Finding trends and statistical patterns in name mentions in news

We extract the individual names of persons mentioned in news reportsfrom a Philippine-based daily in the English language from 2010-2012. Names areextracted using a learning algorithm that filters adjacent capitalized words and runs itthrough a database of non-names grown through training. The number of mentions ofindividual names shows strong temporal fluctuations, indicative of the nature of “hot”trends and issues in society. Despite these strong variations, however, we observe sta-ble rank-frequency distributions across different years in the form of power-laws withscaling exponents α = 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, orthe frequency divided by the number of unique names having the same rank, shows adistribution with dual scaling behavior, with the higher-ranked names preserving theα exponent and the lower-ranked ones showing a power-law exponent α′ = 2.9. Wereproduced the results using a model wherein the names are taken from a Barabasi-Albert network representing the social structure of the system. These results suggestthat names, which represent individuals in the society, are archived differently fromregular words.