  We present a method for generating synthetic versions of Twitter data using
neural generative models. The goal is protecting individuals in the source data
from stylometric re-identification attacks while still releasing data that
carries research value. Specifically, we generate tweet corpora that maintain
user-level word distributions by augmenting the neural language models with
user-specific components. We compare our approach to two standard text data
protection methods: redaction and iterative translation. We evaluate the three
methods on measures of risk and utility. We define risk following the
stylometric models of re-identification, and we define utility based on two
general word distribution measures and two common text analysis research tasks.
We find that neural models are able to significantly lower risk over previous
methods with little cost to utility. We also demonstrate that the neural models
allow data providers to actively control the risk-utility trade-off through
model tuning parameters. This work presents promising results for a new tool
addressing the problem of privacy for free text and sharing social media data
in a way that respects privacy and is ethically responsible.
