Spatial, temporal, and content analysis of Twitter for wildfire hazards

Social media data are increasingly being used for enhancing situational awareness and assisting disaster management. We analyzed the wildfire-related Twitter activities in terms of their attributes pertinent to space, time, content, and network, so as to gain insights into the usefulness of social media data in revealing situational awareness. Findings show that social media data can characterize the wildfire across space and over time, and thus are applicable to provide useful information on disaster situations. Second, people have strong geographical awareness during wildfire hazards and are interested in communicating situational updates related to wildfire damage (e.g., containment percentage and burned acres), wildfire response (e.g., evacuation), and appreciation to firefighters. Third, news media and local authorities are opinion leaders and play a dominant role in the wildfire retweet network.

[1]  Leysia Palen,et al.  Pass it on?: Retweeting in mass emergency , 2010, ISCRAM.

[2]  Shady Elbassuoni,et al.  Practical extraction of disaster-relevant information from social media , 2013, WWW.

[3]  Xinyue Ye,et al.  We Know Where You Are: In Space and Place - Enriching the Geographical Context through Social Media , 2016, Int. J. Appl. Geospat. Res..

[4]  Kurt Hornik,et al.  Text Mining Infrastructure in R , 2008 .

[5]  Alexander Zipf,et al.  A geographic approach for combining social media and authoritative data towards identifying useful information for disaster management , 2015, Int. J. Geogr. Inf. Sci..

[6]  Mica R. Endsley,et al.  Toward a Theory of Situation Awareness in Dynamic Systems , 1995, Hum. Factors.

[7]  Yingcai Wu,et al.  Visual Analysis of Topic Competition on Social Media , 2013, IEEE Transactions on Visualization and Computer Graphics.

[8]  Timothy W. Collins,et al.  What Influences Hazard Mitigation? Household Decision Making About Wildfire Risks in Arizona's White Mountains , 2008 .

[9]  E. Chuvieco,et al.  Human-caused wildfire risk rating for prevention planning in Spain. , 2009, Journal of environmental management.

[10]  S. Schulte,et al.  Wildfire Risk and Climate Change: The Influence on Homeowner Mitigation Behavior in the Wildland–Urban Interface , 2010 .

[11]  Carlos Castillo,et al.  AIDR: artificial intelligence for disaster response , 2014, WWW.

[12]  Marielle Jappiot,et al.  Application of a geographical assessment method for the characterization of wildland–urban interfaces in the context of wildfire prevention: A case study in western Madrid , 2012 .

[13]  Fernando Diaz,et al.  Extracting information nuggets from disaster- Related messages in social media , 2013, ISCRAM.

[14]  I. Annesi-Maesano,et al.  Quantifying wildfires exposure for investigating health-related effects , 2014 .

[15]  Volker C. Radeloff,et al.  Wildfire risk in the wildland―urban interface: A simulation study in northwestern Wisconsin , 2009 .

[16]  Chen Huang,et al.  Microblogging after a major disaster in China: a case study of the 2010 Yushu earthquake , 2011, CSCW.

[17]  Nicole M. Vaillant,et al.  Analyzing the transmission of wildfire exposure on a fire-prone landscape in Oregon, USA , 2014 .

[18]  Mani Srivastava,et al.  Human-centric sensing , 2012, Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences.

[19]  E. Chuvieco,et al.  Integrating geospatial information into fire risk assessment , 2014 .

[20]  Steven Verstockt,et al.  Review of wildfire detection using social media , 2014 .

[21]  E. Chuvieco,et al.  Development of a framework for fire risk assessment using remote sensing and geographic information system technologies , 2010 .

[22]  R. Guha,et al.  What are we ‘tweeting’ about obesity? Mapping tweets with topic modeling and Geographic Information System , 2013, Cartography and geographic information science.

[23]  S. Fotheringham,et al.  Modeling the spatial variation of the explanatory factors of human-caused wildfires in Spain using geographically weighted logistic regression , 2014 .

[24]  Adam Acar,et al.  Twitter for crisis communication: lessons learned from Japan's tsunami disaster , 2011, Int. J. Web Based Communities.

[25]  Junfang Gong,et al.  Spatiotemporal Analysis of Housing Prices in China: A Big Data Perspective , 2017 .

[26]  Scott L. Goodrick,et al.  Wildland fire emissions, carbon, and climate: Wildfire-climate interactions , 2014 .

[27]  Brian H. Spitzberg,et al.  Mapping social activities and concepts with social media (Twitter) and web search engines (Yahoo and Bing): a case study in 2012 US Presidential Election , 2013 .

[28]  Anthony Stefanidis,et al.  #Earthquake: Twitter as a Distributed Sensor System , 2013, Trans. GIS.

[29]  A. Weaver,et al.  Detecting the effect of climate change on Canadian forest fires , 2004 .

[30]  Matthew P. Thompson,et al.  A real-time risk assessment tool supporting wildland fire decisionmaking , 2011 .

[31]  Charles W. McHugh,et al.  Wildfire exposure and fuel management on western US national forests. , 2014, Journal of environmental management.

[32]  Xiangyang Guan,et al.  Using social media data to understand and assess disasters , 2014, Natural Hazards.

[33]  Bruce Evan Goldstein,et al.  Skunkworks in the Embers of the Cedar Fire: Enhancing Resilience in the Aftermath of Disaster , 2008 .

[34]  Timothy W. Collins,et al.  Situating Hazard Vulnerability: People’s Negotiations with Wildfire Environments in the U.S. Southwest , 2009, Environmental management.

[35]  F Cheong,et al.  Social Media Data Mining: A Social Network Analysis of Tweets During the Australian 2010-2011 Floods , 2011 .

[36]  S. Young Behavioral insights on big data: using social media for predicting biomedical outcomes. , 2014, Trends in microbiology.

[37]  Gábor Csárdi,et al.  The igraph software package for complex network research , 2006 .

[38]  T. Swetnam,et al.  Warming and Earlier Spring Increase Western U.S. Forest Wildfire Activity , 2006, Science.

[39]  Cristina Vega-García,et al.  On the comparative importance of fire danger rating indices and their integration with spatial and temporal variables for predicting daily human-caused fire occurrences in Spain , 2011 .

[40]  Jie Yin,et al.  Emergency situation awareness from twitter for crisis management , 2012, WWW.

[41]  Leysia Palen,et al.  Microblogging during two natural hazards events: what twitter may contribute to situational awareness , 2010, CHI.

[42]  Matthew P. Thompson,et al.  Development and application of a geospatial wildfire exposure and risk calculation tool , 2015, Environ. Model. Softw..

[43]  Juan de la Riva,et al.  An insight into machine-learning algorithms to model human-caused wildfire occurrence , 2014, Environ. Model. Softw..

[44]  Keith C. Clarke,et al.  Do Global Cities Enable Global Views? Using Twitter to Quantify the Level of Geographical Awareness of U.S. Cities , 2015, PloS one.

[45]  Marina Kogan,et al.  Think Local, Retweet Global: Retweeting by the Geographically-Vulnerable during Hurricane Sandy , 2015, CSCW.

[46]  Qunying Huang,et al.  Geographic Situational Awareness: Mining Tweets for Disaster Preparedness, Emergency Response, Impact, and Recovery , 2015, ISPRS Int. J. Geo Inf..

[47]  F. Catry,et al.  Influence of territorial variables on the performance of wildfire detection systems in the Iberian Peninsula , 2013 .

[48]  Ming-Hsiang Tsou,et al.  Visualization of social media: seeing a mirage or a message? , 2013 .

[49]  J. Lee,et al.  Using Social Media for Emergency Response and Urban Sustainability: A Case Study of the 2012 Beijing Rainstorm , 2015 .