Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Many novel applications have been built based on analyzing tets about specific topics. While these applications provide different kinds of analysis, they share a common task of monitoring "target" tets from the Twitter stream for a topic. The current solution for this task tracks a set of manually selected keywords with Twitter APIs. Obviously, this manual approach has many limitations. ,  propose a data platform to automatically monitor target tets from the Twitter stream for any given topic. To monitor target tets in an optimal and continuous way,  design Automatic Topic-focused Monitor (ATM), which iteratively 1) samples tets from the stream and 2) selects keywords to track based on the samples. To realize ATM,  develop a tet sampling algorithm to sample sufficient unbiased tets with available Twitter APIs, and a keyword selection algorithm to efficiently select keywords that have a near-optimal coverage of target tets under cost constraints.  conduct extensive experiments to show the effectiveness of ATM. E.g., ATM covers 90% of target tets for a topic and improves the manual approach by 49%.
['Rui Li', 'Shengjie Wang', 'Kevin Chen-Chuan Chang']
I am writing a research paper on a idea Towards social data platform: automatic topic-focused monitor for twitter stream
[{"from": "human", "value": "I am writing a research paper on a idea Towards social data platform: automatic topic-focused monitor for twitter stream"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Many novel applications have been built based on analyzing tets about specific topics. While these applications provide different kinds of analysis, they share a common task of monitoring \"target\" tets from the Twitter stream for a topic. The current solution for this task tracks a set of manually selected keywords with Twitter APIs. Obviously, this manual approach has many limitations. ,  propose a data platform to automatically monitor target tets from the Twitter stream for any given topic. To monitor target tets in an optimal and continuous way,  design Automatic Topic-focused Monitor (ATM), which iteratively 1) samples tets from the stream and 2) selects keywords to track based on the samples. To realize ATM,  develop a tet sampling algorithm to sample sufficient unbiased tets with available Twitter APIs, and a keyword selection algorithm to efficiently select keywords that have a near-optimal coverage of target tets under cost constraints.  conduct extensive experiments to show the effectiveness of ATM. E.g., ATM covers 90% of target tets for a topic and improves the manual approach by 49%."}]