Abstract
1.Introduction
Social media gives us instant access to a continuous stream of information generated by users around the world. This enables real-time monitoring of users’ behavior (Abbar et al., 2015), events’ life-cycles (Weng and Lee. 2010), and large-scale analysis of human interactions in general. Social media platforms are also used to propagate influence, spread content, and share information about events happening in real-time. Detecting the location of events directly from user-generated text can be useful in different contexts, such as humanitarian response, detecting the spread of diseases, or monitoring traffic. In this abstract, we define a system that can be used for any of the purposes described above, and illustrate its usefulness with an application for locating traffic-related events (e.g., traffic jams) in Doha.
Social media gives us instant access to a continuous stream of information generated by users around the world. This enables real-time monitoring of users’ behavior (Abbar et al., 2015), events’ life-cycles (Weng and Lee. 2010), and large-scale analysis of human interactions in general. Social media platforms are also used to propagate influence, spread content, and share information about events happening in real-time. Detecting the location of events directly from user-generated text can be useful in different contexts, such as humanitarian response, detecting the spread of diseases, or monitoring traffic. In this abstract, we define a system that can be used for any of the purposes described above, and illustrate its usefulness with an application for locating traffic-related events (e.g., traffic jams) in Doha.
| Original language | English |
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| Title of host publication | ARC'16 Qatar Foundation Annual Research Conference Proceedings |
| Number of pages | 4 |
| DOIs | |
| Publication status | Published - Mar 2016 |