1. A computer-implemented method for managing user attention by detecting hot topics in social indexes, comprising the steps of: maintaining articles of digital information and at least one social index comprising topics that each relate to one or more of the articles; retrieving topic models matched to the digital information for each topic, comprising: determining a form of training used to form the topic models; generating a training score under example-based training for each topic chosen, comprising: counting numbers of positive and negative training examples; setting a training score in proportion to the positive and negative training examples numbers used; identifying a number of characteristic words comprised in the articles relating to the topic; and adjusting the training score in proportion to the number of the characteristic words; and favoring those topics with the higher training scores, wherein the topic models comprise at least one of fine-grained topic models and coarse-grained topic models; classifying the articles under the topics using the topic models; evaluating each of the topics in the social index for hotness, comprising: defining a plurality of time periods projected from the present; evaluating counts of the articles appearing under each time period; choosing the topics exhibiting a rising curve in the count of the articles that increases with recency during the time periods; and analyzing quality of the articles within the topics chosen; and presenting the topics comprised of the articles having acceptable quality, wherein the steps are performed on a suitably-programmed computer.
1. A computer-implemented method for managing user attention by detecting hot topics in social indexes, comprising the steps of: maintaining articles of digital information and at least one social index comprising topics that each relate to one or more of the articles; retrieving topic models matched to the digital information for each topic, comprising: determining a form of training used to form the topic models; generating a training score under example-based training for each topic chosen, comprising: counting numbers of positive and negative training examples; setting a training score in proportion to the positive and negative training examples numbers used; identifying a number of characteristic words comprised in the articles relating to the topic; and adjusting the training score in proportion to the number of the characteristic words; and favoring those topics with the higher training scores, wherein the topic models comprise at least one of fine-grained topic models and coarse-grained topic models; classifying the articles under the topics using the topic models; evaluating each of the topics in the social index for hotness, comprising: defining a plurality of time periods projected from the present; evaluating counts of the articles appearing under each time period; choosing the topics exhibiting a rising curve in the count of the articles that increases with recency during the time periods; and analyzing quality of the articles within the topics chosen; and presenting the topics comprised of the articles having acceptable quality, wherein the steps are performed on a suitably-programmed computer. 2. A method according to claim 1 , wherein the time periods comprise, in order of decreasing weight, one or more of day over week, day over month, and week over month.