MultiSiam: A Multiple Input Siamese Network For Social Media Text Classification And Duplicate Text Detection
This addresses the issue of chaotic information access on social media platforms by enabling better content organization, though it appears incremental as it builds upon existing Siamese network methods.
The paper tackles the problem of organizing similar social media content by proposing MultiSiam, a multiple-input Siamese network that extends beyond pairwise inputs for duplicate text detection, and uses it to build SMCD for both duplicate grouping and categorization, achieving unspecified performance improvements.
Social media accounts post increasingly similar content, creating a chaotic experience across platforms, which makes accessing desired information difficult. These posts can be organized by categorizing and grouping duplicates across social handles and accounts. There can be more than one duplicate of a post, however, a conventional Siamese neural network only considers a pair of inputs for duplicate text detection. In this paper, we first propose a multiple-input Siamese network, MultiSiam. This condensed network is then used to propose another model, SMCD (Social Media Classification and Duplication Model) to perform both duplicate text grouping and categorization. The MultiSiam network, just like the Siamese, can be used in multiple applications by changing the sub-network appropriately.