Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Online social media microblogs may be a valuable resource for timely identification of critical ad hoc health-related incidents or serious epidemic outbreaks. ,  explore emotion classification of Twitter microblogs related to localized public health threats, and study whether the public mood can be effectively utilized in early discovery or alarming of such events.  analyse user tets around recent incidents of Ebola, finding differences in the expression of emotions in tets posted prior to and after the incidents have emerged.  also analyse differences in the nature of the tets in the immediately affected area as compared to areas remote to the events. The results of this analysis suggest that emotions in social media microblogging data (from Twitter in particular) may be utilized effectively as a source of evidence for disease outbreak detection and monitoring.
['Bahadorreza Ofoghi', 'Meghan Mann', 'Karin Verspoor']
I am writing a research paper on a idea TOWARDS EARLY DISCOVERY OF SALIENT HEALTH THREATS: A SOCIAL MEDIA EMOTION CLASSIFICATION TECHNIQUE
[{"from": "human", "value": "I am writing a research paper on a idea TOWARDS EARLY DISCOVERY OF SALIENT HEALTH THREATS: A SOCIAL MEDIA EMOTION CLASSIFICATION TECHNIQUE"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Online social media microblogs may be a valuable resource for timely identification of critical ad hoc health-related incidents or serious epidemic outbreaks. ,  explore emotion classification of Twitter microblogs related to localized public health threats, and study whether the public mood can be effectively utilized in early discovery or alarming of such events.  analyse user tets around recent incidents of Ebola, finding differences in the expression of emotions in tets posted prior to and after the incidents have emerged.  also analyse differences in the nature of the tets in the immediately affected area as compared to areas remote to the events. The results of this analysis suggest that emotions in social media microblogging data (from Twitter in particular) may be utilized effectively as a source of evidence for disease outbreak detection and monitoring."}]