Bit.ly link analysis on half-life of web content

The team at URL-shortening website Bit.ly has posted an interesting analysis on the attention span to links shared on the Internet via different social media platforms. This provides some quantification to what some have termed internet impatience. Most shared web links experience an initial burst of attention immediately after publication followed by a steep decay to near-zero relative activity. A useful measure is a link’s half-life, defined as the time interval between its peak frequency and half of the rest of all clicks over its lifetime.

From the Bit.ly Blog:

So we looked at the half life of 1,000 popular bitly links and the results were surprisingly similar. The mean half life of a link on twitter is 2.8 hours, on facebook it’s 3.2 hours and via ‘direct’ sources (like email or IM clients) it’s 3.4 hours. So you can expect, on average, an extra 24 minutes of attention if you post on facebook than if you post on twitter.

Distribution of web link half-lifes (Source: Bit.ly Blog)

This half-life distribution plot (x-axis 1 day = 86.400 seconds) of content shared via bit.ly links shows some interesting patterns:

• In general, content half-life is about 3 hours (10.000 sec)
• Content half-life does not depend on the medium through which it is shared
• YouTube content has a different distribution and a considerably longer half-life (about 7 hours)

One is tempted to relate such stats to one’s own browsing experience or look at systematic analysis of how people deal with shared links. For example, Microsoft’s Outlook team did extensive usability research on how people deal with incoming email so as to improve the usability of their mail reader. It was found that most emails fall into one of three categories (Open & Read immediately, Ignore & Discard, File & Flag for future reading). I speculate that bit.ly links received in Twitter or email will be similar, perhaps with the added category of retweet or forward (in the case of a story going viral). YouTube being different can perhaps be attributed to the fact that many videos require more time so we make a more deliberate decision as to whether and when we want to spend that time. For instance, one might say I want to watch this video tonight when I get home from work, which would fit with the 7 hours half-life.

In any event, such statistics show us that when it comes to clicking on shared links, our behavior is fairly predictable and probably driven by simple habits rather than complex thought. On one hand this allows good estimates for the expected life-time clicks. On the other hand, it can be a bit disconcerting to realize that our clicking behavior may be controlled by rather simple behavioral drivers (habitual classification, desire for instant gratification, out-of-sight out-of-mind, etc.). For instance, we usually give the most recent incoming news priority over other criteria of personal content preference. But is the latest really the greatest? I suspect that just like impulse-shopping there is a lot of impulse-clicking. And who does not know the exhausted feeling of getting lost while browsing and in hindsight regretting not having made the best use of one’s time… Perhaps this hints at more opportunities for more personalized and content-preference filtered news delivery mechanisms (such as the News reader app Zite, recently acquired by CNN).