<p>In April and May 2015 Nepal suffered two massive earthquakes of 7.5 and 6 5 magnitudes in the Richter scale, killing 8856 and injuring 22309. Two years later in September 2017, Puerto Rico underwent the Category 5 Hurricane Maria, killing an estimate of 800 to 8000 people and displacing hundreds of thousands of Puerto Ricans (Kishore et al., 2018). This dissertation project is the comparative study of Nepal’s and Puerto Rico’s networked communities, their actors, participants (Potts, 2014), and the users (Ingraham, 2015; Johnson, 1998) who used crisis communication practices to address the havoc created by the disaster. Using a mixed-methods research approach and with framework created with the Assemblage Theory (DeLanda, 2016), I argue that disasters create situations in which various networked communities are formed into transnational assemblages along with an emergence of innovative digital technical and professional communication practices.</p>

In recent years, social media platforms such as Twitter and Facebook have emerged as effective tools for broadcasting messages worldwide during disaster events. With millions of messages posted through these services during such events, it has become imperative to identify valuable information that can help the emergency responders to develop effective relief efforts and aid victims. Many studies implied that the role of social media during disasters is invaluable and can be incorporated into emergency decision-making process. However, due to the "big data" nature of social media, it is very labor-intensive to employ human resources to sift through social media posts and categorize/classify them as useful information. Hence, there is a growing need for machine intelligence to automate the process of extracting useful information from the social media data during disaster events. This dissertation addresses the following questions: In a social media stream of messages, what is the useful information to be extracted that can help emergency response organizations to become more situationally aware during and following a disaster? What are the features (or patterns) that can contribute to automatically identifying messages that are useful during disasters? We explored a wide variety of features in conjunction with supervised learning algorithms to automatically identify messages that are useful during disaster events. The feature design includes sentiment features to extract the geo-mapped sentiment expressed in tweets, as well as tweet-content and user detail features to predict the likelihood of the information contained in a tweet to be quickly spread in the network. Further experimentation is carried out to see how these features help in identifying the informative tweets and filter out those tweets that are conversational in nature.