Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Information filtering has been a major task of study in the field of information retrieval (IR) for a long time, focusing on filtering ll-formed documents such as news articles. Recently, more interest was directed towards applying filtering tasks to user-generated content such as microblogs. Several earlier studies investigated microblog filtering for focused topics. Another vital filtering scenario in microblogs targets the detection of posts that are relevant to long-standing broad and dynamic topics, i.e., topics spanning several subtopics that change over time. This type of filtering in microblogs is essential for many applications such as social studies on large events and news tracking of temporal topics. ,  introduce an adaptive microblog filtering task that focuses on tracking topics of broad and dynamic nature.  propose an entirely-unsupervised approach that adapts to new aspects of the topic to retrieve relevant microblogs.  evaluated our filtering approach using 6 broad topics, each tested on 4 different time periods over 4 months. Experimental results shod that, on average, our approach achieved 84% increase in recall relative to the baseline approach, while maintaining an acceptable precision that shod a drop of about 8%. Our filtering method is currently implemented on TetMogaz, a news portal generated from tets. The bsite compiles the stream of Arabic tets and detects the relevant tets to different regions in the Middle East to be presented in the form of comprehensive reports that include top stories and news in each region. Broad topics on Twitter are highly dynamic.Boolean filtering retrieve high precision but limited number of tets.A proposed adaptive filtering achieved 84% gain in recall with slight drop in prec.Proposed method shod robustness over time, across domains, and query formulations.Our method is currently adopted in a live service that follows news from Twitter.
['Walid Magdy', 'Tamer Elsayed']
I am writing a research paper on a idea Unsupervised adaptive microblog filtering for broad dynamic topics
[{"from": "human", "value": "I am writing a research paper on a idea Unsupervised adaptive microblog filtering for broad dynamic topics"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Information filtering has been a major task of study in the field of information retrieval (IR) for a long time, focusing on filtering ll-formed documents such as news articles. Recently, more interest was directed towards applying filtering tasks to user-generated content such as microblogs. Several earlier studies investigated microblog filtering for focused topics. Another vital filtering scenario in microblogs targets the detection of posts that are relevant to long-standing broad and dynamic topics, i.e., topics spanning several subtopics that change over time. This type of filtering in microblogs is essential for many applications such as social studies on large events and news tracking of temporal topics. ,  introduce an adaptive microblog filtering task that focuses on tracking topics of broad and dynamic nature.  propose an entirely-unsupervised approach that adapts to new aspects of the topic to retrieve relevant microblogs.  evaluated our filtering approach using 6 broad topics, each tested on 4 different time periods over 4 months. Experimental results shod that, on average, our approach achieved 84% increase in recall relative to the baseline approach, while maintaining an acceptable precision that shod a drop of about 8%. Our filtering method is currently implemented on TetMogaz, a news portal generated from tets. The bsite compiles the stream of Arabic tets and detects the relevant tets to different regions in the Middle East to be presented in the form of comprehensive reports that include top stories and news in each region. Broad topics on Twitter are highly dynamic.Boolean filtering retrieve high precision but limited number of tets.A proposed adaptive filtering achieved 84% gain in recall with slight drop in prec.Proposed method shod robustness over time, across domains, and query formulations.Our method is currently adopted in a live service that follows news from Twitter."}]