  In this paper, we study news group modeling and forecasting methods using
quantitative data generated by our large-scale natural language processing
(NLP) text analysis system. A news group is a set of news entities, like top
U.S. cities, governors, senators, golfers, or movie actors. Our fame
distribution analysis of news groups shows that log-normal and power-law
distributions generally could describe news groups in many aspects. We use
several real news groups including cities, politicians, and CS professors, to
evaluate our news group models in terms of time series data distribution
analysis, group-fame probability analysis, and fame-changing analysis over long
time. We also build a practical news generation model using a HMM (Hidden
Markov Model) based approach. Most importantly, our analysis shows the future
entity fame distribution has a power-law tail. That is, only a small number of
news entities in a group could become famous in the future. Based on these
analysis we are able to answer some interesting forecasting problems - for
example, what is the future average fame (or maximum fame) of a specific news
group? And what is the probability that some news entity become very famous
within a certain future time range? We also give concrete examples to
illustrate our forecasting approaches.
