Journal of Medical Internet Research - International Scientific Journal for Medical Research, Information and Communication on the Internet

We introduce a new research framework for analyzing the spatial distribution of web pages and social media (Twitter) messages with related contents, called Visualizing Information Space in Ontological Networks (VISION). This innovative method can facilitate the tracking of ideas and social events disseminated in cyberspace from a spatial-temporal perspective. Thousands of web pages and millions of tweets associated with the same keywords were converted into visualization maps using commercial web search engines (Yahoo application programming interface (API) and Bing API), a social media search engine (Twitter APIs), Internet Protocol (IP) geolocation methods, and Geographic Information Systems (GIS) functions (e.g., kernel density and raster-based map algebra methods). We found that comparing multiple web information landscapes with different keywords or different dates can reveal important spatial patterns and “geospatial fingerprints” for selected keywords. We used the 2012 US Presidential Election candidates as our case study to validate this method. We noticed that the weekly changes of the geographic probability of hosting “Barack Obama” or “Mitt Romney” web pages are highly related to certain major campaign events. Both attention levels and the content of the tweets were deeply impacted by Hurricane Sandy. This new approach may provide a new research direction for studying human thought, human behaviors, and social activities quantitatively.

The volume, velocity, and variety of data that are now becoming available allow us to study urban environments based on human behaviour with a spatial, temporal, and thematic granularity that was not achievable until now. Such data-driven approaches open up additional, complementary perspectives on how urban systems function, especially if they are based on user-generated content (UGC). While the data sources, such as social media, introduce specific biases, they also open up new possibilities for scientists and the broader public. For instance, they provide answers to questions that previously could only be addressed by complex simulations or extensive human-participant surveys. Unfortunately, many of the required data sets are locked in data silos that are accessible only via restricted APIs. Even if these data could be fully accessed, their naïve processing and visualization would surpass the abilities of modern computer architectures. Finally, the established place schemata used to study urban spaces differ substantially from UGC-based point-of-interest (POI) schemata. In this work, we present a multi-granular, data-driven, and theory-informed approach that addresses the key issues outlined above by introducing a theoretical and technical framework to interactively explore the pulse of a city based on social media.

Cognitive regions and places are notoriously difficult to represent in geographic information science and systems. The exact delineation of cognitive regions is challenging insofar as borders are vague, membership within the regions varies non-monotonically, and raters cannot be assumed to assess membership consistently and homogeneously. In a study published in this journal in 2014, researchers devised a novel grid-based task in which participants rated the membership of individual cells in a given region and contrasted this approach to a standard boundary-drawing task. Specifically, the authors assessed the vague cognitive regions of Northern California and Southern California. The boundary between these cognitive regions was found to have variable width, and region membership peaked not at the most northern or southern cells but at substantially less extreme latitudes. The authors thus concluded that region membership is about attitude, not just latitude. In the present work, we reproduce this study by approaching it from a computational fourth-paradigm perspective, i.e., by the synthesis of high volumes of heterogeneous data from various sources. We compare the regions which we identify to those from the human-participants study of 2014, identifying differences and commonalities. Our results show a significant positive correlation to those in the original study. Beyond the extracted regions themselves, we compare and contrast the empirical and analytical approaches of these two methods, one a conventional human-participants study and the other an application of increasingly popular data-synthesis-driven research methods in GIScience.

During puberty, a woman’s breasts are vulnerable to environmental damage (“window of vulnerability”). Early exposure to environmental carcinogens, endocrine disruptors, and unhealthy foods (refined sugar, processed fats, food additives) are hypothesized to promote molecular damage that increases breast cancer risk. However, prospective human studies are difficult to perform and effective interventions to prevent these early exposures are lacking. It is difficult to prevent environmental exposures during puberty. Specifically, young women are repeatedly exposed to media messaging that promotes unhealthy foods. Young women living in disadvantaged neighborhoods experience additional challenges including a lack of access to healthy food and exposure to contaminated air, water, and soil. The purpose of this review is to gather information on potential exposures during puberty. In future directions, this information will be used to help elementary/middle-school girls to identify and quantitate environmental exposures and develop cost-effective strategies to reduce exposures.

The multilevel model of meme diffusion conceptualizes how mediated messages diffuse over time and space. As a pilot application of implementing the meme diffusion, we developed the social media analytics and research testbed to monitor Twitter messages and track the diffusion of information in and across different cities and geographic regions. Social media analytics and research testbed is an online geo-targeted search and analytics tool, including an automatic data processing procedure at the backend and an interactive frontend user interface. Social media analytics and research testbed is initially designed to facilitate (1) searching and geo-locating tweet topics and terms in different cities and geographic regions; (2) filtering noise from raw data (such as removing redundant retweets and using machine learning methods to improve precision); (3) analyzing social media data from a spatiotemporal perspective; and (4) visualizing social media data in diagnostic ways (such as weekly and monthly trends, trend maps, top media, top retweets, top mentions, or top hashtags). Social media analytics and research testbed provides researchers and domain experts with a tool that can efficiently facilitate the refinement, formalization, and testing of research hypotheses or questions. Three case studies (flu outbreaks, Ebola epidemic, and marijuana legalization) are introduced to illustrate how the predictions of meme diffusion can be examined and to demonstrate the potentials and key functions of social media analytics and research testbed.