Maximizing Collective Attention: The Digg Study

Successful viral marketing requires the attention of internet users. This is difficult when users are inundated daily with online content. As a result, there is an inherent competition for attention between pieces of content. Understanding how attention to items grows and fades across online groups (forums, social networks, social news sites) can provide an edge in this competition when the content is catered to the trends.

The Information Dynamics Laboratory at HP Labs conducted a study on this very concept by evaluating the growth and decay of the attention content receives as determined by the users of
Digg.com. What they found was that novelty and popularity interact to determine the growth and saturation of collective attention. The mathematical analysis the authors perform suggests that the rate of growth and decay of attention changes over time and that the novelty of the content is the primary determining factor that affects this rate. The explicit model for collective attention can be found in the full study.

The findings of this analysis are useful for managing a viral marketing campaign. That novelty is found to be such an important factor suggests that it should be the focus behind viral content. If there are a lot of other stories out there of similar topic, users may pass the viral content up as it appears to be ‘just another story.’ Popularity is clearly important as well; as users increase a content’s popularity, the behavior of other users will be to see what all the fuss is about. However, the study is quite clear popularity is good, but novelty is key.

For those mathematically inclined, the model described by the authors provides a way to track the collective attention of submitted viral content. This enables a means of predicting when that content will fall out of favor with the online masses and when new, novel content should be submitted to keep the campaign active. The predictive power of this model could be powerful, but be forewarned: different communities and changing attitudes within communities may alter parts of the model and skew predictions.

May 28, 2008 |

Maximizing Collective Attention: The Digg Study

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