Discovering similar Twitter accounts using semantics. (May 2016)

• Record Type: Journal Article
• Authors: Razis, Gerasimos; Anagnostopoulos, Ioannis
• Is Part Of: Engineering applications of artificial intelligence. Volume 51(2016:Mar.)
• Page Start: 37
• Page End: 49
• Publication Date: 2016-05
• Subjects: Similarity network -- Social semantics -- Twitter entities; Engineering -- Data processing -- Periodicals; Artificial intelligence -- Periodicals; Expert systems (Computer science) -- Periodicals; Ingénierie -- Informatique -- Périodiques; Intelligence artificielle -- Périodiques; Systèmes experts (Informatique) -- Périodiques; Artificial intelligence; Engineering -- Data processing; Expert systems (Computer science); Periodicals; 620.00285
• DOI: 10.1016/j.engappai.2016.01.015
• Languages: English
• ISSNs: 0952-1976

Abstract

Abstract: On daily basis, millions of Twitter accounts post a vast number of tweets including numerous Twitter entities (mentions, replies, hashtags, photos, URLs). Many of these entities are used in common by many accounts. The more common entities are found in the messages of two different accounts, the more similar, in terms of content or interest, they tend to be. Towards this direction, we introduce a methodology for discovering and suggesting similar Twitter accounts, based entirely on their disseminated content in terms of Twitter entities used. The methodology is based exclusively on semantic representation protocols and related technologies. An ontological schema is also described towards the semantification of the Twitter accounts and their entities.