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Inequality on Twitter

Inequality on Twitter

A lot has been written about economic inequality as measured by distribution of income, wealth, capital gains, etc. In previous posts such as Inequality, Lorenz-Curves and Gini-Index or Visualizing Inequality we looked at various market inequalities (market share and capitalization, donations, etc.) and their respective Gini coefficients.

With the recent rise of social media we have other forms of economy, in particular the economy of time and attention. And we have at least some measures of this economy in the form of people’s activities, subscriptions, etc. Whether it’s Connections on LinkedIn, Friends on FaceBook, Followers on Twitter – all of the social media platforms have some social currencies for attention. (Influence is different from attention, and measuring influence is more difficult and controversial – see for example the discussions about Klout-scores.)

Another interesting aspect of online communities is that of participation inequality. Jakob Nielsen did some research on this and coined the well-known 90-9-1 rule:

“In most online communities, 90% of users are lurkers who never contribute, 9% of users contribute a little, and 1% of users account for almost all the action.”

The above linked article has two nice graphics illustrating this point:

Illustration of participation inequality in online communities (Source: Jakob Nielsen)

As a user of Twitter for about 3 years now I decided to do some simple analysis, wondering about the degrees of inequality I would find there. Imagine you want to spread the word about some new event and send out a tweet. How many people you reach depends on how many followers you have, how many of those retweet your message, how many followers they have, how many other messages they send out and so on. Let’s look at my first twitter account (“tlausser”); here are some basic numbers of my followers and their respective followers:

Followers of tlausser Followers on Twitter

Some of my followers have no followers themselves, one has nearly 100,000. On average, they have about 3600 followers; however, the total of about 385,000 followers is extremely unequally distributed. Here are three charts visualizing this astonishing degree of inequality:

Of 107 followers, the top 5 have ~75% of all followers that can be reached in two steps. The corresponding Gini index of 0.90 is an example of extreme inequality. From an advertising perspective, you would want to focus mostly on getting these 5% to react to your message (i.e. retweet). In a chart with linear scale the bottom half does barely register.

Most of my followers have between 100-1000 followers themselves, as can be seen from this log-scale Histogram.

What kind of distribution is the number of followers? It seems that Log[x] is roughly normal distributed.

As for participation inequality, let’s look at the number of tweets that those (107) followers send out.

Some of them have not tweeted anything, the chattiest has sent more than 16,000 tweets. On average, each follower has 1280 tweets; the total of 137,000 tweets is again highly unequally distributed for a Gini index of 0.77.

The top 10 make up about 2/3 of the entire conversation.

Again the bottom half hardly contributes to the number of tweets; however, the ramp in the top half is longer and not quite as steep as with the number of followers. Here is the log-scale Histogram:

I did the same type of analysis for several other Twitter Users in the central range (between 100-1000 follower). The results are similar, but certainly not yet robust enough to statistical sampling errors. (A larger scale analysis would require a higher twitter API limit than my free 350 per hour.)

These preliminary results indicate that there are high degrees of inequality regarding the number of tweets people send out and even more so regarding the number of followers they accumulate. How many tweets Twitter users send out over time is more evenly distributed. How many followers they get is less evenly distributed and thus leads to extremely high degrees of inequality. I presume this is caused in part due to preferential attachment as described in Barabasi’s book “Linked: The new science of networks“. Like with all forms of attention, who people follow depends a lot on who others are following. There is a very long tail of small numbers of followers for the vast majority of Twitter users.

That said, the degree of participation inequality I found was lower than the 90-9-1 rule, which corresponds to an extreme Gini index of about 0.96. Perhaps that’s a sign of the Twitter community having evolved over time? Or perhaps just a sign of my analysis sample being too small and not representative of the larger Twitterverse.

In some way these new media are refreshing as they allow almost anyone to publish their thoughts. However, it’s also true that almost all of those users remain in relative obscurity and only a very small minority gets the lion share of all attention. If you think economic inequality is too high, keep in mind that attention inequality is far higher. Both are impacting the policy debate in interesting ways.

Turning social media attention into income is another story altogether. In his recent Blog post “Turning social media attention into income“, author Srininvas Rao muses:

“The low barrier to entry created by social media has flooded the market with aspiring entrepreneurs, freelancers, and people trying to make it on their own. Standing out in it is only half the battle. You have to figure out how to turn social media attention into social media income. Have you successfully evolved from blogger to entrepreneur? What steps should I take next?”

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Posted by on December 6, 2011 in Industrial, Scientific, Socioeconomic

 

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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).

 
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Posted by on September 9, 2011 in Scientific, Socioeconomic

 

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