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

Data collection First set: Hurricane Sandy Level 1: 27,259 Tweets (machine coded) Level 2: 1785 Tweets (human coder) Collected between 10/27 and 10/30 2012 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

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

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)

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

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

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

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?

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

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

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), 554-559. 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), 500-518. 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), 519-534. 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), 647-653. 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), 238-248. 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), 171-186 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), 765-787.