## 5 Sehaa: Results and Discussions
### Awareness vs. Afflicted Tweets  
The tweets detected by the first-level classifiers comprise both the \"afflicted by\" and \"awareness\" tweets (see Section 4.3). The \"afflicted by\" tweets include cases of sickness, suffering, and medication. The awareness tweets communicate to create awareness about diseases. Usually, the awareness tweets are posted by medical practitioners who are tweeting for raising public awareness purposes. The purpose of the second-level classifier is to separate the actual cases from the awareness tweets in order to compute the actual number of diseases cases. Moreover, this classification also helps with finding the level of awareness-related activities for particular diseases across various cities in the Kingdom of Saudi Arabia (KSA). Figure 9 depicts the total number of awareness and afflicted cases for the set of fourteen diseases in the KSA. The top five diseases in terms of awareness tweets were hypertension (HYT), dermal diseases (DRD), heart diseases (HRD), diabetes (DBT), and cancer (CNR). The top five diseases in terms of the actual \"afflicted by\" cases were dermal diseases (DRD), heart diseases (HRD), hypertension (HYT), cancer (CNR), and diabetes (DBT). The rankings of the diseases using the awareness tweets are the same as for the first-level classification. However, the ranking of the occurrence of the diseases  
Figure 8: Sehaa: distribution of diseases across major cities (_Normalized_) (post-classification) (first-level). using the \"afflicted by\" cases is different from the first-level classification (see Figure 9). Although this appears to be a surprise, it is expected because the number of the awareness tweets is much higher than the number of the tweets reporting the actual cases, and these bigger numbers dominated the first-level classification trends in Figure 7. Compare these results with the pre-classification results in Figure 6 and note that the trend for the diseases is also different from the second-level classification results in Figure 9. We conclude that the most accurate top five diseases detected by the Sehaa system are the ones depicted in Figure 9 for \"afflicted by\" cases, that is, dermal diseases (DRD), heart diseases (HRD), hypertension (HYT), cancer (CNR), and diabetes (DBT). Figure 10 depicts the ratio of awareness and afflicted cases for Saudi Arabia and its five major cities. We infer from these values the level of awareness activities being carried out by various cities in comparison to the occurrence of each of the fourteen diseases. We expect bigger cities to carry out a higher number of awareness activities, as is usually the case due to the larger population, higher education levels, and use of technologies (Twitter). However, we reiterate that Figure 10 depicts the ratio and not the number of awareness activities. Note in Figure 10 that Riyadh has the highest ratio of awareness to afflicted cases for six of the fourteen diseases: AST, HYT, CNR, CGH, DBT, and THD. Interestingly, the top two diseases--dermal diseases (DRD) and heart diseases (HRD)--are not included in these six diseases, implying that Riyadh should do more in creating awareness for these diseases. Jeddah is considered the second major city in Saudi Arabia. It has the highest ratio of awareness to afflicted cases for three of the fourteen diseases: CHT (cholesterol), CLN (colon), and TBC (tuberculosis). None of these are among the top five diseases in Saudi Arabia. Jeddah needs to do more in creating awareness for the top five national diseases. Taif is the fifth major city with a population that is one-eighth of that of the largest city Riyadh. However, it is commendable it has a comparable ratio of awareness to afflicted cases for several diseases. Note in Figure 7 that seven out of 14 diseases were not detected in Taif city and hence these diseases have zero values in Figure 10. Considering the two facts together, Taif has the lowest number of disease cases in Saudi Arabia while maintaining a high number of awareness activities.