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  • Burgeoning area of study Facebook use in VT, then in NIU, Community websites in the S. Cal wildfires Earthquakes – popular online forum In this work, we look at Twitter use during 2 events – a flood and a grassfire
  • Microblogged information is one source that may contribute to situational awareness ; our goal is to identify and measure features of communication that could support technology development – eventual goal is to automatically extract information communicated during time and safety critical situations, but first we need to understand what these communications are all about We’re concerned with how people faced with a natural hazard communicate and figure out what to do Role Twitter plays in that process
  • RR – NOAA warning in late Feb, river runs south to north – ice jamb issues Above flood level for several weeks March 28 it crested all time record high in Fargo, and on April 16 in Winnipeg crest of a flood is the highest level that the water reaches before receding
  • Fires happened April 9-10 – central and southern OK At least 60 injuries, 31 counties a state of emergency, 8 suffered damage, close to 270 buildings destroyed and over 100,000 acres burned
  • Going to talk about the method METHOD is a big contribution How we go about finding tweets in the midst of the chaos that is the Twitterverse that relate to our subject of study, and then distill from there to get a good representation of what is happening in the Twitterverse? Studies thus far have talked about the growth of Twitter, who uses it, trending topics, etc. But how do we gain an understanding of what people are saying? Mention ‘Big Science’???
  • Choose search terms quickly – based on examination of the public twitter stream, search of mainstream media Choosing search terms is an aspect of researching CMC in crisis and disaster that involves tradeoffs. Decisions are made on what to search for, and with those decisions comes a limitation on the data that are collected. In the 2 cases presented here, our judgment is that we retrieved a relevant and seemingly good sample
  • Close to 5 thousand for RR Close to 4 thousand for OK
  • Took only those user streams that had more than three tweets that included a search term Went through and coded each tweet as on- or off-topic Got a set of on-topic tweets, went through and identified only those who were “local individuals” Important to look at local individuals – both events were contained to specific areas, neither had a large national or international response – there was a lot of communication among residents of each area Rationale – want to look at more active users, some people fall off, only tweet once, didn’t keep any low-activity users
  • Almost 22,000 tweets All hand-coded Got waist-deep in the data, wanted to understand what people were talking about at a detailed level. We knew they were tweeting about the flood and the fires…but what were they saying? Will now launch into a discussion of what local individuals were tweeting about
  • Features of geo-location information – a feature is counted for every category of geo-location information mentioned Possible for one tweet to include two or more features – the one here has both ‘city name’ and ‘highway’ The second one has both ‘street name/address’ and ‘city name’
  • Ground-up – read and analyze every tweet Geo-location – location of people, events, shelters, evacuation sites 5 categories Graph shows geo-location occurrences as a percentage of all on-topic tweets Subsequent analysis is showing that location information is frequent in this type of Twitter communication
  • 2 main reasons for differences in geo-location info: difference between fire and flood, difference between people in ‘warning’ and ‘impact’ stages Wildfire unexpected, floods predicted Geo-location information more useful in wildifire – fires are erratic, know exactly where they’re burning, where evacs ordered, where shelters set up Floods – people know where the river is, severe flooding has happened before, not as much geo-location info being broadcast Length of each event RR had a long period of ‘warning’ – outcomes are uncertain, not sure where to evacuate, what areas will be hardest hit, etc. OK had very little warning – almost all these tweets were during ‘impact’ and ‘recovery’ stages – fires had swept through, destroyed buildings, etc. – people needed to know where to direct resources, what sheltering options were available Indicates for now that geo-location information will vary depending on the type of disaster (among other factors) – gives us a basis for anticipating future
  • No easily-identifiable geo-location information, but info that gives an idea about the location of both Twitterer and the emergency Something to pay attention to – location isn’t always communicated in a straightforward manner We want to automatically extract critical information OK – 8% contain geo-reference info RR – 6% contain geo-reference info
  • One additional phenomenon of note - markedness Certain places, landmarks, etc. become taken-for-granted and expected when referred to in more general terms. For example “Pacific Ocean” Because we searched on ‘red river’ and not ‘sheyenne river’ – red river was noted as ‘unmarked’ in some tweets, while the Sheyenne was marked Not making claims this is how it is always, but is something to watch for re: automatic extraction
  • Phenomena - Implicature Meaning of a statement goes beyond semantic content Examples Tweet about the level of the river, but also a warning, especially for those who are area residents and know what level dikes can hold to Geo-location, location referencing, – but also a warning, maybe a request for help or advice Again – update about help arriving, but also a warning? Situation is dire near Velma Need to watch out for linguistic intricacies that are conveyed in these 140 character messages, there is a lot of meaning bound up in some of them that may contribute to SA
  • These categories built up based on the qualitative coding These categories represent information that is actionable and may contribute to situational awareness
  • OK higher in: Evacuation info, Damage/Injury, Fire Line/Emergency Location, Wind RR higher in: Preparatory Activity, Flood level, Weather, Volunteer Info Based on differences in hazard type Though there are differences, overall, situation updates were distributed throughout the data collection periods for each event and concentrated during the height of each emergency Fire line/Hazard location and Flood Level – very similar
  • OK – all tweets collected during 6 day collection period
  • OK all on topic tweets
  • OK Sit update tweets
  • RR – all tweets collected during 51 day collection period
  • RR all on topic tweets against all tweets
  • RR Sit update tweets and all on-topic tweets see what percentage of ALL tweets See what percentage of On-Topic tweets
  • Additional information that contributes to our understanding of Twitter behavior – Analysis of retweets Higher percentages in the Sit. Update and Geolocation categories, than the overall On-topic, indicating a preference for re-tweeting this sort of information Twitter users have a preference for pass along this type of information
  • Begun to characterize the types of information communicated during mass emergency More information that we might glean from referring to traditional emergency management organizational frameworks
  • SPEND TIME Evolved from our analysis of our coding scheme, also fleshes out standard information categories used in emergency response This framework doesn’t represent what emergency managers communicate, it shows what affected people “on the ground” talk about Example – Hazards Location – this may be general, precise or relative – this gets at the heterogeneity of the information conveyed on Twitter by those who are directly affected Like what we refer to with geo-location, which may be very precise, and geo-referencing, which may be relative These blue parts show the intricacies of the tweets, that it’s not straightforward “warning” or “hazard location” – there are specific aspects to these information types, and our goal is to take what we see as humans and help machines accomplish these same organizational tasks For example, in the ‘preparation’ category – it may happen at the personal or community levels, which is a helpful distinction when we think about the different communities that may benefit from this information We articulate more – new ways in which people deal with preparation, etc. Personal vs. community is interesting The way we start to construct this info later on – we may want to know what individuals are doing (individual space) and communicty space, ho w we deal with the info may shape the SA overview
  • First is personal response during the OK fires Second is community response Both give an indication of what is happening “on the ground” Fargo shutting down sandbagging ops tonight @ 6 pm. Will have enough bags (3 mil) by then
  • SPEND TIME Evolved from our analysis of our coding scheme, also fleshes out standard information categories used in emergency response This framework doesn’t represent what emergency managers communicate, it shows what affected people “on the ground” talk about Example – Hazards Location – this may be general, precise or relative – this gets at the heterogeneity of the information conveyed on Twitter by those who are directly affected Like what we refer to with geo-location, which may be very precise, and geo-referencing, which may be relative These blue parts show the intricacies of the tweets, that it’s not straightforward “warning” or “hazard location” – there are specific aspects to these information types, and our goal is to take what we see as humans and help machines accomplish these same organizational tasks For example, in the ‘preparation’ category – it may happen at the personal or community levels, which is a helpful distinction when we think about the different communities that may benefit from this information We articulate more – new ways in which people deal with preparation, etc. Personal vs. community is interesting The way we start to construct this info later on – we may want to know what individuals are doing (individual space) and communicty space, ho w we deal with the info may shape the SA overview
  • Not possible to sift through Twitter to find actionable, critical information Need a deep-seated understanding of what people “on the ground” are doing during these time and safety-critical situations Allows actors in the space to have a better understanding of what’s going on – Twitter one place people are looking Goal of EM is to have SA People “on the ground” have the means to reach large audiences via Twitter Affordances of Twitter allow us to gain information

Transcript

  • 1. Microblogging During Two Natural Hazards Events: What Twitter May Contribute to Situational Awareness Colorado University of Colorado at Boulder Sarah Vieweg Kate Starbird Amanda L. Hughes Leysia Palen
  • 2. Social Media & Emergency
    • Facebook, Community Websites, Online Forums
    2007 VT Shootings 2007 Southern California Fires 2008 NIU Shootings 2008 Sichuan Earthquake
  • 3. Situational Awareness
      • “ All knowledge that is accessible and can be integrated into a coherent picture, when required, to assess and cope with a situation”
      • - Sarter & Woods, 1991
  • 4.  
  • 5.  
  • 6. Studying Twitter “ Twitter space” Tweet Tweet Tweet Tweet Tweet Tweet Tweet Tweet Tweet Tweet Tweet Tweet Tweet Tweet Tweet Tweet Tweet Sample
  • 7. Search Terms
    • RR Floods: March 8 – April 27
    • Search Terms: red river, redriver
    • Returned: 13,153 tweets
    • OK Fires: April 8 – 13
    • Search Terms: oklahoma, okfire, grass fire, grassfire
    • Returned: 6674 tweets
  • 8. Unique Users
    • RR Floods: March 8 – April 27
    • Returned: 4983 unique Twitter users
    • OK Fires: April 8 – 13
    • Returned: 3852 unique Twitter users
  • 9. Twitter User Tweet Stream User Tweet Streams
  • 10. Matches Search Terms User Tweet Streams
  • 11. But what are they talking about here? User Tweet Streams
  • 12. User Streams
    • RR Floods
    • Returned: 4,592,466 tweets
    • OK Fires
    • Returned: 1,986,091 tweets
  • 13. Threshold Users with >3 tweets that include a keyword On-topic: From MWCFD: Eastwood addition, a lot of damage. Part has power, part doesn't. Won't open it back up Off-topic: My Horoscope for today: Your hard work always pays off -- although it might take longer than you want it to.  Local Individuals
  • 14. Final Data Sets RR Local Individual set: 19,162 tweets 49 unique Twitter users OK Local Individual set: 2779 tweets 46 unique Twitter users
  • 15. Geo-location Information
    • 65mph winds in Newcastle Oklahoma - Turner Turnpike closed down because of low visibility
    • 25th street past 40th ave in Fargo has been closed
  • 16. Geo-Location Information
  • 17. Differences between RR and OK
    • Wildfire vs. Flood
    • “ Impact” vs. “Warning”
  • 18. Location Referencing
    • Tweets that include information that use one place as a reference for another or the mention of a location via a landmark
    • We are on the western central edge of town , so we are a fair distance from any water for now.
    • Could see flames above the trees two miles away from my house
  • 19. Markedness
    • The river is now over 40 feet and there has been a breach in the dike; neighborhood a few blocks south of us is evacuating.
    • The Sheyenne River went up 10 feet in 12 hours…
  • 20. Implicature Current Red River level in Grand Forks is 45.57 feet More than 7 grass fires on going, house burns near Childress Tx Guard will send helicopters to assist in #OKFires near Velma
  • 21. Situational Updates
    • Warning
    • Preparatory Activity
    • Fire/Hazard Location
    • Flood Level
    • Weather
    • Wind
    • Visibility
    Evacuation Information Volunteer Information Animal Management Damage/Injury reports Road Conditions Advice - emergency - information space
  • 22. Differences in Situational Updates OK Fires higher in: Fire Line/Emergency Location Wind Evacuation Info Damage/Injury RR Floods higher in: Flood Level Preparatory Activity Weather Volunteer Info
  • 23.  
  • 24.  
  • 25.  
  • 26.  
  • 27.  
  • 28.  
  • 29. Tweets with Geo-location and Situational Update information are more likely to be re-tweeted Re-Tweets
  • 30. Situational Awareness Features
    • Bottom-up communication of information
    • Information people orient around
    • Possibility to collate information and get an idea of the “big picture”
  • 31. Warning (hazards-focused) Response to Warning Preparation (pre-warning) Evacuation Sheltering Animal Management Damage & Injury Advice Other Environmental Conditions Hazards Location (pre-warning) ... ... ... personal community personal community preparation possible exploitation info space weather visibility road general relative precise personal location specific location historical hazard
  • 32. Personal: @user It can start anywhere - a simple spark can burn down a whole neighborhood. I watered the yard and roof and hope for the best Community: Red in Fargo jumped a foot overnight now @34 and rising. Urgent call for sandbagging volunteers. Head to the Dome or call 476-4000 Response to Warning
  • 33. Warning (hazards-focused) Response to Warning Preparation (pre-warning) Evacuation Sheltering Animal Management Damage & Injury Advice Other Environmental Conditions Hazards Location (pre-warning) ... ... ... personal community personal community preparation possible exploitation info space weather visibility road general relative precise personal location specific location historical hazard
  • 34. Precise: At least a dozen houses have burned east of Lake Draper. Area includes land between SE 119 to SE 149 & Hiawassee & Anderson Rds Relative: We R on the edge of the city hre, but 20-30 miles from the fires Hazards Location
  • 35. Contribute to Situational Awareness
  • 36. Why Does This Matter? One way to tackle the problem of managing crises is to look at how people communicate about them Primes us for thinking about how to extract information and shape behavior
  • 37. Thank You! Project Epic Empowering the Public with Information in Crisis http://epic.cs.colorado.edu/ Acknowledgements: This work is supported by NSF IIS-0546315 and IIS-0910586