## 4 Proposed Approach
### Priority Computing Unit
#### 4.2.2 Event Grader Module  
A city scenario is susceptible to a wide range of events (e.g., crimes, floods, traffic jams, protests, accidents, and explosions) and each one has a different nature and impacts the citizens routine in different ways. People posting direct or indirect information about such events on _Twitter_ may contribute to optimizations on any sensor-based monitoring systems, once events are properly detected and classified. Actually, each event that may happen has a risk (or priority) and this risk level is defined by two factors: the event scope and the event severity [69]. Based on [PERSON]'s scope/severity grid, five contextual severity levels are established and a method to calculate the scope of events was created, as presented in Table 2. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Contextual Severity (\(\mathit{sev}(\mathit{e})\))** & **Event Type** & **Example** \\ \hline
100 & Unbounded & City Christmas party, big show \\
75 & Social & Movie premiere, company party \\
50 & Weather & Rainfall, snow \\
25 & Mobility & Visit to Central Park \\
0 & Imperceptible & Global events \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Contextual severity levels for prioritization of events. The \"Contextual severity\" directly refers to the event nature and is obtained by analyzing the event keywords, while the scope refers to the sensitivity of a spatial region and is obtained by analyzing the average population (number of _Twitter_ users) in the area reached by the event. Analyzing Table 2, one can see that \"Unbounded\" is the event type with the highest severity level. Actually, city public celebrations and big private events (e.g., New Year at Times Square and [PERSON] show at Madison Square Garden) impact many people (e.g., fans, merchants, neighbors, and police) and causes the reconfiguration of its neighborhood for a certain period of time (e.g., traffic redirection). This event type must be carefully watched, because floods, fires, explosions, car crashes, among other disaster-related events can quickly impact many people in this region, requiring immediate actions to relieve the impact and prevent consequences, for example providing health care, redirecting the traffic and call the fire department in the case of a fire. For the second group, \"Social\" related events refer to small and non-periodic events such as company parties and ceremonies. This events are restricted to a small region, but it may indicates a forthcoming unbounded event (e.g., _event described by a message saying \"Pre-concert dinner and drinks with my friend!\"_). \"Weather\" events usually involve large regions but they consequences are less harmful than previous events due to the lower density of people in the location. \"Mobility\" events are characterized by movement of considerable groups of people, which describes the city routines and average behavior (e.g., series of check-ins on Central Park on a Saturday). Such events are mostly harmless, but they require attention due their capability to describe the usual flow of people across the city. Finally, \"Imperceptible\" events cause no damage nor changes in the city routine (e.g., a global event missed by the event detector) and are discarded in the classification process. To perform the extraction of the contextual severity (\(sev(e)\)) from a event \(e\), we ran the event detector over a section \(W_{t}\) of our complete dataset \(W\), obtaining \(E_{t}\) events. Each \(e\in E_{t}\) were manually tagged by observing the text of its corresponding pivot tweet \(w_{p}\). The manual classification process was performed three times, in different days, and the final tag of each event was defined as the most common classification for that event and, in case of an inconsistency on the tags, a definitive classification is performed.