Linguistic phenomena, such as clusters of related words, disseminate through social networks at different rates but most diffusion models focus on the discrete adoption of new linguistic phenomena (i.e. new topics or memes). It is possible much of linguistic diffusion happens via the changing rates of existing word categories or concepts (those that are already regularly being used) rather than new ones. In this study we introduce a new metric, contrastive lexical diffusion (CLD) coefficient, which attempts to measure the degree to which ordinary language (here clusters of common words) catch on over friendship connections over time. For instance topics related to meeting and job are found to be sticky, while negative thinking and emotion, and global events, like `school orientation’ were found to be less sticky even though they change rates over time. We evaluate CLC coefficient over both quantitative and qualitative tests, finding they predict the spread of tweets and friendship connections, they converge with human judgments of lexical diffusion (r=0.92), and they replicate across disjoint networks (r=0.85). Comparing CLD scores can help understand lexical diffusion: positive emotion words appear more diffusive than negative emotions, first-person plurals (we) score higher than other pronouns, and numbers and time appear less diffusive.