HP Lab’s research conducted on twitter – the world’s greatest public SMS network shows that the number of friends (and not user’s number of followers) is the actual driver of twitter user’s activity. In this post I will review HP’s research findings and present my disagreement with the research conclusions.

A study of social interactions within Twitter (via Snake Coffee), conducted by B. A. Huberman, D.M. Romero, and F. Wu (HP Labs) reveals that the driver of usage is a sparse and hidden network of connections underlying the “declared” set of friends and followers.
In a paper named Social networks that matter: Twitter under the microscope (PDF) the researchers investigated how relevant a list of “friends” is to members of the network. Practically, they were interested in finding out how many people each user communicates directly with through Twitter. They have defined user’s friend as a person whom the user directed at least two posts. By that the researchers were able to compare number of friends to number of followers.

Research Results:

1. Saturation of delivered content:

The researchers expected that users who receive attention from many people (hold many followers) will post more often than users who receive little attention. Indeed, the total number of posts increases with both the number of followers and friends. However, as Figure 1 shows, the number of total posts eventually saturates as a function of the number of followers. This implies that users with a large number of followers are not necessarily those with very large number of total posts.

On the other hand, the number of total posts does not saturate as a function of number of friends, as seen on Figure 2.

The researchers concluded that in order to predict how active a Twitter user is, the number of friends is a more accurate signal than the number of his followers.

2. Social saturation:

The research shows (see Figure 4) that even though the number of
friends initially increases as the number of followees increases, after a while the number of friends saturates. This trend can be explained by the fact that the cost of “declaring” a new followee is very low compared to the cost of maintaining friends. Hence, the number of people a user actually communicates with eventually stops increasing while the number of followees can continue to grow indefinitely.

3. Reciprocity:

The research presents evidence to reciprocated attention. On average, 90 percent of a user’s friends reciprocate attention by being friends of the user as well (see figure 6).

Research Conclusions:

1. Twitter users have a very small number of friends compared to the number of followers and followees they declare. This implies the existence of two different networks: a very dense one made up of followers and followees, and a sparser and simpler network of actual friends. The latter proves to be a more influential network in driving Twitter usage since users with many actual friends tend to post more updates than users with few actual friends. On the other hand, users with many followers or followees post updates more infrequently than those with few followers or followees.

2. A link between any two people does not necessarily imply an interaction between them. In the case of Twitter, most of the links declared within Twitter were meaningless from an interaction point of view.

My observations:

HP’s research provided an important reflection of interaction patterns which characterize twitter, namely reciprocity and the social interaction pattern.
At the same time, I find HP’s research assumptions to be mistaken. The twitter interaction is not based only on a personal direct interaction. The number of followers a twitter member holds reflects user’s ability to influence by content. Therefore- it can not be counted as “meaningless from an interaction point of view”. Twitter is not only a social network (and by that it differs from Facebook and MySpace) – its a User Generated Content as well.

TrendsSpotting will discuss twitter observations in a Trend Report – soon to be released. I would be happy to receive your own insights on twitter interaction patterns and social norms.

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