Computational Text Analysis 

Books and server hall.

Computational analysis offers new ways to derive meaning from text. Our project "Mining for Meaning: The Dynamics of Public Discourse on Migration" uses large corpora of text as social sensors to measure what people feel, think, and talk about, which allows us to track the emergence of shared social understandings.

At IAS we have established a research environment committed to the study of the dynamics of public discourse based on large-scale computational text analysis. Generously funded by the Swedish Research Council (grant 2018-05170), the project joins computational text analysis specialists focused on methodological innovation with social scientists attuned to social and political dynamics. This makes IAS a unique place for deriving insights into the evolution of political discourse through the use of theoretically driven computational innovations.

In the wake of digitization, social-media platforms, publicly accessible newspaper repositories, and machine-readable political documents, have become digital archives of public sentiment. Our international and interdisciplinary group---with researchers from Linköping University, Aalto University, the University of Lucerne, and the Institut Politechnique de Paris---analyzes such data to mine for the meaning embedded in these texts.

We study the discourse dynamics between politicians, media, and the public to understand meaning making, explore shifts in sentiment, and follow how collectively agreed narratives arise. In particular, we seek to understand the emergence and transformation of the public debate on immigration in Sweden. Our research will provide answers to questions such as:

  • How do public discourses on immigration form and change over time?
  • What part do social media, traditional media, and political parties play and how do they interact with each other?
  • How does opinion polarization occur online and how does misinformation spread?
  • How do discourse dynamics in local contexts (e.g. neighborhoods) interact with real-world events (e.g. crime) and social outcomes (e.g. residential segregation)?

Our aim is to understand the processes by which issues surrounding migration and integration are framed by different social groups, media outlets, and political parties, which we hope allows for evidence-based policy recommendations that tackle key challenges of integration.

Read more: Who is setting the discourse on immigration

Project time: January 2019 - open end

Our international team of dedicated text analysts come together for a kick-off workshop in March 2019.


Activities and output

Diffusion of a false conservative-leaning message in an integrated (left) or segregated (right) experimental network.

Online echo chambers favour the spreading of misinformation

We created 16 independent online ecosystems in which participants could share true and false messages about society, science, politics. Results reveal that partisan sorting systematically undermines the veracity of information circulating.

Server room.

The Computational Turn in Sociology

Analytical sociologists are harnessing troves of digitized text, digital trace, and social network data—along with the computational tools for their analysis—to answer sociology’s core questions in novel and rigorous ways.

Image illustrating computer programming with a number of different symbols.

From Documents to Data: A Framework for Total Corpus Quality

As large corpora of digitized text become increasingly available, sociologists are rediscovering the potential of text data for inquiries into social and cultural phenomena.

Funders and partners