SignificanceHow do democracies make decisions? We can read transcripts from parliament houses and legislative halls to see how particular ideas are introduced and debated, but we understand very little about the general principles of how these systems deal with information, or the origins of those principles. Here we study the parliamentary assembly of the first 2 years of the French Revolution, a model for democracies and revolutions across the globe, and show how patterns of speaking are created, picked up, and ignored or propagated. Political ideology, top–down rules, and individual charisma all affect how word patterns survive and thrive or, conversely, disappear and drop away.

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Individuals with depression are prone to maladaptive patterns of thinking, known as cognitive distortions, whereby they think about themselves, the world, and the future in overly negative and inaccurate ways. These distortions are associated with marked changes in an individual’s mood, behavior, and language. We hypothesize that societies can undergo similar changes in their collective psychology that are reflected in historical records of language use. Here, we investigate the prevalence of textual markers of cognitive distortions in over 14 million books for the past 125 y and observe a surge of their prevalence since the 1980s, to levels exceeding those of the Great Depression and both World Wars. This pattern does not seem to be driven by changes in word meaning, publishing and writing standards, or the Google Books sample. Our results suggest a recent societal shift toward language associated with cognitive distortions and internalizing disorders.

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Mass communication over social media can drive rapid changes in our sense of collective identity. Hashtags in particular have acted as powerful social coordinators, playing a key role in organizing social movements like the Gezi park protests, Occupy Wall Street, #metoo, and #blacklivesmatter. Here we quantify collective identity from the use of hashtags as self-labels in over 85,000 actively-maintained Twitter user profiles spanning 2017–2019. Collective identities emerge from a graph model of individuals’ overlapping self-labels, producing a hierarchy of graph clusters. Each cluster is bound together and characterized semantically by specific hashtags key to its formation. We define and apply two information-theoretic measures to quantify the strength of identities in the hierarchy. First we measure collective identity coherence to determine how integrated any identity is from local to global scales. Second, we consider the conspicuousness of any identity given its vocabulary versus the global identity map. Our work reveals a rich landscape of online identity emerging from the hierarchical alignment of uncoordinated self-labeling actions.