Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Allergies are one of the most common chronic diseases worldwide. One in five Americans suffer from either allergy or asthma symptoms. With the prevalence of social media, people sharing experiences and opinions on personal health symptoms and concerns on social media are increasing. Mining those publicly available health related data potentially provides valuable healthcare insights. ,  propose a real-time allergy surveillance system that first classifies tets to identify those that mention actual allergy incidents using bag-of-words model and NaiveBayesMultinomial classifier and applies in-depth text and spatiotemporal analysis. Our experimental results show that the proposed system can detect predominant allergy types with high precision and that allergy-related tet volume is highly correlated to the ather data (daily maximum temperature).  believe that this is the first study that examines a large-scale social media stream for in-depth analysis of allergy activities.
['Kathy Lee', 'Ankit Agrawal', 'Alok N. Choudhary']
I am writing a research paper on a idea Mining Social Media Streams to Improve Public Health Allergy Surveillance
[{"from": "human", "value": "I am writing a research paper on a idea Mining Social Media Streams to Improve Public Health Allergy Surveillance"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Allergies are one of the most common chronic diseases worldwide. One in five Americans suffer from either allergy or asthma symptoms. With the prevalence of social media, people sharing experiences and opinions on personal health symptoms and concerns on social media are increasing. Mining those publicly available health related data potentially provides valuable healthcare insights. ,  propose a real-time allergy surveillance system that first classifies tets to identify those that mention actual allergy incidents using bag-of-words model and NaiveBayesMultinomial classifier and applies in-depth text and spatiotemporal analysis. Our experimental results show that the proposed system can detect predominant allergy types with high precision and that allergy-related tet volume is highly correlated to the ather data (daily maximum temperature).  believe that this is the first study that examines a large-scale social media stream for in-depth analysis of allergy activities."}]