed to address the information overload problem facing scholarly environments and to enhance the research process. There are four main contributions in this dissertation. First, this study identifies, quantifies, and analyzes international researchers’ dynamic scholarly information behaviors, activities, and needs, especially after the emergence of social media platforms. The findings based on qualitative and quantitative analysis report new scholarly patterns and reveals differences between researchers according to academic status and discipline. Second, this study mines massive scholarly datasets, models diverse multidimensional non-traditional web-based indicators (altmetrics), and evaluates and predicts scholarly and societal impact at various levels. The results address some of the limitations of traditional citation-based metrics and broaden the understanding and utilization of altmetrics. Third, this study recommends scholarly venues semantically related to researchers’ current interests. The results provide important up-to-the-minute signals that represent a closer reflection of research interests than post-publication usage-based metrics. Finally, this study develops a new scholarly framework by supporting the construction of online scholarly communities and bibliographies through reputation-based social collaboration, through the introduction of a collaborative, self-promoting system for users to advance their participation through analysis of the quality, timeliness and quantity of contributions. The framework improves the precision and quality of social reference management systems. By analyzing and modeling digital footprints, this dissertation provides a basis for tracking and documenting the impact of scholarship using new models that are more akin to reading breaking news than to watching a historical documentary made several years after the events it describes

Predicting Research that will be Cited in Policy Documents

Scientific publications and other genres of research output are increasingly being cited in policy documents. Citations in documents of this nature could be considered a critical indicator of the significance and societal impact of the research output. In this study, we built classification models that predict whether a particular research work is likely to be cited in a public policy document based on the attention it received online, primarily on social media platforms. We evaluated the classifiers based on their accuracy, precision, and recall values. We found that Random Forest and Multinomial Naive Bayes classifiers performed better overall.Comment: 2 page extended abstract submitted for ACM WebSci'17 conferenc

Pok\'emon Go: Impact on Yelp Restaurant Reviews

Pok\'emon Go, the popular Augmented Reality based mobile application, launched in July of 2016. The game's meteoric rise in usage since that time has had an impact on not just the mobile gaming industry, but also the physical activity of players, where they travel, where they spend their money, and possibly how they interact with other social media applications. In this paper, we studied the impact of Pok\'emon Go on Yelp reviews. For restaurants near Pok\'eStops, we found a slight drop in the number of online reviews

Exploring Features for Predicting Policy Citations

In this study we performed an initial investigation and evaluation of altmetrics and their relationship with public policy citation of research papers. We examined methods for using altmetrics and other data to predict whether a research paper is cited in public policy and applied receiver operating characteristic curve on various feature groups in order to evaluate their potential usefulness. From the methods we tested, classifying based on tweet count provided the best results, achieving an area under the ROC curve of 0.91.Comment: 2 pages, accepted to JCDL '1

Toward Systematic Design Considerations of Organizing Multiple Views

Multiple-view visualization (MV) has been used for visual analytics in various fields (e.g., bioinformatics, cybersecurity, and intelligence analysis). Because each view encodes data from a particular perspective, analysts often use a set of views laid out in 2D space to link and synthesize information. The difficulty of this process is impacted by the spatial organization of these views. For instance, connecting information from views far from each other can be more challenging than neighboring ones. However, most visual analysis tools currently either fix the positions of the views or completely delegate this organization of views to users (who must manually drag and move views). This either limits user involvement in managing the layout of MV or is overly flexible without much guidance. Then, a key design challenge in MV layout is determining the factors in a spatial organization that impact understanding. To address this, we review a set of MV-based systems and identify considerations for MV layout rooted in two key concerns: perception, which considers how users perceive view relationships, and content, which considers the relationships in the data. We show how these allow us to study and analyze the design of MV layout systematically.Comment: Short paper with 4 pages + 1 reference page, 2 figures, 1 table, accepted at IEEE VIS 2022 conferenc