1. An Analysis of Twitter Usage at the
National Weather Service in
Louisville, KY
Robert Prestley
The Pennsylvania State University
Ron Steve and Kevin Deitsch
National Weather Service Louisville
November 17, 2016
3. 3
The National Weather Service increasingly relies
on social media in operations.
2012
9 accounts
4. 4
The National Weather Service increasingly relies
on social media in operations.
2012
9 accounts
2014
130 accounts
And over a million followers!
5. 5
The National Weather Service increasingly relies
on social media in operations.
2012
9 accounts
2014
130 accounts
Dedicated Decision Support Services
(DSS) desk
2015
6. 6
Our research focused on three main questions:
1. Which types of posts reach and are engaged
by users?
2. How did switch to DSS desk impact posting?
3. How does post frequency and average
reach vary in the lead-up and aftermath of a
high-impact event?
7. 7
Methodology
• Pulled analytic data from
Twitter posts from Aug
2014 – May 2016
• Classified tweets by
category and type
• Year-on-year comparison to
assess DSS desk impact
• Investigated 10 high-impact
events (4 short-fuse, 6
long-fuse)
Above: February 16, 2015 snow – a long fuse event
Below: Kentucky Derby Day storms, May 7, 2016 – a
short fuse event
14. Forecast posts are only part of a complete social
media strategy for NWS offices
14
0 1000 2000 3000 4000
Observed Weather
Technical Issues
Forecast
Twitter Interaction
Interesting
Nowcast
Educational/Outreach
Storm Report
Average Reach
0% 1% 2% 3% 4% 5%
Average Engagement Rate
15. Photos are the best medium for posting
information.
15
0.0%
0.5%
1.0%
1.5%
2.0%
2.5%
3.0%
3.5%
4.0%
Photo Link Text
AverageEngagementRate
16. Posting frequency and average reach have
increased since implementation of the DSS desk
16
0
5
10
15
20
25
30
35
40
45
2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25
NumberofDays
Number of Tweets Per Day
Pre-DSS
Post-DSS
0
10
20
30
40
50
60
70
80
0.5-1 1-1.5 1.5-2 2-2.5 2.5-3 3-3.5 3.5-4 4-4.5 4.5-5
NumberofDays
Average Daily Reach, in Thousands
Pre-DSS Mean: 7.5
Pre-DSS Mean: 2013.6
Post-DSS Mean: 9.8
Percent Change: 30.7%
Post-DSS Mean: 2540.0
Percent Change: 26.1%
17. Engagement in long-fuse events is spread begins
sooner and ends later than for short-fuse events.
17
0
1000
2000
3000
4000
5000
0.00
0.40
0.80
1.20
1.60
AverageImpressions
TweetsperHour
Tweet Frequency
Average Impressions
0
1000
2000
3000
4000
5000
0.00
0.40
0.80
1.20
1.60
120+ 96-120 72-96 48-72 24-48 24-Begin During End-24 24-48 48-72 72-96 96-120 120+
AverageImpressions
TweetsperHour
Time, Relative to Event Onset
Pre-Storm Post-Storm
Short-fuse
Long-fuse
18. Summary
• DSS desk has likely
had impact
• Photos are the best
medium
• More posts before
long-fuse events
18
19. Anecdotal Results
• Interaction with other offices, federal agencies,
EM, news media
• Facebook vs Twitter
• Humanize communications
19
20. 20
Next Steps
• Greater focus on Facebook
• Sort by weather type (e.g. storm, snow, heat)
• Look at other statistics – likes, comments,
shares, retweets
• Look at how social media is used by other
NWS pages, note any differences
• Surveys on social media use for weather
information
21. 21
Acknowledgements
• Huge thanks to my mentors: Ron Steve, Ted
Funk, and Kevin Deitsch
• Thanks also to my roommate and fellow
Hollings student Michael Dunn for driving me
around all summer!
Photo Sources: NWS Louisville, The Courier-Journal, AP Photo/Darron Cummings
