  Bragging is a speech act employed with the goal of constructing a favorable
self-image through positive statements about oneself. It is widespread in daily
communication and especially popular in social media, where users aim to build
a positive image of their persona directly or indirectly. In this paper, we
present the first large scale study of bragging in computational linguistics,
building on previous research in linguistics and pragmatics. To facilitate
this, we introduce a new publicly available data set of tweets annotated for
bragging and their types. We empirically evaluate different transformer-based
models injected with linguistic information in (a) binary bragging
classification, i.e., if tweets contain bragging statements or not; and (b)
multi-class bragging type prediction including not bragging. Our results show
that our models can predict bragging with macro F1 up to 72.42 and 35.95 in the
binary and multi-class classification tasks respectively. Finally, we present
an extensive linguistic and error analysis of bragging prediction to guide
future research on this topic.
