Social search is garnering lots of attention these days. Yet despite all the hoopla, it's not likely to displace traditional algorithmic search any time soon.
What is social search? There isn't even a good definition. Just about everyone who's doing some form of it is trying a different approach. Simply put, social search tools are Internet way-finding services informed by human judgment.
"Way-finding" because they're not strictly search engines in the sense most people know them. And "human judgment" because at least one person, but more likely dozens, hundreds, or more, has consumed the content and decided it's worthy enough to recommend to others.
But "informed" can mean many things. In its broadest sense in this context, "informed" means influenced. In the best of all possible worlds this influence is helpful, thoughtful, and wholesome. Unfortunately, some informed influences on search results come from egregiously uninformed people. Sometimes, from downright idiots.
Social search takes many forms, ranging from simple shared bookmarks or tagging of content with descriptive labels to more sophisticated approaches that combine human intelligence with computer algorithms. Despite all the recent attention, social search really isn't new. So why's it such a hot topic? To understand, it's helpful to know some human-mediated search effort history.
A Brief History of Social Search
We've always had social search, even from the Internet's early days. Before the emergence of the first search engines in 1993 or thereabouts, people relied on pages with links to their favorite sites. One of the first was created by Web inventor Tim Berners-Lee, and it's still online, though most of the links are long since broken.
Yahoo, one of the first Web site directories, was created by a team of human editors who surfed the Web and wrote brief descriptions of the sites they found. The Open Directory Project, the Librarians' Index of the Internet, and the U.K.-based Resource Discovery Network are all Web site directories created by people that have been around since the early days.
You can argue even algorithmic search engines, which rely on automated software-based crawlers and indexing systems, are social search systems to a degree. After all, the software's written by people and incorporates judgments about site quality, relevance, and importance.
Indeed, Google's famous PageRank algorithm, which analyzes the Web's link structure and assigns more importance to pages with many high quality links pointing to them, is fundamentally a form of social search. PageRank relies on the collective judgment of Webmasters linking to other content on the Web. Links, in essence, are positive votes by Webmasters for their favorite sites.
Social search, as it's evolving today, incorporates both automated software and human judgments about Web content's nature. That's what makes social search intriguing — and fundamentally flawed, at least at this point.
Why? Several reasons.
Fundamentally, no matter how many people get involved with bookmarking, tagging, voting, or otherwise highlighting Web content, the scale and scope of the Web means most content will be unheralded by social search efforts. The Web's simply growing too quickly for humans to keep up with it.
That doesn't mean social search efforts aren't useful. In most cases, they are. It simply means people-mediated search will never be as comprehensive as algorithmic search.
Another problem arises with tagging. Despite their popularity, especially with the Web 2.0 mob, tags aren't a panacea for categorizing and organizing the Web. Used properly, tags can be very helpful in describing Web content. Problems arise with the inherent ambiguity of language. Words often have multiple meanings, and people can have different interpretations of the same word.
The Web lacks what librarians call a controlled vocabulary, a set of terms that have specific, unambiguous meanings that can be used in a uniform, consistent fashion by everyone tagging Web content. Without a controlled vocabulary, tagging ultimately remains a chaotic, messy process.
Another factor is human laziness. Even if a controlled vocabulary were available, not everyone would take advantage of it. We've always had the ability to add tags and other metadata to Microsoft Office documents, yet how many people actually do this?
We also have idiots and spammers. Some people, no matter how well intentioned, simply do a poor job of labeling content. Others will deliberately mislabel content to attempt to fool search engines. In both cases, it's difficult for software to recognize this spuriously labeled content. In social search, it's difficult to filter the noise from the signal.
Despite these problems, social search still holds promise for improving our overall information seeking and consuming activities on the Web. Ultimately, it's likely that a combination of algorithmic search and the various types of social search systems will fuse into a hybrid that will work really well to satisfy a wide variety of information needs.
We're not there yet, but I expect to see real progress sometime over the next couple of years.
Next: a look at the current crop of social search systems and the unique approaches and benefits offered by each.
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