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Extracting Sociological Trends from Indonesian Newspapers: Impact of Regime Changes

Explore elite network shifts during regime changes in Indonesia using digital humanities approach to analyze electronic newspaper archives from 1945 and 1998. Discover global patterns hidden in masses of data through network analysis, focusing on actors, polarity, authority, and cohesion over time. Interdisciplinary collaboration with a focus on societal influence and network dynamics.

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Extracting Sociological Trends from Indonesian Newspapers: Impact of Regime Changes

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  1. Apakabar? Extracting sociological data from masses of Indonesian newspaper clippings

  2. What if? • In 4th largest nation in the world… • … with its chaotic bureaucracy… • … but its vibrant press… • we could extract vital sociological trends automatically from its dozens of digital/ digitised newspapers?

  3. Elite network shifts during regime changeA digital humanities approach to network analysis using Indonesian language electronic newspaper archives

  4. Regime change 1945 1965 1998

  5. Elite shifts • Old regime • Decays internally • Crumbles rapidly • Creative & destructive ferment • New ruling coalition

  6. Sociology and computers • Historical sociologists know a lot already • KITLV catalogue • 1945 - 457 titles on “Indonesian revolution” • 1965 - 643 titles on “PKI” (Indonesian Communist Party – destroyed after putsch) • 1998 - 1042 titles on “Reformasi” • But too many elite actors to follow – trees and forest • Can digital humanities show us global patterns? • Mountains of data, mathematical modeling, complexity

  7. Network • Node (vertex) • Individual • Organisation • Line (edge) • Undirected • Directed (polar) • Analysis • Polarity (friend/ foe) • Authority (no. incoming) • Small world (shortest dist.) • Cohesion, centrality, brokers, cliques…. over time • Etc etcetc!

  8. Normal elite networks

  9. Elite network shifts

  10. Digital humanities • Automatically read digital/ digitised Indonesian newspaper archives for 1945 and 1998 • KITLV, NIOD, KB, WayBackMachine • Extract names (individuals, organisations, places) • Build elite networks • Undirected (related if in same article) • Polar (sentiment analysis - friend/ foe) • Analyse networks - actor-centred, context-oriented

  11. Analytical boundaries • Who is “elite”? What is an elite “network”? • Positional, decisional, reputational, or relational? • Influence (foxes), or domination (lions)? • National, regional, or community? • What do newspapers (not) reveal about elite networks? • They “manufacture consent” (highly filtered) • Lots of discussion needed!

  12. Interdisciplinary • KITLV • NIOD • InformaticaUvA • Informatika ITB • DANS • Erasmus Studio, Erasmus U Rot

  13. Research objectives • Indonesian PhD – read newspapers, produce networks • Postdoc 1– interrogate networks sociologically • Postdoc 2– interrogate networks mathematically • Produce demonstrable prototype software • Basis for future collaboration

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