Controversy Detection: a Text and Graph Neural Network Based Approach

• URL: http://arxiv.org/abs/2112.11445v1
• Date: Fri, 3 Dec 2021 09:06:46 GMT
• Title: Controversy Detection: a Text and Graph Neural Network Based Approach
• Authors: Samy Benslimane (ADVANSE, LIRMM), J\'erome Az\'e (ADVANSE, LIRMM), Sandra Bringay (UPVM, ADVANSE, LIRMM), Maximilien Servajean (LIRMM, ADVANSE, UPVM), Caroline Mollevi
• Abstract summary: Controversial content refers to any content that attracts both positive and negative feedback. 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.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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