Filtering the Flood of Informationby Seb
So we’ve reached the end of our series on dealing with information overload. Our final post will examine one of the most established strategies: filtering. Defined as paying attention to just the most important and helpful information, and leaving aside any other sources, filtering is something we do every day, consciously or subconsciously.
Filtering can be viewed in two ways, depending on the flow of information you are dealing with. It can be seen as a process by which you search for information that is useful to you, or a process by which you block information that is not useful to you. Either way, filtering is a necessary tool for sifting out the useful from the useless.
Information Overload or Filter Failure?
Filtering is such an essential part of coping with information overload that American writer Clay Shirky suggests we replace the very phrase ‘information overload’ with the term ‘filter failure’. He believes that we need to accept the fact that the volume of information we are exposed to will continue to increase, and what we need to pay attention to is the way we filter it. The traditional filters we have always relied on are now broken, and we need to establish new, more robust filter systems.
Shirky suggests that the internet has increased the need to filter information ourselves because it has removed the economic necessity for others to filter information for us.
In the past much of our information came from books or newspapers, and the data presented in these would be filtered for quality, style, and entertainment value by publishers or editors. They had a financial incentive for performing this filter function: poorly filtered–in this case, edited and drafted–books or newspapers didn’t sell.
The internet has removed the economic need for this type of filtering. The cost of publishing online is so low that anyone can do it, and there is nobody to judge the value or quality of that information. In some ways this feels liberating. There are no editors or publishers to decide on our behalf what we will or will not find useful or valuable. But there’s a cost. We are flooded with a huge volume of questionable information to which we need to apply our own filters.
Categories of Filtering
There are various ways that we filter information, and Tim Kastelle suggests that these can be broken down into two categories, manual judgemental filtering, or automated mechanical filtering.
People have always constructed their own filters to provide information that they find relevant or interesting and to exclude everything else. If you always buy the same daily newspaper or tune into the same radio station, you are implementing a filter that provides you with information from a certain viewpoint, or expressed in a particular style. If you always shop at the same store, you are effectively filtering out all the products that other stores may have to offer.
Judgemental filtering is what people do all the time. They look at information, perhaps read or watch it, evaluate it, analyse it, and finally decide whether it has any value for them. This can be a long and slow process, which isn’t always efficient in today’s information-rich world, although filtering information this way does get quicker the more you know about the subject in question.
One way to increase the efficiency of judgemental filtering is word of mouth information. This means relying on networks of friends or colleagues to filter information for you and to let you know what would be valuable to you. A simple example would be talking to friends about TV shows that are worth watching, rather than wading through hundreds of channels to find something you might enjoy.
Social media has rapidly expanded this concept of network filtering, and although this sometimes means we have better access to knowledgeable people to suggest relevant and valuable information, it can also mean that our filters become less effective and too much information gets through.
When we talk about mechanical filtering we usually mean algorithmic filtering, which is the basis of most search engines and RSS feeds. The most famous is probably Google’s complex algorithms which use an intricate set of rules to decide which of the millions of web pages on the internet are likely to be of interest to us, and which ones should be relegated to the realms of obscurity.
Algorithmic filtering is widespread on the internet, where it is used to determine which blog posts show up in your RSS feed, or which friend’s status updates will appear on your Facebook timeline. Algorithms are continually evolving in an attempt to provide users with relevant, valuable information, and to emulate the judgemental process involved in manual filtering.
At Specificfeeds we appreciate the value of manual filtering, but acknowledge that it is simply not powerful enough to cope with proactive information pushes we have to deal with on a daily basis. We specialise in filtering information into clean, precise, specific streams that will ensure users end up with something that interests them and that will provide them the value they are looking for.