  We extract the individual names of persons mentioned in news reports from a
Philippine-based daily in the English language from 2010-2012. Names are
extracted using a learning algorithm that filters adjacent capitalized words
and runs it through a database of non-names grown through training. The number
of mentions of individual names shows strong temporal fluctuations, indicative
of the nature of "hot" trends and issues in society. Despite these strong
variations, however, we observe stable rank-frequency distributions across
different years in the form of power-laws with scaling exponents \alpha = 0.7,
reminiscent of the Zipf's law observed in lexical (i.e. non-name) words.
Additionally, we observe that the adjusted frequency for each rank, or the
frequency divided by the number of unique names having the same rank, shows a
distribution with dual scaling behavior, with the higher-ranked names
preserving the \alpha exponent and the lower-ranked ones showing a power-law
exponent \alpha' = 2.9. We reproduced the results using a model wherein the
names are taken from a Barabasi-Albert network representing the social
structure of the system. These results suggest that names, which represent
individuals in the society, are archived differently from regular words.
