We present results of an extensive analysis  of data set containing 6.5 million articles published in  New York Times. Using a cosine  similarity measure describing co-occurrences among  words in the articles we have constructed a directed network dependent on time-dependent  historical  framework  of the considered data set as well as on individual attractiveness of published papers.  We have estimated a total volume of all trees in the constructed network that were started on a given day. A large volume means that an article or group of articles published on this day launched long-living information threads. The maximum volume has been found for days related to the beginning of  Great Depression in October 1929 (Wall Street  Crash on Black Tuesday). A distribution of informational explosiveness  of individual articles has been also studied. The explosiveness has been defined as a ratio of number of articles that follow a given articles to a number of  articles that were followed by  this article. The distribution is close to a power law with a characteristic exponent 3.5.

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