Communications of the ACM

content associated with large cities and tourist attractions. Another example of the network effect of Web bias is the link structure of the Web itself. Figure 3 plots the number of links from the Web within Spain to other countries, along with exports from Spain to the same other countries. 3 The countries toward the bottom right are outliers, as they had all sold the right to use their domains for other purposes (such as the .fm country code, top-level domain for the Federated States of Micronesia). Ignoring them, the correlation between exports and number of links is more than 0.8 for Spain. In fact, the more developed a country is, the greater is the correlation, ranging from

0.6 for Brazil to 0.9 for the U.K. 4

(such as number of pages per website or number of links per webpage). Figure 2 plots the number of links in U.K. webpages on the x-axis and the number of webpages on the y-axis. Zipf’s law is clearly visible on the right side, in the line with the more negative slope. However, there is a strong social force at the beginning of the x-axis I call the “shame effect” that makes the slope less negative. It also illustrates that many people prefer to exert the least effort, though most people also need to feel they do enough to avoid feeling ashamed of their effort. 5 These two effects are common characteristics of people’s activity on the Web.

Finally, Nobel laureate Herbert Simon said, “A wealth of information creates a poverty of attention.” Activity bias thus generates a “digital desert” across the Web, or Web content no one ever sees. A lower bound comes from Twitter data where Saez-Trumper and I8 found that 1.1% of the tweets were written and posted by people without followers. Reviewing Wikipedia use statistics gave us an upper bound, whereby 31% of the articles added or modified in May 2014 were never visited in June. The actual size of the digital desert on the Web likely lies in the first half of the 1% to 31% range.

On the other hand, bias is not always negative. Due to activity bias, all levels of Web caching are highly effective at keeping the most used content readily available, and the load on websites and the Internet network in general is then much lower than would be potentially possible.

Data Bias

As with people skills, data quality is
heterogeneous and thus, to some ex-
tent, expected to be biased. People
working in government, universities,
and other institutions that deal with
information should publish data of
higher quality and less bias, while so-
cial media as a whole is much larger,
biased, and without doubt, of lower
average quality. On the other hand,
the number of people contributing to
social media is probably at least one
order of magnitude greater than the
number of people working in informa-
tion-based institutions. There is thus
more data of any quality coming from
all people, including high-quality data,
no matter what definition of what qual-
ity one uses. Still, a lot of fake content
on the Web seems to spread faster than
reliable content. 17

The first set of biases seen in people interacting with the Web is due to their demographics. Accessing and using the Internet correlates with educational, economic, and technological bias, as well as other characteristics, causing a ripple effect of bias in Web content and links. For example, it is estimated that over 50% of the most popular websites are in English, while the percentage of native English speakers in the world is approximately only 5%; this increases to 13% if all English speakers are included, as estimated by Wikipedia. Geographical bias is also seen in Web