Robin Good: Alexis Dufresne of Faveeo, an up and coming information filtering and discovery tool not yet available to the general public, has been posting some interesting articles on topics related to news curation, filtering and discovery.
In particular, I found interesting his recent analysis on automated solutions and algorithms designed to help scale curation efforts, as these are generally discarded as inappropriate for any type of professional work. But, as he rightly points out, there are several tasks inside a curator workflow that can indeed help and reduce the curator's workload without limiting his ability to manually select and edit what he finds most appropriate.
Alexis pinpoints at least three different areas in which algorithms and automated operations can indeed greatly help the curator's work. These are:
1) Discovery of new sources and networks: ...By teaching a machine about the kind of sources and users a curator is looking for, a machine could process from the incredible mass of sources and people out there to figure out those who are likely to be trusted sources of information. By using techniques of text analysis, social reach, semantic density, popularity and more, this task could be done by a machine.
2) Learning the profile of a curator: A lot of engines are focusing on filtering the semantic meaning of an article in order to recommend other content. But by using advanced NLP techniques and text extraction methods, we could go further and have an idea of the tone, the lenght and other signals that can indicate the preferences of a human curator, other than simply the actual keywords used in the text.
3) Social recommendations: ...By detecting users that seem to click, like, share or save the same articles, we can connect them together to mutualize their search and discovery operations, in order to speed things up.
Rightful. Helpful. 8/10
Via Robin Good