94 Kellogg Insight and the environment). When the researchers compared their computer-generated results with traditional survey results for 239 brands, they found that, in most cases, the survey results closely matched the results produced by the algorithm. And in contrast to the sluggish process of administering surveys—or, for that matter, other automated tools that must be trained on “labeled data,” or data that are painstakingly hand-tagged with information about users or their messages—the algorithm responds more quickly to shifts in public perception. “Anytime we want to run this model, we can just query again, and if there are new players in the field—new, trendy sustainability exemplars—then we’ll catch them with the new query,” Cutler says. Discovering Emerging Trends and Hashtags The ability to identify these exemplar accounts, and quickly, can be use - ful for other purposes as well, like identifying trends as they emerge. Take hashtags. Because they can pack a lot of punch into a short charac - ter count, they have become particularly important for understanding a user’s perceptions and intent. As Cutler explains, a user might “give an opinion like, ‘I love days like this #EARTHDAY.’ And really, the hashtag is the only thing that’s giving you context about what you love.” But hashtags can change seemingly overnight. Nobody wants to build a model to track engagement for the hashtag #ECOFRIDAY when there is a good chance that users will abandon that hashtag and start using Based on research from Jennifer Cutler