**Earthquake Observation by Social Sensors**

Takeshi Sakaki and Yutaka Matsuo *The University of Tokyo Japan*

#### **1. Introduction**

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Many studies have examined observation and detection of earthquakes using physical sensors. These systems require highly accurate physical sensors located over a broad area, necessitating great expense to set up the supporting infrastructure.

Social media have garnered much attention recently and the number of social media users has been increasing. Social media are kinds of media for social interaction among users. Users create contents for themselves and exchange them on social media. Social media include many kinds of forms, including weblog, wikis, videos and microblogs. One of the biggest characteristics of social media is *user-generated contents*.

Social media users often make posts about what happened around them: live performance, sports events and natural disaster, including earthquake. Figure 1 depicts the graph of tweet counts and the sizes of earthquake on March 11th 2011, the day of the Great Eastern Japan Earthquake. It is apparent that tweet counts and earthquake occurrences are correlated. It means that when earthquakes occurs, social media users make posts about those earthquakes.

Along with the popularization of social media, new methods for earthquake observation are appearing. These method use information about earthquakes posted on the internet by users. For example, the web site *Did You Feel It?*, operated by the United States Geological Survey (USGS), gathers earthquake information from web-site users through a

Fig. 1. Size of earthquakes and change of tweet counts on the day of the Great Eastern Japan Earthquake

**iShake**

Fig. 3. Screenshot of *iShake*.

earthquake epicenter automatically.

Fig. 4. Image of the Toretter mechanism.

The Toretter mechanism is shown in Fig. 4.

**Toretter**

The iShake project has developed a smartphone application (Fig. 3) that uses a phone to measure acceleration during an earthquake and report those data to researchers for processing (CITRIS, 2011). This project, conducted by UC Berkeley, is designed to create a system that moves beyond *Did You Feel It?*. Data from smartphone applications can complement data obtained from ground monitoring instruments, thereby improving the

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*Toretter* extracts tweets referring to earthquakes and estimates the location of the earthquake epicenter using location information of those tweets(Sakaki et al., 2010). A temporal model and spatial model for earthquake detection are defined by social sensors. Then methods are proposed to detect earthquakes and to estimate the location of an

resolution and accuracy of earthquake intensity maps.

questionnaire format(Intensity, 2005). From the Twitter web-site, *Toretter* extracts tweets that refer to earthquakes and estimates the location of an earthquake's epicenter using location information included with those tweets(Sakaki et al., 2010)

These methods treat social media users as sensors. We designate these virtual sensors as *social sensors*, which entail no costs. Unfortunately, such sensors provide a signal that is extremely noisy because users sometimes misunderstand phenomena, sleep, and are not near a computer.

We introduce these methods and explain a process for earthquake detection by analyzing social sensor information. We introduce current studies and services for earthquake observation using *social sensors* . Moreover, we explain *Toretter* as an example and describe its mechanisms.
