Thematically Analysing Social Network Content During Disasters Through the Lens of the Disaster Management Lifecycle

Social Networks such as Twitter are often used for disseminating and collecting information during natural disasters. The potential for its use in Disaster Management has been acknowledged. However, more nuanced understanding of the communications that take place on social networks are required to more effectively integrate this information into the processes within disaster management. The type and value of information shared should be assessed, determining the benefits and issues, with credibility and reliability as known concerns. Mapping the tweets in relation to the modelled stages of a disaster can be a useful evaluation for determining the benefits/drawbacks of using data from social networks, such as Twitter, in disaster management. A thematic analysis of tweets' content, language and tone during the UK Storms and Floods 2013/14 was conducted. Manual scripting was used to determine the official sequence of events, and classify the stages of the disaster into the phases of the Disaster Management Lifecycle, to produce a timeline. Twenty-five topics discussed on Twitter emerged, and three key types of tweets, based on the language and tone, were identified. The timeline represents the events of the disaster, according to the Met Office reports, classed into B. Faulkner's Disaster Management Lifecycle framework. Context is provided when observing the analysed tweets against the timeline. This illustrates a potential basis and benefit for mapping tweets into the Disaster Management Lifecycle phases. Comparing the number of tweets submitted in each month with the timeline, suggests users tweet more as an event heightens and persists. Furthermore, users generally express greater emotion and urgency in their tweets. This paper concludes that the thematic analysis of content on social networks, such as Twitter, can be useful in gaining additional perspectives for disaster management. It demonstrates that mapping tweets into the phases of a Disaster Management Lifecycle model can have benefits in the recovery phase, not just in the response phase, to potentially improve future policies and activities.

[1]  H. Raghav Rao,et al.  Retweeting the Fukushima nuclear radiation disaster , 2014, CACM.

[2]  Stefan Stieglitz,et al.  Quantitative Approaches to Comparing Communication Patterns on Twitter , 2012 .

[3]  B. Faulkner Towards a framework for tourism disaster management , 2001 .

[4]  C. Haruechaiyasak,et al.  The role of Twitter during a natural disaster: Case study of 2011 Thai Flood , 2012, 2012 Proceedings of PICMET '12: Technology Management for Emerging Technologies.

[5]  R.J.P. Stronkman,et al.  Towards a realtime Twitter analysis during crises for operational crisis management , 2012, ISCRAM.

[6]  Donald Alan Thomas A general inductive approach for qualitative data analysis , 2003 .

[7]  Leysia Palen,et al.  Supporting “Everyday Analysts” in Safety- and Time-Critical Situations , 2011, Inf. Soc..

[8]  David R. Thomas,et al.  A General Inductive Approach for Analyzing Qualitative Evaluation Data , 2006 .

[9]  Amanda Lee Hughes,et al.  Crisis Informatics: Studying Crisis in a Networked World , 2007 .

[10]  Fernando Diaz,et al.  Extracting information nuggets from disaster- Related messages in social media , 2013, ISCRAM.

[11]  Leysia Palen,et al.  Microblogging during two natural hazards events: what twitter may contribute to situational awareness , 2010, CHI.

[12]  Amanda Lee Hughes,et al.  Crisis in a Networked World , 2009 .

[13]  David Alexander,et al.  Towards the development of a standard for emergency planning. , 2005 .

[14]  Laura Giurca Vasilescu,et al.  Disaster Management CYCLE – a theoretical approach , 2008 .

[15]  Leysia Palen,et al.  Natural Language Processing to the Rescue? Extracting "Situational Awareness" Tweets During Mass Emergency , 2011, ICWSM.

[16]  Leysia Palen,et al.  Pass it on?: Retweeting in mass emergency , 2010, ISCRAM.