1. Kenneth A. Lachlan University of Connecticut
Twitter use by affected publics during crises and disasters: What we know from recent research and considerations for emergency management
Kenneth A. Lachlan
University of Connecticut

2. Data collection First set: Hurricane Sandy
Level 1: 27,259 Tweets (machine coded)
Level 2: 1785 Tweets (human coder)
Collected between 10/27 and 10/
Second set: Winter Storm Nemo
Level 1: 15,120 Tweets (machine coded)
Level 2: 799 Tweets (human coder)
Collected between noon and midnight, 2/8 2013

3. Thinking about this in CERC terms….
Pre crisis
Initial event
Maintenance
Resolution
Evaluation
Affective concerns may be especially salient during the pre crisis
and initial stages

4. First data collection:
Findings
First data collection:
Day 1: info = 41%, affect = 35%
Day 4: info = 32.5%, affect = 47%
Unspecified fear, magnitude
Second data collection:
Localized: 10% affect at noon, 59% by midnight (#bosnow)
Non-localized: 37.5% at noon, half by midnight (#nemo)

5. Variable Crises Mazer, et al. (2015)
Tweets following two school shootings
Volume peaked within an hour of either event
Misinformation and rumor
82% info for one shooting, 78% for the other
Why?
-high involvement
-central location
-reaction only/no warning period

6. Serial Transmission Sutton, et al. (2014)
Looked at accounts of officials during wildfire
Imperative sentences more likely to get retweeted
- direct instructions and behavioral recommendations
Inclusion of URL has no effect
Follower count predictive of serial transmission

7. Volume must be dealt with
Takeaways
Volume must be dealt with
Regardless of the other considerations, you have a lot of content to cut through to get your message out
Countering with a “tweet storm” is one approach
Serial transmission is another
There may be specific message and targeting strategies that can be used to generate serial transmission

8. 2) Different crises beget different approaches
Takeaways
2) Different crises beget different approaches
Uncertainty reduction may be paramount with long precrisis
Unforeseen events may play out differently
Level of involvement/risk
Local versus national impact – who is interested?

9. 3) Misinformation must be countered
Takeaways
3) Misinformation must be countered
May actually be of greater concern in localized crises that are not over-tweeted
Constant monitoring of relevant hashtags as they emerge
Use source credibility and serial transmission to neutralize

10. 4) Dialogue with affected individuals
Takeaways
4) Dialogue with affected individuals
Engage in “masspersonal” interactions
Dialogue with single user gets broadcast to entire follower list
Engender trust and sense of goodwill

11. References
Lachlan, K.A., Spence, P.R., & Lin, X. (2014). Expressions of Risk Awareness and Concern through Twitter: On the Utility of Using the Medium as an Indication of Audience Needs. Computers in Human Behavior, 35 (2),
Lachlan, K.A., Spence, P.R., Lin, X., & Del Greco, M. (2014). Screaming into the wind: Twitter use during Hurricane Sandy. Communication Studies, 65 (5),
Lachlan, K.A., Spence, P.R., Lin, X., Najarian, K., & Del Greco, M. (2014). Twitter use during a weather event: comparing content associated with localized and nonlocalized hashtags. Communication Studies, 65 (5),
Lachlan, K.A., Spence, P.R., Lin, X., Najarian, K., & Del Greco, M. (2015). Social media and crisis management: CERC, search strategies, and Twitter content. Computers in Human Behavior, 54 (1),
Mazer, J.P, Thompson, B., Cherry, J., Russell, M., Payne, H.J., Kirby, E.G., & Pfohl (2015). Communication in the face of a school crisis: Examining the volume and content of social media mentions during active shooter incidents. Computers in Human Behavior, 53 (3),
Spence, P.R., Lachlan, K.A., Lin, X., & Del Greco, M. (2014). Variability in Twitter content across the stages of a natural disaster: Implications for crisis communication. Communication Quarterly, 63 (2),
Sutton, J., Spiro, E.S., Johnson, B., Fitzhugh, S., Gibson, B., & Butts, C.T. (2014). Warning tweets: Serial transmission of messages during the warning phase of a disaster event. Information, Communication, & Society, 17 (6),