Quantifying collective identity online from self-defining hashtags.

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.

Historical language records reveal a surge of cognitive distortions in recent decades.

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.

Individuals, institutions, and innovation in the debates of the French Revolution.

The French Revolution brought principles of "liberty, equality, fraternity" to bear on the day-to-day challenges of governing what was then the largest country in Europe. Its experiments provided a model for future revolutions and democracies across the globe, but this first modern revolution had no model to follow. Using reconstructed transcripts of debates held in the Revolution's first parliament, we present a quantitative analysis of how this body managed innovation. We use information theory to track the creation, transmission, and destruction of word-use patterns across over 40,000 speeches and a thousand speakers. The parliament as a whole was biased toward the adoption of new patterns, but speakers' individual qualities could break these overall trends. Speakers on the left innovated at higher rates, while speakers on the right acted to preserve prior patterns. Key players such as Robespierre (on the left) and Abbé Maury (on the right) played information-processing roles emblematic of their politics. Newly created organizational functions-such as the Assembly president and committee chairs-had significant effects on debate outcomes, and a distinct transition appears midway through the parliament when committees, external to the debate process, gained new powers to "propose and dispose." Taken together, these quantitative results align with existing qualitative interpretations, but also reveal crucial information-processing dynamics that have hitherto been overlooked. Great orators had the public's attention, but deputies (mostly on the political left) who mastered the committee system gained new powers to shape revolutionary legislation.