"OMG, from here, I can see the flames!": a use case of mining location based social networks to acquire spatio-temporal data on forest fires

By B. Longueville, Robin S. Smith, Gianluca Luraschi. 2009

352 citations recorded by the source corpus.

Subfields: Geography, Planning and Development, Transportation, Signal Processing

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Citations in the corpus

Towards Decision Support and Goal Achievement: Identifying Action-Outcome Relationships From Social Media (2015, neutral): “Similar studies have been conducted in urban informatics [8], mental health [9, 16], natural disaster monitoring [11, 41], and other domains.”
Information credibility on twitter (2011, neutral): “Twitter has been used widely during emergency situations, such as wildfires [6], hurricanes [12], floods [32, 33, 31] and earthquakes [15, 7].”
#Earthquake: Twitter as a Distributed Sensor System (2013, background): “The temporal, spatial, and social dynamics of Twitter activity during a major forest fire were addressed by De Longueville et al. (2009) in an effort to investigate the performance of Location-Based Social Networks (LBSN) as information creation and dissemination platforms. In the same direction, an”
Processing Social Media Messages in Mass Emergency (2014, background): “…information [Chowdhury et al. 2013; Iyengar et al. 2011] By location Ground-zero information from ground zero (victims’ reports, bystanders) [De Longueville et al. 2009; Ao et al. 2014] Near-by areas information originating close to the affected areas [De Longueville et al. 2009]…”
Real-Time Detection of Traffic From Twitter Stream Analysis (2015, background): “[27] analyze geo-tagged tweets to detect forest fire events and outline the affected area.”
Hip and trendy: Characterizing emerging trends on Twitter (2011, methodology): “Sakaki et al. (2010) study social, spatial, and temporal characteristics of earthquake-related tweets, and De Longueville, Smith, and Luraschi (2009) describe a method for using Twitter to track forest ﬁres and the response to the ﬁres by Twitter users.”
Twitter under crisis: can we trust what we RT? (2010, background): “[1] describe the use of Twitter during a forest fire close to Marseille in mid-2009, they identified different types of twitter users: those related to mass media outlets, those acting as aggregators of information, and normal citizens.”
Predicting information credibility in time-sensitive social media (2013, methodology): “We perform a comparison among classifiers trained from: a) Full collection of cases (NEWS/CHAT/UNSURE), b) Merged collection of cases (NEWS versus THE REST), and c) Pruned collection of cases (NEWS/CHAT).”
A geographic approach for combining social media and authoritative data towards identifying useful information for disaster management (2015, background): “This is a common limitation of studies that work with text-based analysis of social media (e.g. De Longueville et al. 2009, MacEachren et al. 2011, Terpstra and de Vries 2012, Fuchs et al. 2013, Spinsanti and Ostermann 2013), which could only be completely overcome either with a very costly manual…”