First responder communities must identify technologies that are e ective in performing duties ranging from law enforcement to emergency medical to re ghting. We aimed to create tools that gather and assist in quickly understanding responders' requirements using semantic technologies and social network analysis. We describe the design and prototyping of a set of semantically-enabled interactive tools that provide a "dashboard" for visualizing and interacting with aggregated data to perform focused social network analysis and community identi cation. 1 In response to a request from NIST to develop approaches to using social networks and associated technology to improve rst responder e ectiveness and safety, we used semantic technologies and social network analysis to locate Twitter-based rst responder sub-communities and to identify current topics and active stakeholders within those communities. Our objective is to create a repeatable set of Twitter-compatible methods that constitute an initial requirements gathering process. We report on using social media analysis techniques for the tasks of identifying rst responder communities and on examining tools and techniques for identifying potential requirements stakeholders within those networks. Our First Responders Social Network Analysis Work ow (Figure 12) has helped researchers make sense of the vast quantity of information moving through Twitter. Identi ed stakeholders might be engaged by researchers in (for example) participatory design3 tasks that are elements of a requirements gathering
1 A technical report discussing this work in greater detail may be found at [
We employed the Twitter Search API 4 to collect tweets containing one or more hashtags from a list of 17 hashtags identi ed as relevant by the rst responder community.5 We report on two events: the anticipated February 2013 Nemo storm and the unanticipated Boston Marathon bombing. A visualization tool allows browsing over time showing (for example) total tweets for a hashtag over time while enabling a user to zoom in and explore with ner temporal granularity. 3
We created a tool to visualize and enable interaction with topic modeling6
results, applying MALLET (http://mallet.cs.umass.edu/ ) across Twitter
sample data. The tool presents topics as a pie chart; each "pie slice" represents
an emergent topic, with assigned names indicating the most prevalent hashtags
occurring in that topic. A popup list of hashtags enables the researcher to view
other hashtags that are more loosely related to the topic.
4 See e.g. "Using the Twitter Search API" http://bit.ly/1sY7O
5 For the complete list see http://www.sm4em.org/active-hashtags/
6 See especially [
Machine learning can help researchers identify hashtags that are topically related to the users area of interest but might not be immediately obvious. One of our tools uses co-occurrence to help identify evolving hashtags of interest, relating Twitter frequent posters with hashtags. The intensity of each cell in a matrix indicates the relative frequency with which a given user has tweeted using a particular hashtag. Users are ltered by weighted entropy and a subset is selected to provide the most coverage over hashtags of interest. Researchers may use this tool to develop a ne-grained understanding of topics and to pinpoint users of interest for further requirements gathering. 5
Situations may arise where close examination of network dynamics and
conversation evolution is necessary. "Multi-modal" data visualizations enable researchers
to move seamlessly from macro-scale visualizations to the micro-scale of
individual tweets. Fig 2 shows one level where the propagation and retweeting of themes
can be dynamically observed, while another level supports examination of
individual tweets and associated media content.7
7 Further details of the Twitter dataset used for this visualization may be found in [
Our social network analysis and visualization tools demonstrate methods of passive social network monitoring8 intended to help researchers discover topical social network conversations among rst responders. These tools have limited ability to connect and engage researchers with individual persons of interest. Current and future work includes extending the tools to expose and make actionable more user information, including identifying which individuals are most active on pertinent hashtags and are stakeholders of interest from a requirements gathering perspective. The time-sensitive nature of any Twitter sample dataset requires that visualization tools be adept at ltering over time periods of interest. Current and future work includes improved support for browsing over time with emphasis on nding and understanding topic shifts.
Recent events have demonstrated [
We are grateful to the Law Enforcement Standards O ce (OLES) of the U.S. National Institute of Standards and Technology (NIST) for sponsoring this work, and members of the DHS First Responders Communities of Practice Virtual Social Media Working Group (VSMWG) for numerous helpful discussions. 8 Passive monitoring supports constant monitoring of a "default" set of known rst responder hashtags meaning that when unanticipated events such as natural disasters happen, it is likely we'll have a useful if not perfect sample dataset. Active monitoring, conducted after the fact supports a deeper examination of user activity, including a focused examination of retweets and an investigation of "spontaneous" hashtags that emerge throughout the event.