Understanding the efficiency of social media based crisis communication during hurricane Sandy Abstract   Rapid communication during extreme events is one of the critical aspects of successful disaster management strategies. Due to their ubiquitous nature, social media platforms are expected to offer a unique opportunity for crisis communication. In this study, about 52.5 million tweets related to hurricane Sandy posted by 13.75 million users are analyzed to assess the effectiveness of social media communication during disasters and identify the contributing factors leading to effective crisis communication strategies. Efficiency of a social media user is defined as the ratio of attention gained over the number of tweets posted. A model is developed to identify more efficient users based on several relevant features. Results indicate that during a disaster event, only few social media users become highly efficient in gaining attention. In addition, efficiency does not depend on the frequency of tweeting activity only; instead it depends on the number of followers and friends, user category, bot score (controlled by a human or a machine), and activity patterns (predictability of activity frequency). Since the proposed efficiency metric is easy to evaluate, it can potentially detect effective social media users in real time to communicate information and awareness to vulnerable communities during a disaster.

Tweeting the United Nations Climate Change Conference in Paris (COP21): An analysis of a social network and factors determining the network influence Abstract   To understand the Twitter network of an environmental and political event and to extend the network theory of social capital, we first performed a network analysis of the English tweets during the first 10 days of the United Nationsxe2x80x99 Conference of the Parties in Paris in 2015. Accounts for nonprofit and government agencies were more likely to be influential in the Twitter network and be retweeted, whereas individual accounts were more likely to retweet others. Based on a quota sample of 133 Twitter accounts and using both manual and machine coding, we further found that the number of followers (but not the size of following) and the commongoal frame (i.e., mitigationadaptation) positively predicted an accountu0027s influence in the Twitter network, whereas the conflict frame negatively predicted an accountu0027s network influence.