Using the hash tag histogram and social kinematics for semantic clustering in social media

Abstract

This work addresses automated semantic clustering of twitter users by analysis of their aggregated text posts (tweets). This semantic clustering of text is an application of a theory we refer to as Social Kinematics. Social Kinematics is a term coined by our team to refer to the field-theoretic approach we develop and describe in [1–3, 5]. It is used here to model human interaction in social media. This social modeling technique regards social media users as field sources, and uses the Laplacian to model their interaction. This yields a natural analogy with physical kinematics. Automation is described that allows social media text posts (organized by author into “threads”) to self-organize as a precursor to analysis and characterization. The goal of this work is to automate the characterization of user-generated text content in terms of its semantics (meaning). Characterization here means the determination of intuitive “categories” for content, and the automatic assignment of user-generated content to these categories. Categories might include: Advertising, Subscribed feeds (news, weather, traffic, etc.), Discussion of current events (politics, sports, popular culture, etc.), and Casual conversation (filial, friend-to-friend, etc.) Characterization is performed by retrieving text posts by Twitter users; numericizing these using a field model; and clustering them by their semantics. An innovation is the application of the field model to semantic characterization of text. This is based upon the observation that user hash tags are a priori semantic tags, making expensive and brittle semantic mapping of the tweet text unnecessary.