Question: How is order of binomials tracked across time?
Evidence: The present work: Binomials in large-scale online text. In this work, we use data from large-scale Internet text corpora to study binomials at a massive scale, drawing on text created by millions of users. Our approach is more wholesale than prior work - we focus on all binomials of sufficient frequency, without first restricting to small samples of binomials that might be frozen. We draw our data from news publications, wine reviews, and Reddit, which in addition to large volume, also let us characterize binomials in new ways, and analyze differences in binomial orderings across communities and over time. Furthermore, the subject matter on Reddit leads to many lists about people and organizations that lets us study orderings of proper names — a key setting for word ordering which has been difficult to study by other means.
We also address temporal and community structure in collections of binomials. While it has been recognized that the orderings of binomials may change over time or between communities BIBREF5, BIBREF10, BIBREF1, BIBREF13, BIBREF14, BIBREF15, there has been little analysis of this change. We develop new metrics for the agreement of binomial orderings across communities and the movement of binomial orderings over time. Using subreddits as communities, these metrics reveal variations in orderings, some of which suggest cultural change influencing language. For example, in one community, we find that over a period of 10 years, the binomial `son and daughter' went from nearly frozen to appearing in that order only 64% of the time.
While these changes do happen, they are generally quite rare. Most binomials — frozen or not — are ordered in one way about the same percentage of the time, regardless of community or the year. We develop a null model to determine how much variation in binomial orderings we might expect across communities and across time, if binomial orderings were randomly ordered according to global asymmetry values. We find that there is less variation across time and communities in the data compared to this model, implying that binomial orderings are indeed remarkably stable.
 We draw our data from news publications, wine reviews, and Reddit, which in addition to large volume, also let us characterize binomials in new ways, and analyze differences in binomial orderings across communities and over time. 
We develop new metrics for the agreement of binomial orderings across communities and the movement of binomial orderings over time. Using subreddits as communities, these metrics reveal variations in orderings, some of which suggest cultural change influencing language.
We develop a null model to determine how much variation in binomial orderings we might expect across communities and across time, if binomial orderings were randomly ordered according to global asymmetry values. 
Answer: The answers are shown as follows:
* draw our data from news publications, wine reviews, and Reddit
* develop new metrics for the agreement of binomial orderings across communities and the movement of binomial orderings over time
*  develop a null model to determine how much variation in binomial orderings we might expect across communities and across time