  Social media has provided a platform for users to gather and share
information and stay updated with the news. Such networks also provide a
platform to users where they can engage in conversations. However, such
micro-blogging platforms like Twitter restricts the length of text. Due to
paucity of sufficient word occurrences in such posts, classification of this
information is a challenging task using standard tools of natural language
processing (NLP). Moreover, high complexity and dynamics of the posts in social
media makes text classification a challenging problem. However, considering
additional cues in the form of past labels and times associated with the post
can be potentially helpful for performing text classification in a better way.
To address this problem, we propose models based on the Hawkes process (HP)
which can naturally incorporate the temporal features and past labels along
with textual features for improving short text classification. In particular,
we propose a discriminative approach to model text in HP where the text
features parameterize the base intensity and/or the triggering kernel. Another
major contribution is to consider kernel to be a function of both time and
text, and further use a neural network to model the kernel. This enables
modelling and effectively learning the text along with the historical
influences for tweet classification. We demonstrate the advantages of the
proposed techniques on standard benchmarks for rumour stance classification.
