Tales of Accidental SEO

I have a post on this blog that’s a top-10 result for a pretty generic search term. Yes, the page is relevant, and uses the terms in question pretty frequently. But, honestly, I don’t make any effort at SEO on this blog: it’s a soapbox, not a marketing engine, and I don’t care to invest the time and energy necessary to get myself into the top ranks on the SERPs. But somehow I’ve done it accidentally, and I think I know how.

By linking my Blogger profile to my Google+ profile, my blog posts become “social media” content in some part of Google’s algorithm. Because “social media” and the “live web” are the hip things in search engineering these days, that gets me an arbitrarily huge boost in rank. It’s not based on profiling either: I can run the search anonymously and get the same results, and I can have friends that don’t use Google+ run the search and get the same results.

Why do I think it’s related to Google+ at all? My profile picture of G+ is right next to the post (though, oddly, none of the photos from the actual post are in there), and it includes the byline “by Adrian Price – in 70 Google+ circles”. That’s not part of my blog post, that’s not even part of my Blogger profile, aside from the fact that it is linked to my Google+ profile.

Social marketing in so many ways is your parents trying to talk to you in the same language you use with your friends. To poach a phrase, it’s so unhip it’s a wonder their bums don’t fall off. Honestly, almost every social marketing effort I’ve ever seen reeks of desperation, confusion, and so much effort trying to “seem engaged” that they would have saved time in the end actually being engaged.

So, any marketers out there desperately trying to squeeze every drop of ROI they can out of social media, consider this: it looks like, just maybe, you can get quite a lot out of it just by having it at all, even if you aren’t using it to push out news, or contests, or desperately promoting your latest video in an attempt to force it to “go viral”. Who knows how long the free lunch will last, but you might as well take advantage while you can.


Today marks the public launch of Stackoverflow.com, the new programming Q&A site started by the famous Joel Spolsky. So far it looks like it’s shaping up to be a very good resource for developers; and I agree with everything they said about Googling for solutions in this post. You can look me up on the site.

Beyond the site itself, which I think will become an oft-used resource for me, I also really like their badge system. I think a similar system could be a huge boon to any forum, social networking site, or other community site. Game developers learned years ago that people will be more active in an area they already enjoy if they can be recognized for their activity. The same idea applies just as well to community sites. I think it’s brilliant, and I think we’re going to be seeing similar implementations a lot more often.

Symbolic Tagging: Tags 2.0

Tagging has become extremely popular these days, and with good reason – people naturally catalog things under various categories mentally, and are able to recall them by any of those routes. So, it makes sense to use a multiple-tagging system rather than a singular system like plain categories or folders.

Combining tagging with social networking, as Del.icio.us does, is particularly effective – a set of aggregate tags that allow a community to classify data for use by the whole. However, the problem with this is that not everyone uses the same tags to mean the same things.

The words are just symbols; tags demand meaning. It makes sense, in a simple system, to consider words and their meaning to be one and the same; however, as the system expands, it needs to understand the relationships between words and meanings. Enter symbolic tagging. Rather than making the tags the words and the words the tags, separate the two – after all, they are in fact separate.

From an architecture perspective, this means we need two constructs: the symbology (words) and the semantics (tags), with a 1..*:1..* relationship between the two. One symbol can have multiple semantics, and one semantic can have multiple symbols. As an example, let’s say two symbols, “foo” and “bar” both share a semantic X. When a user searches for “foo”, they will see all records associated with semantic X; the same when they search for “bar”. Alternately, assume the symbol “baz” is attached to two semantics, X and Y. When a user searches for “baz”, they will see all records associated with semantic X, Y, or both.

How does the system learn which symbols reflect which semantics? The same way it determines which records match which tags – community input. Take, for example, the following process for developing and refining a symbolic-semantic map.

1. As new records are added, the user adding them tags those records as they normally would. Any time a user inputs a tag that isn’t already in the map, a new symbol is created for the tag, and a new semantic is created for it as well. At initialization, the two share a 1:1 relationship.
2. The user may opt to go through a refinement process, either manually initiated, or initiated as an additional step in the new-record process. This refinement process prompts the user with a list of tags they have used, and for each tag T, lists other tags associated with records which are also associated with tag T. The user may mark zero or more of these related tags as being synonymous with the tag in question.
3. For each tag being marked synonymous, if that tag is associated with more than one semantic, the user may chose one or more semantic associations between the two tags. The semantics can be identified by the list of tags associated with them. If none of the semantic lines is appropriate, the user may choose “other” to create a new semantic and link it to the two tags being compared. If the tag in question is only associated with one semantic, that semantic is automatically used, avoiding the additional step.

This allows the system to continue its organic social self-construction, while greatly improving the quality of the tag browsing/searching system as a whole, and imparting exponentially more meaning on the dataset itself, which can be used in other areas of research – the data from one large-scale implementation could prove invaluable for semantic computing, computer linguistics, and social networking research and development.

So, Del.icio.us – are you up to the task?