Where do prescient ideas—those that initially challenge conventional assumptions but later achieve widespread acceptance—come from? Although their outcomes in the form of technical innovation are readily observed, the underlying ideas that eventually change the world are often obscured. Here we develop a novel method that uses deep learning to unearth the markers of prescient ideas from the language used by individuals and groups. Our language-based measure identifies prescient actors and documents that prevailing methods would fail to detect. Applying our model to corpora spanning the disparate worlds of politics, law, and business, we demonstrate that it reliably detects prescient ideas in each domain. Moreover, counter to many prevailing intuitions, prescient ideas emanate from each domain's periphery rather than its core. These findings suggest that the propensity to generate far-sighted ideas may be as much a property of contexts as of individuals.

Which groups are most likely to become visionaries that define the future of their field? Because vision is difficult to measure, prior work has reached conflicting conclusions: one perspective emphasizes the benefits of being large, established, and central, while another stresses the value of being small, upstart, and peripheral. We propose that this tension can be resolved by disentangling vision---the capacity to generate contextually novel ideas that foretell the future of a field---from the traces of vision that result in tangible innovation. Using Bidirectional Encoder Representations from Transformers (BERT), we develop a novel method to identify the visionaries in a field from conversational text data. Applying this method to a corpus of over 100,000 quarterly earnings calls conducted by 6,000 firms from 2011 to 2016, we develop a measure---prescience---that identifies novel ideas which later become commonplace. Prescience is predictive of firms’ stock market returns: A one standard deviation increase in prescience is associated with a 4% increase in annual returns, and firms exhibiting especially high levels of prescience (above the 95th percentile) reap especially high returns. Moreover, contrary to theories of incumbent advantage, we find that small firms are more likely to possess prescience than large firms. The method we develop can be readily extended to other domains to identify visionary individuals and groups based on the language they use rather than the artifacts they produce.

Conspiracies are consequential and social, yet online conspiracy groups that consist of individuals (and bots) seeking to explain events or a system have been neglected in sociology. We extract conspiracy talk about the COVID-19 pandemic on Twitter and use the biterm topic model (BTM) to provide a descriptive baseline for the discursive and social structure of online conspiracy groups. We find that individuals enter these communities through a gateway conspiracy theory before proceeding to extreme theories, and humans adopt more diverse conspiracy theories than do bots. Event-history analyses show that individuals tweet new conspiracy theories, and tweet inconsistent theories simultaneously, when they face a threat posed by a rising COVID-19 case rate and receive attention from others via retweets. By contrast, bots are less responsive to rising case rates, but they are more consistent, as they mainly tweet about how COVID-19 was deliberately created by sinister agents. These findings suggest human beings are bricoleurs who use conspiracy theories to make sense of COVID-19, whereas bots are designed to create moral panic. Our findings suggest that conspiracy talk by individuals is defensive in nature, whereas bots engage in offense.

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