Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: For the majority of us, inter-personal communication is an essential part of our daily lives. Instant Messaging, or IM, has been growing in popularity for personal and work-related communication. The low cost of sending a message, combined with the limited awareness provided by current IM systems result in messages often arriving at inconvenient or disruptive times. In a step towards solving this problem,  created statistical models that successfully predict responsiveness to incoming instant messages -- simply put: whether the receiver is likely to respond to a message within a certain time period. These models re constructed using a large corpus of real IM interaction collected from 16 participants, including over 90,000 messages. The models  present can predict, with accuracy as high as 90.1%, whether a message sent to begin a new session of communication would get a response within 30 seconds, 1, 2, 5, and 10 minutes. This type of prediction can be used, for example, to drive online-status indicators, or in services aimed at finding potential communicators.
['Daniel Avrahami', 'Scott E. Hudson']
I am writing a research paper on a idea Responsiveness in instant messaging: predictive models supporting inter-personal communication
[{"from": "human", "value": "I am writing a research paper on a idea Responsiveness in instant messaging: predictive models supporting inter-personal communication"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: For the majority of us, inter-personal communication is an essential part of our daily lives. Instant Messaging, or IM, has been growing in popularity for personal and work-related communication. The low cost of sending a message, combined with the limited awareness provided by current IM systems result in messages often arriving at inconvenient or disruptive times. In a step towards solving this problem,  created statistical models that successfully predict responsiveness to incoming instant messages -- simply put: whether the receiver is likely to respond to a message within a certain time period. These models re constructed using a large corpus of real IM interaction collected from 16 participants, including over 90,000 messages. The models  present can predict, with accuracy as high as 90.1%, whether a message sent to begin a new session of communication would get a response within 30 seconds, 1, 2, 5, and 10 minutes. This type of prediction can be used, for example, to drive online-status indicators, or in services aimed at finding potential communicators."}]