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

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. 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. 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. 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. 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. Human “Voluntweeters”
Trained volunteers, off-site, who aid the
onsite emergency managers by
monitoring and filtering social media
6(C) 2013 Roxanne Hiltz

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. 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. 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).
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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. 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. 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. 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. 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. Thank You
QUESTIONS?
15(C) 2013 Roxanne Hiltz