Dealing with Information
Overload When Using Social
Media for Emergency
Management: Emerging Solutions
Starr Roxanne Hiltz...
Contents
A focused literature review of issues
related to information overload and
potential solutions to the problem, in
...
Information Overload in the
Context of CMC
the delivery of too many communications and
to an increase in social density th...
A review that covers:
Emergent social conventions
use of “voluntweeters,” and technical
features of social media , useful ...
Social conventions in the Twitterverse
Hashtags help with finding and filtering
@ to indicate that tweet is about or for a...
Human “Voluntweeters”
Trained volunteers, off-site, who aid the
onsite emergency managers by
monitoring and filtering soci...
Examples of Human
“Voluntweeters”
Example 1: Haiti, “tweek the tweet” suggested
hastag markups ( eg, “#need”); translated ...
Machines- Natural Language
processing,1
(Verma et al., 2011) describes a
program developed to automatically
identify messa...
Machines- Natural Language
processing, 1 continued
Tweets that contribute to situational
awareness are likely to be writte...
Natural Language Processing, 2
Cameron, Power, Robinson, and Yin (2012)
developed a platform and client tools to
demonstra...
A GIS to aid Sensemaking:
“Sensplace2”
Prototype (MacEachern et al. 2011)
visual display including the following
features:...
Sensplace2 continued
Also includes
a display in the form of a ‘tweet map,’
supporting simultaneously, a geographic overvie...
Conclusion
Natural language processing to filter
and aggregate and analyze social media
data streams, combined with geo-
v...
Conclusion continued
Pilot interviews with US government
emergency managers indicate great
enthusiasm for such systems, th...
Thank You
QUESTIONS?
15(C) 2013 Roxanne Hiltz
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Dealing with Information Overload When Using Social Media for Emergency Management: Emerging Solutions

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Presentation of Starr Roxanne Hiltz and Linda P. Plotnick on the topic "Dealing with Information Overload When Using Social Media for Emergency Management: Emerging Solutions" at ISCRAM2013

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Dealing with Information Overload When Using Social Media for Emergency Management: Emerging Solutions

  1. 1. Dealing with Information Overload When Using Social Media for Emergency Management: Emerging Solutions Starr Roxanne Hiltz (NJIT) Linda Plotnick (Jacksonville State, AL) ISCRAM 2013
  2. 2. Contents A focused literature review of issues related to information overload and potential solutions to the problem, in the realm of social media use in emergency management 2(C) 2013 Roxanne Hiltz
  3. 3. Information Overload in the Context of CMC the delivery of too many communications and to an increase in social density that gives individuals access to more communications than they can easily respond to information entropy, whereby incoming messages are not sufficiently organized by topic or content to be easily recognized as important (Hiltz & Turoff, 1985) 3(C) 2013 Roxanne Hiltz
  4. 4. A review that covers: Emergent social conventions use of “voluntweeters,” and technical features of social media , useful in organizing and filtering the information , to obtain situational awareness Natural language processing and visual map based (GISis) displays 4(C) 2013 Roxanne Hiltz
  5. 5. Social conventions in the Twitterverse Hashtags help with finding and filtering @ to indicate that tweet is about or for a specific person or organization Retweets and follow@ to recommend who/ what to follow Analyses build on/ document the usefulness: e.g., Starbird and Palen (2010) show: how these conventions were used during two disasters retweets authored by “local” users are more likely to be about the event. 5(C) 2013 Roxanne Hiltz
  6. 6. Human “Voluntweeters” Trained volunteers, off-site, who aid the onsite emergency managers by monitoring and filtering social media 6(C) 2013 Roxanne Hiltz
  7. 7. Examples of Human “Voluntweeters” Example 1: Haiti, “tweek the tweet” suggested hastag markups ( eg, “#need”); translated and passed on tweets to on the ground personnel (Starbird and Stamberger, 2010). Example 2: Use as liaisons between the overburdened personnel of official response organizations and the users of social media who want to receive information (St. Denis, Hughes, and Palen, 2012). Team of 8 filtered and passed on relevant SM info during a 3 week forest fire and also used the SM to pass on instructions for the public, from the manager. 7(C) 2013 Roxanne Hiltz
  8. 8. Machines- Natural Language processing,1 (Verma et al., 2011) describes a program developed to automatically identify messages communicated via Twitter that can contribute to situational awareness. 8(C) 2013 Roxanne Hiltz
  9. 9. Machines- Natural Language processing, 1 continued Tweets that contribute to situational awareness are likely to be written in a style that is: objective, impersonal and formal 9 Thus, the identification of subjectivity, personal style and formal register provide useful features for extracting tweets that contain tactical information. (achieved 80% accuracy). (C) 2013 Roxanne Hiltz
  10. 10. Natural Language Processing, 2 Cameron, Power, Robinson, and Yin (2012) developed a platform and client tools to demonstrate how relevant Twitter messages can be identified and utilized to inform the situation awareness of an emergency incident as it unfolds. Their system uses a clustering engine to gather and visually display clustering sets of tweets related to an incident. 10(C) 2013 Roxanne Hiltz
  11. 11. A GIS to aid Sensemaking: “Sensplace2” Prototype (MacEachern et al. 2011) visual display including the following features: a ‘tweet list’ that includes 500 identified/ selected most relevant tweets for any inquiry, with a color-coded strip to indicate the relevance ranking of each selected Tweet. 11(C) 2013 Roxanne Hiltz
  12. 12. Sensplace2 continued Also includes a display in the form of a ‘tweet map,’ supporting simultaneously, a geographic overview of the location of the selected tweets, and the ability to get more detail by selecting places or applying spatial filtering. A ‘heatmap’ is included in the overview tools that uses color to depict tweet frequency by concept (topic), time or place specifications 12(C) 2013 Roxanne Hiltz
  13. 13. Conclusion Natural language processing to filter and aggregate and analyze social media data streams, combined with geo- visual displays as part of the delivery of results, have a great deal of promise for overcoming the information overload problem faced by emergency managers. 13(C) 2013 Roxanne Hiltz
  14. 14. Conclusion continued Pilot interviews with US government emergency managers indicate great enthusiasm for such systems, that would filter and visually display “relevant” results, incorporated into their “regular” EMIS. 14(C) 2013 Roxanne Hiltz
  15. 15. Thank You QUESTIONS? 15(C) 2013 Roxanne Hiltz

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