Thursday, April 20, 2017

Zika in Twitter: Health Narratives

In the paper we explored how health narratives and event storylines pertaining to the recent Zika outbreak emerged in social media and how it related to news stories and actual events.

Specifically we combined actors (e.g. twitter uses), locations (e.g. where the tweets originated) and concepts (e.g. emerging narratives such as pregnancy) to gain insights on the mechanisms that drive participation, contributions, and interactions on social media  during a disease outbreak. Below you can read a summary of our paper along with some of the figures which highlight our methodology and findings.  

An overview of the Twitter narrative analysis approach, starting with data collection, and proceeding with preprocessing and data analysis to identify narrative events, which can be used to build an event storyline.

Background: The recent Zika outbreak witnessed the disease evolving from a regional health concern to a global epidemic. During this process, different communities across the globe became involved in Twitter, discussing the disease and key issues associated with it. This paper presents a study of this discussion in Twitter, at the nexus of location, actors, and concepts.
Objective: Our objective in this study was to demonstrate the significance of 3 types of events: location related, actor related, and concept- related for understanding how a public health emergency of international concern plays out in social media, and Twitter in particular. Accordingly, the study contributes to research efforts toward gaining insights on the mechanisms that drive participation, contributions, and interaction in this social media platform during a disease outbreak. 
Methods: We collected 6,249,626 tweets referring to the Zika outbreak over a period of 12 weeks early in the outbreak (December 2015 through March 2016). We analyzed this data corpus in terms of its geographical footprint, the actors participating in the discourse, and emerging concepts associated with the issue. Data were visualized and evaluated with spatiotemporal and network analysis tools to capture the evolution of interest on the topic and to reveal connections between locations, actors, and concepts in the form of interaction networks. 
Results: The spatiotemporal analysis of Twitter contributions reflects the spread of interest in Zika from its original hotspot in South America to North America and then across the globe. The Centers for Disease Control and World Health Organization had a prominent presence in social media discussions. Tweets about pregnancy and abortion increased as more information about this emerging infectious disease was presented to the public and public figures became involved in this. 
Conclusions: The results of this study show the utility of analyzing temporal variations in the analytic triad of locations, actors, and concepts. This contributes to advancing our understanding of social media discourse during a public health emergency of international concern.

Keywords: Zika Virus; Social Media; Twitter Messaging; Geographic Information Systems.

Spatiotemporal participation patterns and identifiable clusters over 4 of our twelve week study. The top left panel shows the data during the first week, and time progresses from left to right and from top to bottom towards .

Subsets of the full retweet network pertaining to the WHO (left) and CDC (right), and clusters identified within them. Magenta clusters are centered upon health entities, green upon news organizations, orange upon political entities.

Visualizing a narrative storyline across locations (blue), actors (red), and concepts (green).

Full Reference:
Stefanidis, A., Vraga, E., Lamprianidis, G., Radzikowski, J., Delamater, P.L., Jacobsen, K.H., Pfoser, D., Croitoru, A. and Crooks, A.T. (2017). “Zika in Twitter: Temporal Variations of Locations, Actors, and Concepts”, JMIR Public Health and Surveillance, 3 (2): e22. (pdf)

As normal, any feedback or comments are most welcome. 

Saturday, April 08, 2017

Talk from the AAG

The last few days I have been attending the  Association of American Geographers (AAG) Annual Meeting in Boston. A common theme at the AAG sessions I attended  (to me at least) seemed to  be the rise of new sources of data which give us new ways to explore geographical problems and the challenges of working with bigger data sets. Perhaps where this was most explicitly expressed were in the Geographic Data Science sessions which was pitched to be at the nexus of data science and geography.

While at the meeting I participated in a panel under the theme of "Geographic Data Science", and as part of the Symposium on Human Dynamics in Smart and Connected Communities, I co-organized two sessions entitled Agents - the 'atomic unit' of social systems? which also included Agent-Bingo.  Finally I and gave a presentation of our current research at Mason, entitled "Megacities through the Lens of Computational Social Science", more details can be seen below. For those wanting to know more on the synthetic population generation, click here.

Geographic Data Science Panel

Megacities through the Lens of Computational Social Science


Currently there are over 35 megacities, cities with over 10 million inhabitants, and the number of such cities are expected to grow in the coming years. These habitats represent many challenges from an agent-based modeling perspective. Their size and density, the diverse behaviors of their inhabitants, and their evolving social network of communities along with multiple interacting subsystems need to be understood, captured and modeled. To capture and link the dynamics that shape and form these systems, we must grapple with them in their entirety. While there have been many models applied to specific subsystems of megacities (e.g. traffic, disease spread, urban growth etc.) their interactions often go untouched.

The lens of computational social science (CSS), the interdisciplinary science of complex social systems and their investigation through computational modeling and related techniques can be used to understand and model megacities. Given the advances in computational power and the availability of fine scale datasets, what are the opportunities offered to us with respect to exploring megacities? In an attempt to answer this question we will demonstrate how new sources of data (e.g. volunteered geographical information) can be fused with more traditional data (e.g. census data) to create the basis of a megacity model both in terms of its physical environment and its social environment. We will then show results from a simulated disaster explores how people potentially react and behave to the evolving crisis within a megacity.

Keywords: Megacities, GIS, Agent-based modeling, Social Networks, Behavior

Full References:
Crooks A.T., Kennedy W.G., Burger, A. Oz, T. and Heppenstall, A. (2017), Megacities through the Lens of Computational Social Science, The Association of American Geographers (AAG) Annual Meeting, 5th-9th, April, Boston, MA. (pdf)

Tuesday, April 04, 2017

Smart Cities in IEEE Pervasive Computing

We are excited to announce that the special issue that we organized for IEEE Pervasive Computing is now out. In the special issue entitled "Smart Cities" and demonstrates the state of the art of pervasive computing technologies that collect, monitor, and analyze various aspects of urban life. The articles and departments in the special issue highlight the coming revolution in urban data via some of the different approaches researchers are taking to build tools and applications to better inform decision making (to reduce energy consumption or improve visitor flows, for example). Such research will be critical to setting goals for sustainable urban development within different global contexts. We need to better understand cities and their underlying systems if we want to improve the quality of urban life. To this end, in the special issue we have an introduction (editorial) followed by a number of articles, an interview and a research spotlight:
We hope you enjoy them. Thank you for the authors who submitted papers, the reviewers, Rob Kitchen for giving an interview and Barbara Lenz and Dirk Heinrichs for discussing their research. Lastly, we would also like to thank the IEEE Pervasive Computing team for ensuring that the special issue came to fruition.

Full Reference to the Introduction: 
Crooks, A.T., Schechtner, K., Day, A.K and Hudson-Smith, A (2017), Creating Smart Buildings and Cities, IEEE Pervasive Computing, 16 (2): 23-25. (pdf)

Friday, March 10, 2017

Geovisualization of Social Media

Figure 1: Map Mashup of Twitter data, where eachdot
represents a tweet, the text corresponds to the selected
 tweet marked with a star
In the recently released "The International Encyclopedia of Geography: People, the Earth, Environment, and Technology" we were asked to write a brief entry entitled "geovisualization of social media". Below is a summary of  our chapter:

The proliferation of social media over the last decade is presenting substantial computational challenges associated with the management, processing, analysis and visualization of the corresponding massive volumes of data. Furthermore, this new form of information also imposes new-found challenges upon the geographical community due to the unique nature of its content, as analyzing such data calls for a hybrid mix of spatial and social analysis. The spatial content of social media comprises primarily coordinates from which the contributions originate, or references to specific locations. At the same time, these data have a strong social component, as they can reveal the underlying social structure of the user community through manifestations of their interactions. Analyzing both the spatial and social content of social media feeds is referred to as geosocial analysis. Within this entry we explore the geovisualization opportunities and challenges that are emerging as social media are becoming the subject of study of the geographical community.
In more detail, we start off discussing how the geographic content of social media feeds represents a new type of geographic information. It transcends the early definitions of crowdsourcing or volunteered geographic information as it is not the product of a process through which citizens explicitly and purposefully contribute geographic information to update or expand geographic databases. Instead, the type of geographic information that can be harvested from social media feeds can be referred to as Ambient Geographic Information; it is embedded in the content of these feeds, often across the content of numerous entries rather than within a single one, and has to be somehow extracted. Nevertheless, it is of great importance as it communicates instantaneously information about emerging issues. At the same time, it provides an unparalleled view of the complex social networking and cultural dynamics within a society, and captures the temporal evolution of the human landscape.

In many cases, the geovisualization of social media feeds predominately take the appearance of web map mashups, in essence portraying the location of social media usage on a map. Such an early attempt to visualize social media is shown Figure 1. We argue that while this approach is informative, it often falls short of capturing the depth, richness, and complexity of the information that can be gleaned from social data. As a result, a need for more advanced geovisualization approaches that are capable of better capturing and communicating the complexity and multidimensionality of social media arises. And this is the focus of our chapter. We discuss briefly the geovisualization of network structures (such as shown in Figure 2), the geovisualization of network structure dynamics, the geovisualization of social media content (such as shown in Figure 3) along with the visualization of social media analysis (Figure 4) and conclude the chapter with a list of emerging research challenges.

Figure 2: Visualizing communities: a social network of an interest group (a), and the geovisualization of the  largest community shown over the contiguous U.S (B).

Figure 3: Visualizing social media content dynamics by coupling a Twitter stream viewer (A), a Twitter activity density map (B), and a ranked list top hash-tags (C) and top authors (E), a time slider (D), and author/hash-tags time series graphs.
Figure 4: Visualizing spatiotemporal clusters of tweets following the 2013 Boston bombing. Red circles indicate the approximate radius of each cluster, and color is used to indicate time.

We hope you enjoy. As always any feedback or comments most welcome. Please note this chapter was written a couple of years ago and more recent work by us has been done, click here to see some.

Full Reference:
Croitoru, A., Crooks, A.T., Radzikowski, J. and Stefanidis, A. (2017), Geovisualization of Social Media, in Richardson, D., Castree, N., Goodchild, M. F., Kobayashi, A. L., Liu, W. and Marston, R. (eds.), The International Encyclopedia of Geography: People, the Earth, Environment, and Technology, Wiley Blackwell. DOI: 10.1002/9781118786352.wbieg0605 (PDF)

Thursday, March 09, 2017

Cellular Automata

In the recently released "The International Encyclopedia of Geography: People, the Earth, Environment, and Technology" I was asked to write a brief entry on "Cellular Automata". Below is the abstract to my chapter, along some of the images I used in my discussion, the full reference to the chapter.

Cellular Automata (CA) are a class of models where one can explore how local actions generate global patterns through well specified rules. In such models, decisions are made locally by each cell which are often arranged on a regular lattice and the patterns that emerge, be it urban growth or deforestation are not coordinated centrally but arise from the bottom up. Such patterns emerge through the cell changing its state based on specific transition rules and the states of their surrounding cells. This entry reviews the principles of CA models, provides a background on how CA models have developed, explores a range of applications of where they have been used within the geographical sciences, prior to concluding with future directions for CA modeling. 

The figures below are a sample from the entry, for example, we outline different types of spaces within CA models such as those shown in Figures 1 and 2. We also show how simple rules can lead to the emergence of patterns such as the Game of Life as shown in Figure 3 or  Rule 30 as shown in Figure 4.

Figure 1: Two-Dimensional Cellular Automata Neighborhoods

Figure 2: Voronoi Tessellations Of Space Where Each Polygon Has A Different Number Of Neighbors Based On A Shared Edge.

Figure 3: Example of Cells Changing State from Dead (White) To Alive (Black) Over Time Depending On The States of its Neighboring Cells.

Figure 4: A One-Dimensional CA Model Implementing “Rule 30” Where Successive Iterations Are Presented Below Each Other.

Full Reference:
Crooks, A.T. (2017), Cellular Automata, in Richardson, D., Castree, N., Goodchild, M. F., Kobayashi, A. L., Liu, W. and Marston, R.  (eds.), The International Encyclopedia of Geography: People, the Earth, Environment, and Technology, Wiley Blackwell. DOI: 10.1002/9781118786352.wbieg0578. (pdf)