Liang, Yuan (2013-08). Event Modeling in Social Media with Application to Disaster Damage Assessment. Master's Thesis. Thesis uri icon

abstract

  • This thesis addresses the modeling of events in social media, with an emphasis on the detection, tracking, and analysis of disaster-related events like the 2011 Tohuku Earthquake in Japan. Successful event modeling is critical for many applications including information search, entity extraction, disaster assessment, and emergency monitoring. However, modeling events in social media is challenging since: (i) social media is noisy and oftentimes incomplete, in the sense that users provide only partial evidence of their participation in an event; (ii) messages in social media are usually short, providing only little textual narrative (thereby making event detection difficult); and (iii) the size of short-lived events typically changes rapidly, growing and shrinking in sharp bursts. With these challenges in mind, this thesis proposes a framework for event modeling in social media and makes three major contributions: The first contribution is a signal processing-inspired approach for event detection from social media. Concretely, this research proposes an iterative spatial- temporal event mining algorithm for identifying and extracting topics from social media. One of the key aspects of the proposed algorithm is a signal processing-inspired approach for viewing spatial-temporal term occurrences as signals, analyzing the noise contained in the signals, and applying noise filters to improve the quality of event extraction from these signals. The second contribution is a new model of population dynamics of event-related crowds in social media as they first form, evolve, and eventually dissolve. Toward robust population modeling, a duration model is proposed to predict the time users spend in a particular crowd. And then a time-evolving population model is designed for estimating the number of people departing a crowd, which enables the prediction of the total population remaining in a crowd. The third contribution of this thesis is a set of methods for event analytics for leveraging social media in an earthquake damage assessment scenario. Firstly, the difference between text tweets and image tweets is investigated, and then three features - tweet density, re-tweet density, and user tweeting count - are extracted to model the intensity attenuation of earthquakes. The observation that the relationship between social media activity vs. the loss/damage attenuation suggests that social media following a catastrophic event can provide rapid insight into the extent of damage.
  • This thesis addresses the modeling of events in social media, with an emphasis on the detection, tracking, and analysis of disaster-related events like the 2011 Tohuku Earthquake in Japan. Successful event modeling is critical for many applications including information search, entity extraction, disaster assessment, and emergency monitoring. However, modeling events in social media is challenging since: (i) social media is noisy and oftentimes incomplete, in the sense that users provide only partial evidence of their participation in an event; (ii) messages in social media are usually short, providing only little textual narrative (thereby making event detection difficult); and (iii) the size of short-lived events typically changes rapidly, growing and shrinking in sharp bursts. With these challenges in mind, this thesis proposes a framework for event modeling in social media and makes three major contributions:



    The first contribution is a signal processing-inspired approach for event detection from social media. Concretely, this research proposes an iterative spatial- temporal event mining algorithm for identifying and extracting topics from social media. One of the key aspects of the proposed algorithm is a signal processing-inspired approach for viewing spatial-temporal term occurrences as signals, analyzing the noise contained in the signals, and applying noise filters to improve the quality of event extraction from these signals.



    The second contribution is a new model of population dynamics of event-related crowds in social media as they first form, evolve, and eventually dissolve. Toward robust population modeling, a duration model is proposed to predict the time users spend in a particular crowd. And then a time-evolving population model is designed for estimating the number of people departing a crowd, which enables the prediction of the total population remaining in a crowd.



    The third contribution of this thesis is a set of methods for event analytics for leveraging social media in an earthquake damage assessment scenario. Firstly, the difference between text tweets and image tweets is investigated, and then three features - tweet density, re-tweet density, and user tweeting count - are extracted to model the intensity attenuation of earthquakes. The observation that the relationship between social media activity vs. the loss/damage attenuation suggests that social media following a catastrophic event can provide rapid insight into the extent of damage.

publication date

  • August 2013