Abstract: A method, computer program product and system are disclosed for determining the semantic density of textualized digital media (a measure of how much information is conveyed in a sentence or clause relative to its length). The more semantically dense text is, the more information it conveys in a given space. Users input a topic, a timeline, and one or more target web media sources for analysis. Text in the target media sources is deconstructed to determine density, and a density rating assigned to the web media source. Over time, users can track trends in the density of text media relative to a given topic, and determine how much information is being conveyed in connection with the topic, such as a political campaign. Line graphs, pie charts, and other time-elapsed output graphic representations of the semantic density are generated and rendered for the user.

Abstract: A method, computer program product and system are disclosed for determining the semantic density of textualized digital media is (a measure of how much information is conveyed in a sentence or clause relative to its length). The more semantically dense text is, the more information it conveys in a given space. Users input a topic, a timeline, and one or more target web media sources for analysis. Text in the target media sources is deconstructed to determine density, and a density rating assigned to the web media source. Over time, users can track trends in the density of text media relative to a given topic, and determine how much information is being conveyed in connection with the topic, such as a political campaign. Line graphs, pie charts, and other time-elapsed output graphic representations of the semantic density are generated and rendered for the user.