93 Kellogg Insight consumers are saying about their brands. Though Cutler believes text analysis has its place, there are serious drawbacks to relying on text alone. For example, although 20 percent of U.S. adults have Twitter accounts, fewer than half post actively. “Among those who write, very few are going to write about a brand, and even fewer still are going to write about your brand,” Cutler explains. But consumers reveal a lot about themselves online, even when they say nothing at all. These Twitter lurkers are following other users—companies, politi - cians, celebrities, friends—and making hand-curated lists of accounts organized by topic (“sports,” “science,” or “politics”). And unless they have made their Twitter account private, all of this information is pub - licly available. Across these many millions of user-curated lists, certain commonali - ties begin to emerge. @ESPN, for instance, might appear on many user lists labeled “sports.” Cutler’s algorithm identifies exemplary accounts for particular topics. It searches for accounts that appear on many lists labeled, for instance, “environment” and narrows those accounts down to the strongest exemplars, such as @SierraClub or @Greenpeace. The algorithm then looks for overlap between the followers of those exemplary accounts and the followers of a particular brand (say, Toyota Prius). This information is used to compute a score that shows how the brand is associated with the attribute. Lower scores mean most custom - ers do not associate the brand strongly with the attribute (say, Walmart and luxury); higher scores indicate a stronger association (Toyota Prius Based on research from Jennifer Cutler