The Hashtag Fail Whale

If you’ve spent time trying to analyze twitter data you undoubtedly have come across the topic problem.

The Problem
“What’s this tweet about?”
“Are there other tweets that are related?”

Somewhere along the way, Twitter, it seems, decided the hashtag was enough to answer the above.

Hashtags are the wild west of naming conventions. Anyone can create or use one (and most people don’t). Further, with a character limited tweet, proper tagging cuts into tweet content.

But as a user, I want to be able to find all tweets about a news story, an event, a book that was just released. Relying on Twitter users to manually add hashtags to do that is a fail whale of a different color.

The Solution
Why hasn’t Twitter borrowed from Delicious – a service that finds the balance between the freedom and discovery of a folksonomy, and the clarity and utility of a taxonomy?

Why not auto suggest tags as someone generates the tweet?  And these tags don’t count against the character limit.  And further, if someone doesn’t select or create a tag, Twitter auto generates one (and designates it differently than a user generated/selected one).

Just as I can search for an @ so I can connect a person, making a # (or a new version of a tag) work the same way could make Twitter even more powerful — and make it more money.

A Consumer Use Case
Imagine I just watched top chef last week and I wanted to Tweet about a cheftestant.  I start drafting my pithy tweet and auto suggested tags include: top chef, top chef season 8, top chef season 8 episode 7, top chef cheftestant smith.

Since I’m commenting on Cheftestant Smith about her performance in episode 7 of the current season, I select those two last tags.

Later, when a fan of Cheftestant Smith (or she herself), is searching for tweets, she doesn’t have to use an @ people search on herself (and if she used twitter, the autosuggest would suggest her @ handle as well).  But more useful, instead of relying on a #topchef tag, searching on top chef would bring up all the tags as discussed above, allowing a user to see more related tweets (rather than those hashtagged), less noise (created by NLP searches), and drill down (a certain episode, ingredient, etc of the episode).

But how does this make Twitter money?

The Business Case

1.  Tag Bidding

2. Page Buying

3.  Tag Structuring

Using the previous use case, imagine where Bravo can get value.  First, they bid on auto suggest tags — they can help structure the conversation.  And auto suggest will bring in both “paid” and “organic” tags to ensure the user still finds value in the tags.

Then, for each tag, a “page” can be registered/bought.  When someone clicks a tag, they go to that page that aggregates the conversations, but also allows for custom content as well — sponsored links blossom into sponsored tags and pages.

Finally, organizations can created “tag structures.”  In the Iron Chef example, Bravo could create a taxonomy of tags, creating order and hierarchy around the folksonomy — show level tags, episode, etc.

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