The statistical mechanics of Twitter communities

Hall, Gavin; Bialek, William

doi:10.1088/1742-5468/ab3af0

Cited by 6 publications

(11 citation statements)

References 70 publications

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“…In contrast with the MVM, this community contains multiple pivotal members but wide variation in the strength of their subspace eigenvalues. Twitter communities may be on average sensitive to the behavior a few individuals regardless of identity (17), but this individual-level variation suggests that collective behavior may be much more sensitive to a select Twitter users even within smaller communities (30). Going beyond the detailed few examples in Figure 4, we find large diversity within political institutions that highlights the important role of heterogeneity in social institutions, heterogeneity that is captured in the information geometry of minimal, maxent models.…”

Section: Pivotal Components In Societymentioning

confidence: 77%

“…California (CA) state legislatures (15), US federal legislatures (16), and communities on Twitter (17). Across these examples, we find large diversity: ranging from examples of median-like systems, with pivotal voters or components, to other examples in which no special component emerges.…”

Section: R a F Tmentioning

confidence: 99%

“…We explore other examples of social systems, including votes from US state high courts (14), the California State Assembly and Senate (15), the US federal legislature (16), and communities on Twitter (17). As with the previous two examples, we map behavior in these systems to binary form.…”

Section: Pivotal Components In Societymentioning

confidence: 99%

“…Twitter. We analyze one of the communities from the data considered in Ref (17). In this work, the authors divide the Twitter community into smaller subcommunities using the CNM algorithm (49).…”

Section: Spdr the Spdr Select Sector Indices Track The Standard Andmentioning

confidence: 99%

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Sensitivity of Collective Outcomes Identifies Pivotal Components

et al. 2019

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A social system is susceptible to perturbation when its collective properties depend sensitively on a few pivotal components. Using the information geometry of minimal models from statistical physics, we develop an approach to identify pivotal components to which coarse-grained, or aggregate, properties are sensitive. As an example, we introduce our approach on a reduced toy model with a median voter who always votes in the majority. The sensitivity of majority-minority divisions to changing voter behavior pinpoints the unique role of the median. More generally, the sensitivity identifies pivotal components that precisely determine collective outcomes generated by a complex network of interactions. Using perturbations to target pivotal components in the models, we analyze data sets from political voting, finance, and Twitter. Across these systems, we find remarkable variety, from systems dominated by a median-like component to those whose components behave more equally. In the context of political institutions such as courts or legislatures, our methodology can help describe how changes in voters map to new collective voting outcomes. For economic indices, differing system response reflects varying fiscal conditions across time. Thus, our information-geometric approach provides a principled, quantitative framework that may help assess the robustness of institutions to targeted perturbation and compare institutions with one another and across time.

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Section: Pivotal Components In Societymentioning

confidence: 77%

Section: R a F Tmentioning

confidence: 99%

Section: Pivotal Components In Societymentioning

confidence: 99%

Section: Spdr the Spdr Select Sector Indices Track The Standard Andmentioning

confidence: 99%

See 2 more Smart Citations

Sensitivity of Collective Outcomes Identifies Pivotal Components

et al. 2019

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“…Then, we perform a survey across multiple systems in society including examples of judicial voting across US state high courts [14], California (CA) state legislatures [15], US federal legislatures [16], and communities on Twitter [17]. Across these examples, we find large diversity ranging from examples of median-like systems, with pivotal components, to other examples in which no special component emerges.…”

Section: Introductionmentioning

confidence: 99%

Sensitivity of collective outcomes identifies pivotal components

Lee

Katz

Bommarito

et al. 2020

J. R. Soc. Interface.

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A social system is susceptible to perturbation when its collective properties depend sensitively on a few pivotal components. Using the information geometry of minimal models from statistical physics, we develop an approach to identify pivotal components to which coarse-grained, or aggregate, properties are sensitive. As an example, we introduce our approach on a reduced toy model with a median voter who always votes in the majority. The sensitivity of majority–minority divisions to changing voter behaviour pinpoints the unique role of the median. More generally, the sensitivity identifies pivotal components that precisely determine collective outcomes generated by a complex network of interactions. Using perturbations to target pivotal components in the models, we analyse datasets from political voting, finance and Twitter. Across these systems, we find remarkable variety, from systems dominated by a median-like component to those whose components behave more equally. In the context of political institutions such as courts or legislatures, our methodology can help describe how changes in voters map to new collective voting outcomes. For economic indices, differing system response reflects varying fiscal conditions across time. Thus, our information-geometric approach provides a principled, quantitative framework that may help assess the robustness of collective outcomes to targeted perturbation and compare social institutions, or even biological networks, with one another and across time.

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Ideology from topic mixture statistics: inference method and example application to carbon tax public opinion

Puelma Touzel,

Lachapelle

2024

Environ. Data Science

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Political opposition to fiscal climate policy, such as a carbon tax, typically appeals to fiscal conservative ideology. Here, we ask to what extent public opposition to the carbon tax in Canada is, in fact, ideological in origin. As an object of study, ideology is a latent belief structure over a set of issue topics—and in particular their relationships—as revealed through stated opinions. Ideology is thus amenable to a generative modeling approach within the text-as-data paradigm. We use the Structural Topic Model, which generates word content from a set of latent topics and mixture weights placed on them. We fit the model to open-ended survey responses of Canadians elaborating on their support of or opposition to a carbon tax, then use it to infer the set of mixture weights used by each response. We demonstrate this set, moreso than the observed word use, serves efficient discrimination of opposition from support, with near-perfect accuracy on held-out data. We then operationalize ideology as the empirical distribution of inferred topic mixture weights. We propose and use an evaluation of ideology-driven beliefs based on four statistics of this distribution capturing the specificity, variability, expressivity, and alignment of the underlying ideology. We find that the ideology behind responses from respondents who opposed the carbon tax is more specific and aligned, much less expressive, and of similar variability as compared with those who support the tax. We discuss the implications of our results for climate policy and of broad application of our approach in social science.

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The statistical mechanics of Twitter communities

Cited by 6 publications

References 70 publications

Sensitivity of Collective Outcomes Identifies Pivotal Components

Sensitivity of Collective Outcomes Identifies Pivotal Components

Sensitivity of collective outcomes identifies pivotal components

Ideology from topic mixture statistics: inference method and example application to carbon tax public opinion

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