At some point you’ll need to filter information from your organization’s social media systems to avoid information overload. This article discusses considerations in using ”metadata” for filtering, whether implemented by algorithm or by human trial and error.
- If someone defines their filters too narrowly, they reduce the opportunity for serendipity; but if they define their filters too widely, they are back to information overload.
- Knowing how many people have read an item is a big clue to its value
- When you look at content ratings consider that people are more comfortable giving positive ratings than negative ones, though cultural differences exist between Europe and US [article doesn’t say which way this difference goes… anybody have any ideas on that?]
- Comments indicate how interesting something is — number of commenters suggests breadth of interest and number of comments its depth
- While the most valued content does not always come from the most senior employees, high ratings from highly ranked employees usually have more weight
Implement an enterprise social network without adequate filtering and you risk subjecting employees to information overload. Or if they deal with it by ignoring the social network content altogether, they end up with too little information.
Only by embracing the rich vein of content metadata that a social network provides, will employees be able to find the information they need. Via InfoManagement Direct