  News media has long been an ecosystem of creation, reproduction, and
critique, where news outlets report on current events and add commentary to
ongoing stories. Understanding the dynamics of news information creation and
dispersion is important to accurately ascribe credit to influential work and
understand how societal narratives develop. These dynamics can be modelled
through a combination of information-theoretic natural language processing and
networks; and can be parameterised using large quantities of textual data.
However, it is challenging to see "the wood for the trees", i.e., to detect
small but important flows of information in a sea of noise. Here we develop new
comparative techniques to estimate temporal information flow between pairs of
text producers. Using both simulated and real text data we compare the
reliability and sensitivity of methods for estimating textual information flow,
showing that a metric that normalises by local neighbourhood structure provides
a robust estimate of information flow in large networks. We apply this metric
to a large corpus of news organisations on Twitter and demonstrate its
usefulness in identifying influence within an information ecosystem, finding
that average information contribution to the network is not correlated with the
number of followers or the number of tweets. This suggests that small local
organisations and right-wing organisations which have lower average follower
counts still contribute significant information to the ecosystem. Further, the
methods are applied to smaller full-text datasets of specific news events
across news sites and Russian troll accounts on Twitter. The information flow
estimation reveals and quantifies features of how these events develop and the
role of groups of trolls in setting disinformation narratives.
