Optimizing humanitarian aids : formulating influencer advertisement in social networks

In order to solve problems encountered during natural disasters, in addition to NGOs and relief teams, various individuals intend to help the injured. Although the cooperation of people has remarkable advantages, the disparity between the needs of the injured and the people’s donations can cause problems such as trouble for relief teams and wasting the substantial resources. In generic, the influencer selection in the marketing endeavors is mainly aimed to maximize people’s awareness and attention, but this research proposes a method for influencer selection, using Social Network Analysis (SNA) and optimization techniques, by which it is possible to establish an adaptation between the public attention and the type of injured necessities. The proposed method is applied to a real sample network of Facebook friends, to evaluate the efficiency and validity of the formulated method

CrisMap: a Big Data Crisis Mapping System Based on Damage Detection and Geoparsing

Natural disasters, as well as human-made disasters, can have a deep impact on wide geographic areas, and emergency responders can benefit from the early estimation of emergency consequences. This work presents CrisMap, a Big Data crisis mapping system capable of quickly collecting and analyzing social media data. CrisMap extracts potential crisis- related actionable information from tweets by adopting a classification technique based on word embeddings and by exploiting a combination of readily-available semantic annotators to geoparse tweets. The enriched tweets are then visualized in customizable, Web-based dashboards, also leveraging ad-hoc quantitative visualizations like choropleth maps. The maps produced by our system help to estimate the impact of the emergency in its early phases, to identify areas that have been severely struck, and to acquire a greater situational awareness. We extensively benchmark the performance of our system on two Italian natural disasters by validating our maps against authoritative data. Finally, we perform a qualitative case-study on a recent devastating earthquake occurred in Central Italy