## 3 Literature Review
### Twitter Data Analytics in Healthcare  
The data generated by social media, such as Twitter, provides unexpected opportunities to enrich the health care sector (see, e.g., [62]). Fields of applications in health care that have benefitted from social media data include, for example, building surveillance tools to track a certain disease, studying side effects of medications, and exploring healthcare-related habits. The most common methodologies found in the literature are statistical analysis and text mining (including sentiment analysis) of Twitter data using machine learning algorithms. The notable works are reviewed below. [PERSON] et al. [63] analyzed tweets without aiming to detect a particular illness. The target was to produce \"interest curves\" that document the generation of hypotheses regarding which health-related conditions/topics have occurred frequently. Unlike other studies, this approach is not dedicated to discovering one certain disease. The main contribution was to convert the stream of tweets to a list of health-related topics. [PERSON] and [PERSON] developed a model, called the Ailment Topic Aspect Model (ATAM), to link each disease with its possible symptoms, medications, and related words [64]. The ATAM model has been constructed using Twitter data and trained by a support vector machine algorithm (SVM). The experiments demonstrated the efficiency of ATAM in aligning different groups of diseases with their symptoms, medications, and related words. The authors mentioned that ATAM could be used as a surveillance tool for general health topics. The authors extended in [65] the original ATAM to discover mentions of additional illnesses, including allergies, obesity, and insomnia. The idea of detecting influenza cases from Twitter data was explored by [PERSON] et al. [66]. Using a support vector machine (SVM)-based classifier, they detected the influenza patients. The experiments results demonstrated the practicability of the proposed approach, which showed acceptable correlation comparing with medical reports statistics, especially at the outbreak and early spread (early epidemic) stage. The authors extended their work in [67] and implemented a robust influenza prediction model that enabled the use of direct and indirect information using tweets from urban and rural areas in Japan. This work was further extended in [68]. The authors constructed a more generalized diseases surveillance tool that performs multi-label and cross-language tasks. The multi-label approach wasused to classify tweets into eight different diseases symptoms and label them appropriately with patients' symptoms. The tool is cross-language and works on three different languages: English, Japanese, and Chinese. [PERSON] et al. [69] found that deeper content analysis of tweets leads to a remarkable improvement in influenza's surveillance performance. More precisely, flu-related tweets do not reflect infections alone; rather the tweets might be related to suspicions related to flu infections, worries about the infection, or discussions about the disease itself. Hence, they relied on trained classifiers that can distinguish between the awareness and infection tweets using a pre-annotated set of trained data. Their results demonstrated that by distinguishing between types of flu tweets to identify reports of infection, reasonable surveillance could be recovered. The obtained results brought to the attention of the NLP community that deeper content analysis of tweets is worth investigation. [PERSON] et al. [70] developed a real-time surveillance tool for disease awareness rather than monitoring infection cases. This system offered an opportunity not only for public health officials to identify awareness trends, for which they often have no other data sources, but also to study what drives awareness of influenza in a population. The studies are not limited to tracking the spread of a specific epidemic; they also examined following the side effects of certain medications. For example, [PERSON] et al.