Tweeting about TV: Sharing television viewing experiences via social media message streams

Abstract

Through content analysis of messages posted on Twitter, we categorize the types of content into a matrix — attention, emotion, information, and opinion. We use this matrix to analyze televised political and entertainment programs, finding that different types of messages are salient for different types of programs, and that the frequencies of the types correspond with program content. Our analyses suggest that Twitter picks up where formal social television systems failed: people are using the tool to selectively seek others who have similar interests and communicate their thoughts synchronous with television viewing.