  Predicting the flow of information in dynamic social environments is relevant
to many areas of the contemporary society, from disseminating health care
messages to meme tracking. While predicting the growth of information cascades
has been successfully addressed in diverse social platforms, predicting the
temporal and topological structure of information cascades has seen limited
exploration. However, accurately predicting how many users will transmit the
message of a particular user and at what time is paramount for designing
practical intervention techniques.
  This paper leverages Long-Short Term Memory (LSTM) neural network techniques
to predict two spatio-temporal properties of information cascades, namely the
size and speed of individual-level information transmissions. We combine these
prediction algorithms with probabilistic generation of cascade trees into a
generative test model that is able to accurately generate cascade trees in two
different platforms, Reddit and Github. Our approach leads to a classification
accuracy of over 73% for information transmitters and 83% for early
transmitters in a variety of social platforms.
