Databases Reference
In-Depth Information
CHAPTER 10
Social Networks and
Data Journalism
In this chapter we'll explore two topics that have started to become
especially hot over the past 5 to 10 years: social networks and data
journalism. Social networks (not necessarily just
online
ones) have
been studied by sociology departments for decades, as has their coun‐
terpart in computer science, math, and statistics departments: graph
theory. However, with the emergence of online social networks such
as Facebook, LinkedIn, Twitter, and Google+, we now have a new rich
source of data, which opens many research problems both from a so‐
cial science and quantitative/technical point of view.
We'll hear first about how one company, Morningside Analytics, vis‐
ualizes and finds meaning in social network data, as well as some of
the underlying theory of social networks. From there, we look at con‐
structing stories that can be told from social network data, which is a
form of data journalism. Thinking of the data scientist profiles—and
in this case, gene expression is an appropriate analogy—the mix of
math, stats, communication, visualization, and programming re‐
quired to do either data science or data journalism is slightly different,
but the fundamental skills are the same. At the heart of both is the
ability to ask good questions, to answer them with data, and to com‐
municate one's findings. To that end, we'll hear briefly about data
journalism from the perspective of Jon Bruner, an editor at O'Reilly.
