  Controversial content refers to any content that attracts both positive and
negative feedback. Its automatic identification, especially on social media, is
a challenging task as it should be done on a large number of continuously
evolving posts, covering a large variety of topics. Most of the existing
approaches rely on the graph structure of a topic-discussion and/or the content
of messages. This paper proposes a controversy detection approach based on both
graph structure of a discussion and text features. Our proposed approach relies
on Graph Neural Network (gnn) to encode the graph representation (including its
texts) in an embedding vector before performing a graph classification task.
The latter will classify the post as controversial or not. Two controversy
detection strategies are proposed. The first one is based on a hierarchical
graph representation learning. Graph user nodes are embedded hierarchically and
iteratively to compute the whole graph embedding vector. The second one is
based on the attention mechanism, which allows each user node to give more or
less importance to its neighbors when computing node embeddings. We conduct
experiments to evaluate our approach using different real-world datasets.
Conducted experiments show the positive impact of combining textual features
and structural information in terms of performance.
