## 5 Sehaa: Results and Discussions
### Post-Classification Results (First-Level)  
This section presents and analyzes the results obtained from the first-level classifier, i.e., the classifier that removed unrelated (or irrelevant) tweets (for classifier details, see Section 4.4). The difference between these results compared to the ones presented in the previous section is as follows. Firstly, these results do not include irrelevant tweets as explained in the previous section. Secondly, the results are aggregated based on the common terminologies provided in Table 1. Thirdly, we provide results for major cities in addition to the whole of Saudi Arabia and also provide statistics based on the data, which were normalized with the population sizes of the examined cities. Figure 7 illustrates the post-classification (first-level) distribution of the diseases detected by the Sehaa system. Each set of bars in the figure represents a unique disease and the detected numbers of occurrences are plotted using the log-10 scale on the y-axis. For detailed analysis, we have extracted the tweets for the five major cities in Saudi Arabia and plotted them for each unique disease. There are a total of 14 diseases. The cities are Riyadh, Jeddah, Dammam, Makkah, and Taif. After the classification stages, we filtered the tweets according to their locations by retrieving the locations of the users. The location of a tweet is available in the location attribute of the user object, which is part of the tweet objects. Note that we were only able to find locations of tweets where these were enabled by the user. The sixth bar in the figure for each disease is the total number of occurrences in the whole of Saudi Arabia. The absence of bars implies no cases detected for a disease in a city. The three-lettered abbreviations of the diseases are provided in Table 1.  
Figure 6: Sehaa: symptoms, medications, and diseases (Saudi Arabia) (pre-classification) (pie chart). For further exploration, and to take into consideration the fact that bigger cities, such as Riyadh, have a higher population and hence potentially a higher number of tweeters, the frequency of each disease was normalized against the relevant population. That is, the frequency of a disease for a city was divided by the population of the city, and the frequency of a disease for the whole of Saudi Arabia was divided by the population of the country. These normalized values for the 14 diseases, five cities, and the whole of Saudi Arabia are plotted in Figure 8.  
Figures 7 and 8 show that the top five prevalent diseases in Saudi Arabia are hypertension (HYT), dermal diseases (DRD), heart diseases (HRD), diabetes (DBT), and cancer (CNR). This pattern is somehow different from the pattern of all five major cities (Riyadh, Jeddah, Dammam, Makkah, and Taif). The top five prevalent diseases for the five cities are heart diseases, hypertension, dermal diseases, cancer, and diabetes. However, there are clear differences in some of these cases in terms of the precise frequencies of the top five diseases across the five cities. Note in the normalized values (Figure 8) that Riyadh and Jeddah have the highest frequency among the five cities for most of the diseases. One exception is Makkah, which is the only city where influenza has been detected, and this could be due to a large number of people visiting from all over the world coming in close proximity, which could spread influenza in Makkah more than in other cities. This is generally known in Saudi Arabia and therefore the Sehaa findings have verified the common knowledge in Saudi Arabia. We would like to note here that we are not suggesting that influenza does not exist in other cities, but that perhaps people do not talk about it as much as they do in Makkah. Note also in Figure 8 that the overall national normalized number of the occurrence of most diseases is lower than the frequencies for one or more of the five cities except asthma (AST), hypertension (HYT), dermal diseases (DRD), diabetes (DBT), and thyroid diseases (THD).