## 5 Results
### Event Detection and Classification  
Initially, it was desired to attest the effectiveness of TwitterSensing concerning detection and classification of events of interest. For that, a considerable large tweets database was used as an input for a series of tests. After that, some reference optimizations were defined, demonstrating the practical usage of sensing prioritization. The experiments were performed over one dataset obtained using the _Twitter Realtime Filter_[75]. The obtained dataset (\(W_{NY}\)) consists of 1.74 million geo-tagged tweets sent from New York, one of the most active cities on _Twitter_, from 7 February 2017 to 19 May 2017. Each tweet contains the textual information (message), location (latitude, longitude) from which the message was sent, the time stamp when the message was sent, and identifiers of the author of the tweet. Besides the spatial window, no other constraint was applied when collecting the data. The Event Detector was executed over the entire \(W_{NY}\) considering \(\Delta_{t}=2\) h, obtaining \(|E|=20\),046 events distributed across the city of New York. To perform the event severity classification, we built two disjoint sets \(E_{p}\) and \(E_{t}\): first, we extracted the first four weeks of \(E\) to build the training partition \(E_{t}\), resulting in \(|E_{t}|=8211\); the remaining set \(E_{p}\) comprises 10 weeks, from 0 to 9 and starting on 6 March 2017, and composes the evaluation partition which will be considered on the studies of this section. While building our model, we first considered a baseline Multinomial Naive Bayes (NB) classifier with no optimizations nor preprocessing steps over the input data and, gradually, we expanded it by including preprocessing tasks to increase the model hit rate. By the end of this process, after including all previously mentioned NLP techniques, we achieved an average score of 71.02% correctly classified events over 64.69% of the raw NB performance. When compared to other baseline algorithm using Support Vector Machines, another common approach for text classification, the average baseline rate drops to 42.43%. In all mentioned steps, we employed an 10-fold cross validation procedure. After including our best classifier on Algorithm 1, it was calculated the priority of each \(e\in E_{t}\). The output of this process is discussed in this subsection. Figure 4 shows the number of detected events per week in a logarithm scale. Ranging from 4076 detected events on Week 0 to 436 on Week 9, this number is highly correlated with the amount  
and quality of the input data. Since the detection process is composed by a geographical and semantical analysis, a lower number of detected events is related to the lack of events itself, to a lack of spontaneous report from the users regarding some currently happening events or to highly odd messages about the same event jeopardizing the semantical analysis. In our dataset, we have a notable drop in the rate of detected events starting in Week 5 and stabilizing on the next weeks. The inflexion point, Week 5, correspond to the last week of April where we may have an wide combination of features (e.g., ending of vacations). Besides the number of detected events, it was also assessed the percentage of tweets according to their classification. Figure 5 shows the percentage, in logarithm scale, of events by week grouped by their severity. Mobility-related events are dominant over all weeks, ranging from 83.63% on Week 0 from 90.44% on Week 8. On the other hand, unbounded events are the least frequent events, having weeks with no occurrences of them (Week 9). Despite some similarities, each week has a distinct event signature characterized by notably small changes in the event severity distribution, which directly reflects the city itinerary during the respective period and is essential for performing WSN optimizations. For example, events such as \"shows\" and \"protests\" of Women's History Month [76] triggered the high level of unbounded events on Week 0.