The Flickr frequency norms: What 17 years of images tagged online tell us about lexical processing

Word frequency is one of the best predictors of language processing. Typically, word frequency norms are entirely based on natural-language text data, thus representing what the literature typically refers to as purely linguistic experience. This study presents Flickr frequency norms as a novel word frequency measure from a domain-specific corpus inherently tied to extra-linguistic information: words used as image tags on social media. To obtain Flickr frequency measures, we exploited the photo-sharing platform Flickr Image (containing billions of photos) and extracted the number of uploaded images tagged with each of the words considered in the lexicon. Here, we systematically examine the peculiarities of Flickr frequency norms and show that Flickr frequency is a hybrid metrics, lying at the intersection between language and visual experience and with specific biases induced by being based on image-focused social media. Moreover, regression analyses indicate that Flickr frequency captures additional information beyond what is already encoded in existing norms of linguistic, sensorimotor, and affective experience. Therefore, these new norms capture aspects of language usage that are missing from traditional frequency measures: a portion of language usage capturing the interplay between language and vision, which - this study demonstrates - has its own impact on word processing. The Flickr frequency norms are openly available on the Open Science Framework (https://osf.io/2zfs3/).