Earthquake shakes Twitter users: real-time event detection by social sensors

By Takeshi Sakaki, Makoto Okazaki, Yutaka Matsuo. The Web Conference, 2010

4,214 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Epidemiology, Statistical and Nonlinear Physics

View Paper: https://www.semanticscholar.org/paper/e0f246f277e7e47abe458c3f36264ae8ad5b47f5

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

In Epidemiology

In Statistical and Nonlinear Physics

Citations in the corpus

Climate Change Sentiment on Twitter: An Unsolicited Public Opinion Poll (2015, methodology): “Such expressions may include what individuals are thinking, doing, feeling, etc. Twitter has been used to explore a variety of social and linguistic phenomena [11–13], and used as a data source to create an earthquake reporting system in Japan [14], detect influenza outbreaks [15], and analyze overa”
Discussion Graphs: Putting Social Media Analysis in Context (2014, background): “…informatics (Cranshaw et al. 2012; Schwartz et al. 2013), mental health (De Choudhury, Counts, and Horvitz 2013b; Golder and Macy 2011), natural disaster monitoring (De Longueville, Smith, and Luraschi 2009; Sakaki, Okazaki, and Matsuo 2010), finance (Bollen, Mao, and Zeng 2011) and other domains.”
Twitter Catches The Flu: Detecting Influenza Epidemics using Twitter (2011, neutral): “Sakaki et al. (2010) addressed the detection of earthquakes.”
Open domain event extraction from twitter (2012, background): “[49] train a classifier to recognize tweets reporting earthquakes in Japan; they demonstrate their system is capable of recognizing almost all earthquakes reported by the Japan Meteorological Agency.”
A geographic approach for combining social media and authoritative data towards identifying useful information for disaster management (2015, methodology): “Therefore, in the case of floods, it makes less sense to use georeferenced social media to do event detection, as has been previously done for earthquakes (Sakaki et al. 2010, Crooks et al. 2013).”
Predicting Responses to Microblog Posts (2012, positive): “Mining Twitter Social media has been used to detect events (Sakaki et al., 2010; Popescu and Pennac-chiotti, 2010; Popescu et al., 2011), and even predict their outcomes (Asur and Huberman, 2010; Culotta, 2010).”
Fine-grained location extraction from tweets with temporal awareness (2014, result): “This observation is consistent with the earlier finding that Twitter is an individual news media [10, 25, 29].”
Experimentation Standards for Crisis Informatics (2014, background): “3.3.1 Corpus Although many possible corpora of timestamped docu- ments exist, social media in particular has received more attention because of its sensitivity to changes in the underlying social and physical environment.”
Using Social Media to Enhance Emergency Situation Awareness: Extended Abstract (2015, background): “Disaster management using Twitter has recently been studied for humanitarian crises and natural disasters such as earthquakes, bushﬁres, and cyclones [Sakaki et al. , 2010; Vieweg et al. , 2010; Li et al. , 2012; McMinn et al. , 2014].”
#Earthquake: Twitter as a Distributed Sensor System (2013, methodology): “Harvesting information from social media feeds is in essence a web-mining process (Kosala and Blockeel 2000, Sakaki et al. 2010, Russell 2011).”
Processing Social Media Messages in Mass Emergency (2014, background): “Sakaki et al. [2010] detect hazards and crises such as earthquakes, typhoons, and large trafﬁc jams using temporal and spatial information.”
Comparing and combining sentiment analysis methods (2013, methodology): “Despite the wide use and popularity of some methods, it is unclear which method is better for identifying the polarity (i.e., positive or negative) of a message as the current literature does not provide a method of comparison among existing methods.”
Contextual Enrichment of Remote-Sensed Events with Social Media Streams (2016, background): “The goal of such systems [24] [14] is to provide users a big picture of an event in a way that rescuer can asses the situation and act accordingly.”
Event Summarization Using Tweets (2011, background): “In Figure 5 we plot the measure RECALL@30 on the task of retrieving Comment-Play, Comment-Play-Details, and CommentPlay-Names labeled tweets.”