# DisKnow: A Social-Driven Disaster Support Knowledge Extraction System
## 2 Related Work  
### Social Media for Disaster Management  
A disaster can be defined as a source of danger, and its consequences can adversely affect the world in different ways. Digital social networks have both emerged and evolved greatly in the 2000s, and the large amount of information shared can be of great help in disaster management and in other critical situations [24]. Major systematization work on Twitter messages was carried out by [PERSON] et al. in 2015 [7], where they highlight that 46% of papers were about event detection. Furthermore, 71% of the computer science reviewed studies have no specific application context, and are mainly focused on creating system architectures. This led the authors to the conclusion that disaster management is an opportunity to apply such knowledge to, and could significantly benefit from it, in order to strengthen situation awareness and improve emergency response. It shows that event detection on Twitter is a popular domain and plays an essential role in Twitter research. This is done based on a keyword search directly over Twitter's Application Programming Interface (API). It is usual to use control vocabularies to identify specific predefined events to identify what tweets will be captured and retrieved through the API. Examples of these approaches are the keyword-based filtering to identify predefined events like forest fires [25], earthquakes [26], and floods [27]. Although keyword-based data extraction might be useful, its standalone usage has several associated problems, like noise and bias. There is also a high rate of false results associated with these methods, such as detecting that there is going to be a fire in New York from the tweet \"Arcade fire are going to be playing in NYC\". An example of these problems is the work of [PERSON] et al. [28], where they identify that only 25% of the tweets generated by their initial keyword search were related to their topic. In 2013, through a more detailed analysis of the #TFF hashtag for the Tobacco Free Florida media campaign, the authors found that out of 3104 selected tweets, only 1% were actually related to it [29]. Therefore, a need for methods that can go beyond keyword filtering has been, and still is, one of the main focuses of Twitter research efforts to date.  
The research literature on social networking and social media applied to disasters and crisis situations, is still quite limited. Some researchers, however, have examined these approaches in order to establish pre and post-disaster response programs [21]. One example would be [PERSON] et al. [30] where Twitter was used to construct an earthquake reporting system in Japan, with an extremely positive result of 96% of said earthquakes over Richter' magnitude 3 being detected, directly through monitoring tweets. In another example use-case, demonstrating that collecting trustworthy information is crucial for disaster management, [PERSON] et al. [31] used Apollo [32] to collect and evaluate tens of thousands of tweets about New York City's gas availability during and shortly after 2012's HurricaneSandy to determine the accuracy of individual tweets, while considering unknown source reliability and uncertain provenance. Their results have shown that 90% of the tweets believed by Apollo were true, which is very relevant given that less than half of the tweets were actually true. CrisisTracker [33] has also proven to be a great example of using social media in disaster management, and probably one of the most successful ones. Although it has been used to monitor events like Fukushima's nuclear disaster, its largest field trial dealt with the 2012 civil war in Syria. During the 8-day study, CrisisTracker processed an average of 446,000 tweets a day and managed to successfully reduce the information to consumable stories, being successful in raising the situational awareness of such disaster areas. In practice, it took about 30 min after an isolated incident occurred before CrisisTracker could reduce the overload of social media information to a consumable story.