  Online conversation understanding is an important yet challenging NLP problem
which has many useful applications (e.g., hate speech detection). However,
online conversations typically unfold over a series of posts and replies to
those posts, forming a tree structure within which individual posts may refer
to semantic context from higher up the tree. Such semantic cross-referencing
makes it difficult to understand a single post by itself; yet considering the
entire conversation tree is not only difficult to scale but can also be
misleading as a single conversation may have several distinct threads or
points, not all of which are relevant to the post being considered. In this
paper, we propose a Graph-based Attentive Semantic COntext Modeling (GASCOM)
framework for online conversation understanding. Specifically, we design two
novel algorithms that utilise both the graph structure of the online
conversation as well as the semantic information from individual posts for
retrieving relevant context nodes from the whole conversation. We further
design a token-level multi-head graph attention mechanism to pay different
attentions to different tokens from different selected context utterances for
fine-grained conversation context modeling. Using this semantic conversational
context, we re-examine two well-studied problems: polarity prediction and hate
speech detection. Our proposed framework significantly outperforms
state-of-the-art methods on both tasks, improving macro-F1 scores by 4.5% for
polarity prediction and by 5% for hate speech detection. The GASCOM context
weights also enhance interpretability.
