  The concern regarding users' data privacy has risen to its highest level due
to the massive increase in communication platforms, social networking sites,
and greater users' participation in online public discourse. An increasing
number of people exchange private information via emails, text messages, and
social media without being aware of the risks and implications. Researchers in
the field of Natural Language Processing (NLP) have concentrated on creating
tools and strategies to identify, categorize, and sanitize private information
in text data since a substantial amount of data is exchanged in textual form.
However, most of the detection methods solely rely on the existence of
pre-identified keywords in the text and disregard the inference of the
underlying meaning of the utterance in a specific context. Hence, in some
situations, these tools and algorithms fail to detect disclosure, or the
produced results are miss-classified. In this paper, we propose a multi-input,
multi-output hybrid neural network which utilizes transfer-learning,
linguistics, and metadata to learn the hidden patterns. Our goal is to better
classify disclosure/non-disclosure content in terms of the context of
situation. We trained and evaluated our model on a human-annotated ground truth
dataset, containing a total of 5,400 tweets. The results show that the proposed
model was able to identify privacy disclosure through tweets with an accuracy
of 77.4% while classifying the information type of those tweets with an
impressive accuracy of 99%, by jointly learning for two separate tasks.
