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Social media accounts post increasingly similar content, creating a chaoticexperience across platforms, which makes accessing desired informationdifficult. These posts can be organized by categorizing and grouping duplicatesacross social handles and accounts. There can be more than one duplicate of apost, however, a conventional Siamese neural network only considers a pair ofinputs for duplicate text detection. In this paper, we first propose amultiple-input Siamese network, MultiSiam. This condensed network is then usedto propose another model, SMCD (Social Media Classification and DuplicationModel) to perform both duplicate text grouping and categorization. TheMultiSiam network, just like the Siamese, can be used in multiple applicationsby changing the sub-network appropriately.

MultiSiam: A Multiple Input Siamese Network For Social Media Text Classification And Duplicate Text Detection