  In recent years we have witnessed an explosion of data collected for
different human dynamics, from email communication to social networks
activities. Extract useful information from these huge data sets represents a
major challenge. In the last decades, statistical regularities has been widely
observed in human activities and various models have been proposed. Here we
move from modeling to inference and propose a statistical framework capable to
predict global features of human activities from local knowledge. We consider
four data sets of human activities: email communication, Twitter posts,
Wikipedia articles and Gutenberg books. From the statistics of local
activities, such as sent emails per senders, post per hashtags and word
occurrences collected in a small sample of the considered dataset, we infer
global features, as the number of senders, hashtags and words at the global
scale. Our estimates are robust and accurate with a small relative error.
Moreover, we predict how abundance of a hashtag or of a word may change through
scales. Thus, observing a small portion of tweets and the popularity of a given
hashtag among them, we can estimate whether it will remain popular or not in
the unseen part of the network. Our approach is grounded on statistical ecology
as we discover inference of unseen human activity hallmarks can be mapped into
the unseen species problem in biodiversity. Our findings may have applications
to different areas, from resource management in emails to collective attention
monitoring in Twitter and to language learning process in word databases.
