Geosocial Gauge Paper

As regular readers of the blog know, we have been spending a lot of time recently looking at social media and the growth in locational information within such media. To this end we are very happy to see one of our papers appear in the International Journal of Geographical Information Science. The paper is entitled “GeoSocial Gauge: A System Prototype for Knowledge Discovery from Social Media” which in essence discusses the challenge of merging diverse social media datasets into a single database which can then be used to generate geosocial knowledge. Below is the abstract:

“The remarkable success of online social media sites marks a shift in the way people connect and share information. Much of this information now contains some form of geographical content because of the proliferation of location-aware devices, thus fostering the emergence of geosocial media – a new type of user-generated geospatial information. Through geosocial media we are able, for the first time, to observe human activities in scales and resolutions that were so far unavailable. Furthermore, the wide spectrum of social media data and service types provides a multitude of perspectives on real-world activities and happenings, thus opening new frontiers in geosocial knowledge discovery. However, gleaning knowledge from geosocial media is a challenging task, as they tend to be unstructured and thematically diverse. To address these challenges, this article presents a system prototype for harvesting, processing, modeling, and integrating heterogeneous social media feeds towards the generation of geosocial knowledge. Our article addresses primarily two key components of this system prototype: a novel data model for heterogeneous social media feeds and a corresponding general system architecture. We present these key components and demonstrate their implementation in our system prototype, GeoSocial Gauge.”

Full reference:

Croitoru, A., Crooks, A.T., Radzikowski, J. and Stefanidis, A. (in press), GeoSocial Gauge: A System Prototype for Knowledge Discovery from Social Media, International Journal of Geographical Information Science. DOI: 10.1080/13658816.2013.825724

If you don’t have access to IGIS, send us an email and we can send you an early version of the paper.

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Tweet City: Building Heights by Number of Tweets Movie (London)

What if London’s buildings grew according to the amount of data they generate? Stephan Hügel (@urschrei) and Flora Roumpani (@en_topia) two of our PhD students from The Bartlett Centre for Advanced Spatial Analysis, University College London, have been working on a system to read in twitter data to CityEngine and link the geo…

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GeoSocial Gauge

Over the last couple of months we have been working on getting our GeoSocial Gauge system up and running. The idea behind the website is to bring together social media and geographical analysis to monitor and explore people’s views, reactions, and interactions through space and time. It takes advantage of the emergence of social media to observe the human landscape as the living, breathing organism that it is: we can witness the explosion-like dissemination of information within a society, or the clusters of individuals who share common opinions or attitudes, and map the locations of these clusters. This is an unprecedented development that broadens drastically our understanding of the way that people act, react to events, and interact with each other and with their environment. We refer to this novel approach to study the integration of geography and society as GeoSocial Analysis.
The GeoSocial Gauge has several live streams ranging from exploring the political issues (e.g. Sequester) to to see what people are tweeting about TV (The Walking Dead).

Screen shot of GeoSocial Gauge of the Sequester. Showing the location of tweets, the most frequent words and whether or not the messages are positive (green) or negative (red).
Screen shot of GeoSocial Gauge of The Walking Dead.

Some of our initial work on this type of analyis can be found at:

  • Stefanidis, T., Crooks, A.T. and Radzikowski, J. (2013), Harvesting Ambient Geospatial Information from Social Media Feeds, GeoJournal, 78, (2): 319-338.
  • Crooks, A.T., Croitoru, A., Stefanidis, A. and Radzikowski, J. (2013), #Earthquake: Twitter as a Distributed Sensor System, Transactions in GIS, 17(1): 124-147.

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GeoSocial Gauge

Over the last couple of months we have been working on getting our GeoSocial Gauge system up and running. The idea behind the website is to bring together social media and geographical analysis to monitor and explore people’s views, reactions, and interactions through space and time. It takes advantage of the emergence of social media to observe the human landscape as the living, breathing organism that it is: we can witness the explosion-like dissemination of information within a society, or the clusters of individuals who share common opinions or attitudes, and map the locations of these clusters. This is an unprecedented development that broadens drastically our understanding of the way that people act, react to events, and interact with each other and with their environment. We refer to this novel approach to study the integration of geography and society as GeoSocial Analysis.
The GeoSocial Gauge has several live streams ranging from exploring the political issues (e.g. Sequester) to to see what people are tweeting about TV (The Walking Dead).

Screen shot of GeoSocial Gauge of the Sequester. Showing the location of tweets, the most frequent words and whether or not the messages are positive (green) or negative (red).
Screen shot of GeoSocial Gauge of The Walking Dead.

Some of our initial work on this type of analyis can be found at:

  • Stefanidis, T., Crooks, A.T. and Radzikowski, J. (2013), Harvesting Ambient Geospatial Information from Social Media Feeds, GeoJournal, 78, (2): 319-338.
  • Crooks, A.T., Croitoru, A., Stefanidis, A. and Radzikowski, J. (2013), #Earthquake: Twitter as a Distributed Sensor System, Transactions in GIS, 17(1): 124-147.

Continue reading »

#Earthquake: Twitter as a Distributed Sensor System

Our work on using social media continues to develop and we have recently had a paper accepted in Transactions in GIS, entitled “#Earthquake: Twitter as a Distributed Sensor System“. Below we present our abstract and some of the results.

Social media feeds are rapidly emerging as a novel avenue for the contribution and dissemination of information that is often geographic. Their content often includes references to events occurring at, or affecting specific locations. Within this paper we analyze the spatial and temporal characteristics of the twitter feed activity responding to a 5.8 magnitude earthquake which occurred on the East Coast of the United States (US) on August 23, 2011. We argue that these feeds represent a hybrid form of a sensor system that allows for the identification and localization of the impact area of the event. By contrasting this to comparable content collected through the dedicated crowdsourcing ‘Did You Feel It?’ (DYFI) website of the US Geological Survey we assess the potential of the use of harvested social media content for event monitoring. The experiments support the notion that people act as sensors to give us comparable results in a timely manner, and can complement other sources of data to enhance our situational awareness and improve our understanding and response to such events.

The movie below show geolocated tweets with references to the earthquake through keyword (earthquake or earth and quake) and hashtag search (#earthquake or #quake) for the first hour after the earthquake.

The following images give a glimpse at some of our analysis.

Response pattern as function of distance from epicenter for the first 400 seconds after the earthquake. At the top we see a plot of (reaction time, distance) of all tweets during that period. At the bottom we show the histogram of the number of tweets as a function of distance.
Locations of the 40 tweets in the shaded area of the figure above overlaid over the USGS CDI scale map. Tweet locations are marked as green circles. Color-coding in the graph is ranging from red (high perceived intensity) to yellow (lower perceived intensity). The dashed line shows a distance of approximately 950 km (8.5 degrees of angular distance) from the epicenter.

The movie below gives you an idea of some of the tweet content:

Full reference to this paper is:

Crooks, A. T.,  Croitoru, A.,  Stefanidis, A. and Radzikowski, J. (acepted) “#Earthquake: Twitter as a Distributed Sensor System” Transactions in GIS.

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#Earthquake: Twitter as a Distributed Sensor System

Our work on using social media continues to develop and we have recently had a paper accepted in Transactions in GIS, entitled “#Earthquake: Twitter as a Distributed Sensor System“. Below we present our abstract and some of the results.

Social media feeds are rapidly emerging as a novel avenue for the contribution and dissemination of information that is often geographic. Their content often includes references to events occurring at, or affecting specific locations. Within this paper we analyze the spatial and temporal characteristics of the twitter feed activity responding to a 5.8 magnitude earthquake which occurred on the East Coast of the United States (US) on August 23, 2011. We argue that these feeds represent a hybrid form of a sensor system that allows for the identification and localization of the impact area of the event. By contrasting this to comparable content collected through the dedicated crowdsourcing ‘Did You Feel It?’ (DYFI) website of the US Geological Survey we assess the potential of the use of harvested social media content for event monitoring. The experiments support the notion that people act as sensors to give us comparable results in a timely manner, and can complement other sources of data to enhance our situational awareness and improve our understanding and response to such events.

The movie below show geolocated tweets with references to the earthquake through keyword (earthquake or earth and quake) and hashtag search (#earthquake or #quake) for the first hour after the earthquake.

The following images give a glimpse at some of our analysis.

Response pattern as function of distance from epicenter for the first 400 seconds after the earthquake. At the top we see a plot of (reaction time, distance) of all tweets during that period. At the bottom we show the histogram of the number of tweets as a function of distance.
Locations of the 40 tweets in the shaded area of the figure above overlaid over the USGS CDI scale map. Tweet locations are marked as green circles. Color-coding in the graph is ranging from red (high perceived intensity) to yellow (lower perceived intensity). The dashed line shows a distance of approximately 950 km (8.5 degrees of angular distance) from the epicenter.

The movie below gives you an idea of some of the tweet content:

Full reference to this paper is:

Crooks, A. T.,  Croitoru, A.,  Stefanidis, A. and Radzikowski, J. (acepted) “#Earthquake: Twitter as a Distributed Sensor System” Transactions in GIS.

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The Twitter Languages of London

Last year Eric Fischer produced a great map (see below) visualising the language communities of Twitter. The map, perhaps unsurprisingly, closely matches the geographic extents of the world’s major linguistic groups. On seeing these broad patterns I wondered how well they applied to the international communities living in London. The graphic above shows the spatial …

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ElectionGauge

A project we have been working on at GMU called ElectionGauge has now gone live (although still under development). The idea about the project is tie geo-spatial analysis, linguistic analysis, and social network analysis to analyze Twitter responses to the upcoming US elections in real time with the aim of predicting election results.
One question we are exploring is  do the tweets of users match the speech of candidates? For example, as Maksim Tsvetovat, one of the co-founders says “repeal Obamacare” might identify you as Tea Partier, while “legalize marijuana” puts you in Ron Paul’s camp. While still in beta, below is snapshot from the site:

Find out more see: @maksim2042, @JackieKazil & @ElectionGauge or at Tech Cocktail

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ElectionGauge

A project we have been working on at GMU called ElectionGauge has now gone live (although still under development). The idea about the project is tie geo-spatial analysis, linguistic analysis, and social network analysis to analyze Twitter responses to the upcoming US elections in real time with the aim of predicting election results.
One question we are exploring is  do the tweets of users match the speech of candidates? For example, as Maksim Tsvetovat, one of the co-founders says “repeal Obamacare” might identify you as Tea Partier, while “legalize marijuana” puts you in Ron Paul’s camp. While still in beta, below is snapshot from the site:

Find out more see: @maksim2042, @JackieKazil & @ElectionGauge or at Tech Cocktail

Continue reading »
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