Social media analytics in museums: extracting expressions of inspiration

Museums have a remit to inspire visitors. However, inspiration is a complex, subjective construct and analyses of inspiration are often laborious. Increased use of social media by museums and visitors may provide new opportunities to collect evidence of inspiration more efficiently. This research investigates the feasibility of a system based on knowledge patterns from FrameNet – a lexicon structured around models of typical experiences – to extract expressions of inspiration from social media. The study balanced interpretation of inspiration by museum staff and computational processing of Twitter data. This balance was achieved by using prototype tools to change a museum’s Information Systems in ways that both enabled the potential of new, social-media-based information sources to be assessed, and which caused the museum staff to reflect upon the nature of inspiration and its role in the relationships between the museum and its visitors. The prototype tools collected and helped analyse Twitter data related to two events. Working with museum experts, the value of finding expressions of inspiration in Tweets was explored and an evaluation using annotated content achieved an F-measure of 0.46, indicating that social media may have some potential as a source of valuable information for museums, though this depends heavily upon how annotation exercises are conducted. These findings are discussed along with the wider implications of the role of social media in museums.