  Internet users have been exposing an increasing amount of Personally
Identifiable Information (PII) on social media. Such exposed PII can cause
severe losses to the users, and informing users of their PII exposure is
crucial to raise their privacy awareness and encourage them to take protective
measures. To this end, advanced automatic techniques are needed. While
Information Extraction (IE) techniques can be used to extract the PII
automatically, Deep Learning (DL)-based IE models alleviate the need for
feature engineering and further improve the efficiency. However, DL-based IE
models often require large-scale labeled data for training, but PII-labeled
social media posts are difficult to obtain due to privacy concerns. Also, these
models rely heavily on pre-trained word embeddings, while PII in social media
often varies in forms and thus has no fixed representations in pre-trained word
embeddings. In this study, we propose the Deep Transfer Learning for PII
Extraction (DTL-PIIE) framework to address these two limitations. DTL-PIIE
transfers knowledge learned from publicly available PII data to social media to
address the problem of rare PII-labeled data. Moreover, our framework leverages
Graph Convolutional Networks (GCNs) to incorporate syntactic patterns to guide
PIIE without relying on pre-trained word embeddings. Evaluation against
benchmark IE models indicates that our approach outperforms state-of-the-art
DL-based IE models. Our framework can facilitate various applications, such as
PII misuse prediction and privacy risk assessment, protecting the privacy of
internet users.
