Estimating tie strength in follower networks to measure brand perceptions

Abstract

As public entities like brands and politicians increasingly rely on social media to engage their constituents, analyzing who follows them can reveal information about how they are perceived. Whereas most prior work considers following networks as unweighted directed graphs, in this paper we use a tie strength model to place weights on follow links to estimate the strength of relationship between users. We use conversational signals (retweets, mentions) as a proxy class label for a binary classification problem, using social and linguistic features to estimate tie strength. We then apply this approach to a case study estimating how brands are perceived with respect to certain issues (e.g., how environmentally friendly is Patagonia perceived to be?). We compute weighted follower overlap scores to measure the similarity between brands and exemplar accounts (e.g., environmental non-profits), finding that the tie strength scores can provide more nuanced estimates of consumer perception.