Social media for fighting forest fires

Assessing Credibility and Relevance ofUser-Generated Content on Crisis Events Frank Ostermann Laura Spinsanti European Commission – Joint Research Centre Institute for Environment and Sustainability Digital Earth and Reference Data Unit

2Outline1. Some background on social media in crisis contexts

2 Opportunities for crisis responseCrisis Map of Haiti 2010• near real time• Heterogeneous input• Distributed information processing (crisis mapping Stand-by Tasks Force) www.ushahidi.com

Opportunities for crisis responseCalifornia wildfires 2007• Near real time• Heterogeneous input• More centralized information processing (basically one journalist)

Why not treat information from the citizens as another type of sensor data? + =!

2The Big Picture: Digital Earth’s Nervous System• Multi-layered, inter-operable data sets• Linked and open data• Real-time data input through ubiquitous sensors• Initiatives like GEOSS, Eye on Earth, …

2The Big Picture: Citizens as sensors • Citizens as nervous system • Processing perceptions and knowledge • Feeding the DE Nervous System with UGC stimuli

2Hype, Buzzwords and Platforms aplenty…

2Typology of User-Generated Geographic Content Geographic Explicit Implicit Volunteered (geo)spatial information “True” Volunteered Geographic (VSI). Information (VGI) in the strictest Explicit sense Wikipedia articles about non- geographic topics containing place Open Street Map. names, FoursquareVolunteered Citizen-generated geographic Citizen-generated (geo)spatial content content (CGGC). (CGSC) Implicit Public Tweets referring to the Public Tweets containing a place name properties of an identifiable place.

User-Generated Content during Crisis Events

Use of VGI during crisis events• Horizontal communication during response and recovery• Advantages: • Fast!!! (can beat Earthquake waves…) • Familiar (to users, at least) • Localized• Disadvantages: • Fast!!! (rumor mill…) • Uncertain sources, uncertain accuracy • Data Deluge

Social media offer Managing crisis events needs• rich up-to-date information • up-to-date information• new paths of communication • redundant paths of communication• noise and uncertain lineageand accuracy of the • high-quality and reliableinformation information

What social media offer… What crisis management needs… rich up-to-date information up-to-date information new paths of communication redundant paths of communicationnoise and uncertain lineage and high-quality and reliable information accuracy of the information

Big Data – Big ChallengesHuman volunteered curation faces limits of• Sustainability• ScalabilityProposed Solution: Automated filtering and quality assessment

2Outline2. Geographic context for credibility and relevance

The two main challenges:1. Flood of information -> What is relevant?2. Quality of information -> What is credible and valid?“Back at hotel. Fire skirted (Canada BCGovFireInfo): “Importantround village. Little evidence of notice from the Reg Dist of Bulkley-significant damage. Helicopters Nechako regarding evacuations duestill overhead damping scrub. to wildfires in the areaBeer unaffected” http://ow.ly/2sBxH” “Are you a fireman? Cause you’re always there to extinguish the fire inside my heart.”

Elements of quality assessment Source Content Credibility Relevance Context Location

Relevance• Main criterion: Match with information needs of a user• Three perspectives (Saracevic 1975, Hjorland 2010): • User (What s/he thinks is relevant) • System (Results matching query) • Subject knowledge (goal-, task-oriented)• Geographic relevance vs. geographic aspect of relevance• Aspects include • Topicality (Content) • Location/Origin (Location, Context) • Novelty (Content)

RelevanceInformation on the task and user:• Motivation or objectives (e.g. decision support, evacuation routes, …)• Background knowledge• Own social media / network connections and trusted sourcesInformation on the context:• Geographic characteristics• Linguistic and semantic aspects• Heterogeneity and amount of similar informationInformation on the content:• Timeliness• Topicality

Credibility• Two main characteristics: • Trustworthiness (affecting source credibility) (Source, Context) • Expertise (affecting information credibility, or accuracy) (Location, Source)• Two main heuristics (Metzger et al 2010) • Social confirmation (what do others do or say?) (Source, Context) • Expectancies (what do I already know?) (Location, Context)

CredibilityInformation on the source (i.e. a user): • Comments by peers • Frequency and duration of participation • LocationInformation on the data: • Confirming/contradicting information (thematic clusters) • Spatio-temporal clusters • SemanticsInformation on the context: • Population density • Landcover • Social media usage

Context, Context, ContextTackle the context deficit: • Source characteristics: high number of followers could indicate trustworthiness and/or expertise (social confirmation) • Context characteristics: land cover, population density, other UGC, could indicate trustworthiness and accuracy (expectancy) Our focus: Geographic context

2Outline3. Twitter and Flickr for fighting forest fires

Project Overview• Opportunistic sensing approach• Geographic focus on Europe• Collaboration on-going with: • European Forest Fire Information System (effis.jrc.ec.europa.eu/) • European Media Monitor (emm.newsbrief.eu/overview.html) • Google • Universitat Jaume I de Castellón (Institute of New Imaging Technologies: www.init.uji.es Geographic Information research group: www.geoinfo.uji.es)

Research Objectives1. Deploy a system for using UGC in crisis decision support on forest fires • Integration • Quality control • Communication of risk/uncertainty, alerts2. Assess the added value of using UGC for forest fireresponse.

Forest fire characteristics • Dynamics require near real-time processing • Less signals since often in sparsely populated areas • Predictability and recurrence facilitate sensor and model calibration

Example pieces of information: Text MessagesMicroblogging, e.g. Twitter  User: “Back at hotel. Fire skirted round village. Little evidence of significant damage. Helicopters still overhead damping scrub. Beer unaffected”  User: “The forest fire is sooo close! We are now on evacuation notice. SOO nervous!”  News: “Okanagan forest fire forces resort evacuation: About 60 people fled in boats from a forest fire in B.C.'s Okanagan…http://bit.ly/9cstp7”  Governative (Canada BCGovFireInfo): “Important notice from the Reg Dist of Bulkley-Nechako regarding evacuations due to wildfires in the area http://ow.ly/2sBxH”

Example pieces of information: Visual and Annotations

Workflow overview Retrieval / Sensing W20B CONAVI Analyzer Quality Assessment (local or WPS) Spatio-Temporal CONAVI Clusterer Clustering (local or WPS) Dissemination & Alerting WFS, WMS, SES, SMS, ...

Initial context info and tasks • Spatio-temporal location • Geocoding • Topicality • Natural Language Processing • Landcover • Corine Landcover Classification • Known fires/hotspots • MODIS hotspot data • Population density • Official LAU2 statistics

Detailed implemented CONAVI workflow Twitter Flickr EFFIS European Media Monitor Dissemination Stream- Search Hotspot Geo-coding API SMS, WFS, WMS, RSS, SES ing API API Data 2.1 Topicality Scheduled PLSQL job 3.1 Spatio-temporal 1.1 Retrieval clustering Scheduled Java code Scheduled Python script accessing APIs 2.2 Geo-Coding a) Scheduled PLSQL job calling SatScan job b) Scheduled Java code 2.3 Geographic context 1.2 Storage Scheduled PLSQL job Scheduled Java code 3.2 Quality Re-Assessment writing to DBMS Scheduled PLSQL job 2.4 Quality Assessment Scheduled PLSQL job Oracle DBMS

Forest Fires 2010 on TwitterUsing Streaming API used with two filtering methods: Keywords in several languages  English, Portuguese, Spanish, French, Italian, Greek, German, Catalan  (wild) fire  alert  brush  forest  alarm  shrub  canadair  evacuation  helicopter  hectares  firefighter  brushwood  Data dimension: ~24,5 GB “Gardenhose” stream  Retrieves random subset of 5-15% of all Tweets  Data dimension: ~1200 GB Only 1% of all Tweets is geocoded with coordinates!

Forest Fires 2010 on FlickrUsing Java Search API  No limit to past dates  Therefore, limited search to case study about French fires so far  Same keywords used as for Twitter search  In title, description, tags  Returned entries: 700,000+ Even less than ~1% of all images on Flickr are geocoded with coordinates.

WHATMessage content and Terms distribution

WHATMessage content and Terms distributionThe average number of words per message in our dataset is 17 words.• In 68% of the Tweets only one keyword exists.• In 24.4% of Tweets there are two keywords• In 4.4% of Tweets there are three• In 0.3% (four messages) there are four keywords. 1 terms occurrence

Scoring UGC• Sum of weighted scores: QS(UGCj) = ∑Ni=1wisji • with w being weight for criterion i, and s being the score for the VGI object j• Topicality: keyword-based• Proximity: next concurrent reported hotspot• Land cover: Forest, no-Forest, Built-up• Population Density: Risk factor• Information clusters: Similar messages or lone signal?

2Outline4. Some examples and results

Case Studies:2010 and 2011 French forest fires and their social media echo

Overview of 2010 data – sharding for success At least one Contains Already Geo- Summer Located French keyword geocoded 2010 in France keyword incendie coded later*Twitter > 8 million 611274 6754 31 1123 437 > 700Flickr 61697 458 133 293 243 thousand Total 672971 7212 164 1416 680 * Geo located with Yahoo!Placemaker

Geocoding UGC 2011 TWITTER FLICKR August 2010 August 2011 August 2010 August 2011Number of retrieved UGGC 2,904,065 7,996,228 7,991 17,850 50%Percentage with toponym 35% 27% 53%Percentage with geocode 1.1% 0.92% 20% 21% Geocoders used: • GISCO/LAU2 brute string matching • European Media Monitor algorithms • Yahoo! Placemaker • Exploit triplets of Location information (Source, Content, Message)

WHENTweeting on Forest Fires 96% during first three days 80% during first two days At least 7 Tweets, 35 fires

Spatio-temporal clustering for event detection SatScan Total True False False Parameter number of Positives Positives negatives Setting Clusters 4 6 3 3 3 3 4 3 1 3 2 74 7 67 0 1 8 6 2 0

Case study on French forest fires:spatio-temporal clustering (Case 4)

Space-time cube TIME DIMENSION (showing un- clustered VGI as grey dots)

2011 French geo-located VGI, MODIS hotspots, and forest cover

2011 French forest fires, VGI clusters, and forest cover

Some preliminary results:• Simple keyword queries suffice• Additional Geo-coding indispensable• Topicality and context filtering plus spatio-temporal clustering crucial• Able to detect fires from Tweets and Flickr images by spatio- temporal clustering• Relevance, credibility and overall quality vary greatly, thus more rules and human assessment needed

Thank you for your attention!{Frank.Ostermann, Laura.Spinsanti}@jrc.ec.europa.eu

Social media for fighting forest fires

by jrc_vgi_ff on Apr 26, 2012

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Social media for fighting forest fires Presentation Transcript