发现新闻中名字提及的趋势和统计模式
Finding trends and statistical patterns in name mentions in news
摘要：
我们摘录了2010-2012年菲律宾一份英文日报新闻报道中提到的个人姓名。名字是使用一种学习算法提取的，该算法过滤相邻的大写单词，并将其通过训练生成的非名字数据库运行。提到个人名字的次数显示出强烈的时间波动，表明了社会“热点”趋势和问题的性质。尽管有这些强烈的变化，但是，我们观察到稳定的排名频率分布在不同的年份的幂律的形式与标度指数α= 0.7，让人想起齐普夫定律观察词汇（即非名称）的话。此外，我们观察到每个等级的调整频率，或频率除以具有相同等级的唯一名称的数量，显示具有双重标度行为的分布，其中排名较高的名称保持α指数，排名较低的名称显示幂律指数α' = 2.9。我们使用一个模型重现了结果，其中的名字取自代表系统社会结构的Barabasi-Albert网络。这些结果表明，代表社会中个人的名字与普通单词的存档方式不同。
英文摘要：
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 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, 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 αexponent and the lower-ranked ones showing a power-law exponent α' = 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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
