Implicit Web: Information Voyeurism

The internet has become a wellspring of really ‘neat’ information for those of us that like to know what’s going on ‘under the surface’. For instance I have a wpstats plugin that allows me to see how many visitors I have at my blog, what browser they use, and what pages they visit. It sure beats sending out questionnaires and wading through the irrelevant, superfluous, and inaccurately self-reported information available through more traditional channels of inquiry.

Not surprisingly, there is a term for this new and valuable source of information. The newly minted term is implicit web. The following is from Wikipedia:

The Implicit Web is a concept coined in 2007 to denote web sites which specialize in the synthesis of personal information gleaned from the Internet into a single, coherent picture of user behavior. Implicit data may include clickstream information, media consumption habits, location tracking or any data generated without “explicit” input from a user. Presumed advantages of implicit data include accuracy, ease of input and comprehensiveness.

This implicit information is everywhere on the web. I have Alexa rankings and PageRank indicators on my toolbar that tell me, not only how my weblog ranks relative to the rest of the websites in the universe, but also how well any other site I visit is doing. Seller rankings on eBay alert me about who to trust and who not to trust when I am making a purchase. Statistics provided on internet forums allow me to know how long other community members have been participating in the community forum and how many posts they have contributed.

Amazon provides me with behavioral patterns of others who have sought information about books or music I am currently searching. I know about similar titles they have looked at and I am privy to their ultimate choice. (Of the users who have looked at book X, Y and Z, 73% chose to buy book X)

The implicit information available on the web is descriptive, not interpretive. However, in spiteof this fact and much to the delight of Amazon, and others who employ this implicit information, it is tempting, and often inevitable, for consumers to draw qualitative conclusions from this data. “Wow, if 73% chose book X, book X must be the better choice.”

People like this type of information. From an early age we learned that if “4 out of 5 doctors recommended” something, we needed to pay attention. Now, as the influence of doctor’s opinions has waned, we have come to rely on the opinion of a cadre of anonymous web surfers.

Part of the appeal of “implicit web” is it’s quasi-clandestine nature. Implicit data is collected and processed as a matter of course. No one asks our permission to collect it, the software does it automatically. The involuntary nature of this information tends to give it a validity that it might not otherwise have. No one has to ask me “How’s your website doing?” They can look at my Alexa and PageRank information and draw their own conclusions.

If they had asked I could have told them that my current PageRank was 3 and that my current Alexa Ranking was just under half a million but that wouldn’t have been nearly as much fun as the thrill of self-discovery that comes from ‘not having to ask’. So far, Amazon, eBay, and others are content to allow consumers access to this information. It has resulted in a perceived transparency that has translated into increased trust between vendor and consumer.

Community forums also take advantage of this type of information. Within traditional, face-to-face communities we rely on observable cues such as age, sex, body language, and tone of voice to obtain meta-linguistic cues about fellow forum members. In the absence of these cues internet forums provide both explicit cues (user names and avatars) and implicit cues such as the date someone joins the community and their level of participation. Presumably community members would temper their comments in a threaded discussion differently if they were responding to someone who joined the community two years ago and had contributed a total of 5000 posts on community discussion boards (a rate of almost 7 posts per day), as opposed to someone who had joined yesterday.

In the absence of visual and auditory cues, it is primarily through text and implicit data that community members learn to judge one another. It is also by means of this implicit data that researchers can gain insights into the dynamics of a community. Handicapped by the lack of traditional cues, researchers now have a new form of reliable behavioral cues to explore.

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