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Apa kabar ? Extracting sociological data from masses of Indonesian newspaper clippings

Apa kabar ? Extracting sociological data from masses of Indonesian newspaper clippings. What if?. In 4th largest nation in the world… … with its chaotic bureaucracy… … but its vibrant press…

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Apa kabar ? Extracting sociological data from masses of Indonesian newspaper clippings

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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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