1. A computer implemented method of targeting advertisement, comprising: receiving, by a computer-based system, a topic selected by a user; retrieving topic-matching content matching the selected topic; extracting, by the computer-based system, a plurality of different topical memes from the topic-matching content, wherein each of the extracted topical memes is a point of discussion within the selected topic; measuring an engagement level for each piece of the topic-matching content; obtaining, based on the measuring, a respective viral dynamics metric for each said piece of the topic-matching content, wherein a viral dynamics metric indicates popularity of the respective piece of the topic-matching content; accumulating, for each of the extracted topical memes, the viral dynamics metrics of the pieces of the topic-matching content; obtaining, based on the accumulating, a respective aggregate viral dynamics value for each of the extracted topical memes; determining a most active meme from the extracted topical memes, based on the aggregate viral dynamics value for each of the extracted topical memes; running an advertisement campaign against the most active meme, wherein said advertisement campaign is targeted to web pages containing the most active meme; selecting an advertising network servicing a number of content sources hosting the web pages containing the most active meme; selecting an advertisement assigned to the most active meme; and delivering the selected advertisement to the number of content sources, through the selected advertising network.
1. A computer implemented method of targeting advertisement, comprising: receiving, by a computer-based system, a topic selected by a user; retrieving topic-matching content matching the selected topic; extracting, by the computer-based system, a plurality of different topical memes from the topic-matching content, wherein each of the extracted topical memes is a point of discussion within the selected topic; measuring an engagement level for each piece of the topic-matching content; obtaining, based on the measuring, a respective viral dynamics metric for each said piece of the topic-matching content, wherein a viral dynamics metric indicates popularity of the respective piece of the topic-matching content; accumulating, for each of the extracted topical memes, the viral dynamics metrics of the pieces of the topic-matching content; obtaining, based on the accumulating, a respective aggregate viral dynamics value for each of the extracted topical memes; determining a most active meme from the extracted topical memes, based on the aggregate viral dynamics value for each of the extracted topical memes; running an advertisement campaign against the most active meme, wherein said advertisement campaign is targeted to web pages containing the most active meme; selecting an advertising network servicing a number of content sources hosting the web pages containing the most active meme; selecting an advertisement assigned to the most active meme; and delivering the selected advertisement to the number of content sources, through the selected advertising network. 2. The method of claim 1 , wherein each of the extracted topical memes is extracted by applying a feature extraction algorithm to the topic-matching content.