  Detecting events from social media data streams is gradually attracting
researchers. The innate challenge for detecting events is to extract
discriminative information from social media data thereby assigning the data
into different events. Due to the excessive diversity and high updating
frequency of social data, using supervised approaches to detect events from
social messages is hardly achieved. To this end, recent works explore learning
discriminative information from social messages by leveraging graph contrastive
learning (GCL) and embedding clustering in an unsupervised manner. However, two
intrinsic issues exist in benchmark methods: conventional GCL can only roughly
explore partial attributes, thereby insufficiently learning the discriminative
information of social messages; for benchmark methods, the learned embeddings
are clustered in the latent space by taking advantage of certain specific prior
knowledge, which conflicts with the principle of unsupervised learning
paradigm. In this paper, we propose a novel unsupervised social media event
detection method via hybrid graph contrastive learning and reinforced
incremental clustering (HCRC), which uses hybrid graph contrastive learning to
comprehensively learn semantic and structural discriminative information from
social messages and reinforced incremental clustering to perform efficient
clustering in a solidly unsupervised manner. We conduct comprehensive
experiments to evaluate HCRC on the Twitter and Maven datasets. The
experimental results demonstrate that our approach yields consistent
significant performance boosts. In traditional incremental setting,
semi-supervised incremental setting and solidly unsupervised setting, the model
performance has achieved maximum improvements of 53%, 45%, and 37%,
respectively.
