Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Microblogging services such as Twitter allow users to interact with each other by forming a social network. The interaction beten users in a social network group forms a dialogue or discussion. A typical dialogue beten users involves a set of topics.  make the assumption that this set of topics remains constant throughout the conversation. Using this model of social interaction beten users in the Twitter social network, along with content-derived location information,  employ a probabilistic framework to estimate the city-level location of a Twitter user, based on the content of the tets in their dialogues, using reply-tet messages.  estimate the city-level user location based purely on the content of the tets, which may include reply-tet information, without the use of any external information, such as a gazetteer, IP information etc. The current framework for estimating user location does not consider the underlying social interaction, i.e. the structure of interactions beten the users. ,  calculate a baseline probability estimate of the distribution of words used by a user. This distribution is formed by using the fact that terms used in the tets of a certain discussion may be related to the location information of the user initiating the discussion.  also estimate the top K probable cities for a given user and measure the accuracy.  find that our baseline estimation yields an accuracy higher that the 10% accuracy of the current state of the art estimation.
['Swarup Chandra', 'Latifur Khan', 'Fahad Bin Muhaya']
I am writing a research paper on a idea Estimating Twitter User Location Using Social Interactions--A Content Based Approach
[{"from": "human", "value": "I am writing a research paper on a idea Estimating Twitter User Location Using Social Interactions--A Content Based Approach"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Microblogging services such as Twitter allow users to interact with each other by forming a social network. The interaction beten users in a social network group forms a dialogue or discussion. A typical dialogue beten users involves a set of topics.  make the assumption that this set of topics remains constant throughout the conversation. Using this model of social interaction beten users in the Twitter social network, along with content-derived location information,  employ a probabilistic framework to estimate the city-level location of a Twitter user, based on the content of the tets in their dialogues, using reply-tet messages.  estimate the city-level user location based purely on the content of the tets, which may include reply-tet information, without the use of any external information, such as a gazetteer, IP information etc. The current framework for estimating user location does not consider the underlying social interaction, i.e. the structure of interactions beten the users. ,  calculate a baseline probability estimate of the distribution of words used by a user. This distribution is formed by using the fact that terms used in the tets of a certain discussion may be related to the location information of the user initiating the discussion.  also estimate the top K probable cities for a given user and measure the accuracy.  find that our baseline estimation yields an accuracy higher that the 10% accuracy of the current state of the art estimation."}]