A co-word machine?


Published on

A presentation by Noortje Marres and Carolin Gerlitz (Goldsmiths, University of London) on co-word analysis and developing a co-word machine.

Published in: Education, Technology
  • Be the first to comment

  • Be the first to like this

No Downloads
Total views
On SlideShare
From Embeds
Number of Embeds
Embeds 0
No embeds

No notes for slide

A co-word machine?

  1. 1. A Co-word Machine?Noortje Marres and Carolin Gerlitz(Goldsmiths, University of London)Co-Word Workshop, May 23-25 2012
  2. 2. Co-word analysis•  Network analysis of textual data:tracing relations between terms.•  Co-occurrence & weighting of wordpairs in each others’ vicinity (3, 4, 5words)•  Detect emergence of new significantthemes, and/or their fluctuations overtime (Callon et al, 1983; Danowski,2009)•  Methodological strategy to studyhappening content.•  Existing tools: Infomous; Wordij.
  3. 3. #1 Relevance vs. Co-word•  Critique of citation and relevanceanalysis (Small, 1973; Callon et al,1983).•  Tyranny of relevance and popularity.•  Results in performative re-production ofpopularity ranks.•  Static measure from the outside.•  Aggregation and one middle: creates aEuclidean space.•  Two-dimensional ordering: low or highrelevance.
  4. 4. #1 Relevance vs. Co-word•  Co-word analysis developed in 1980sas critique of citation analysis, buildingon co-citation analysis•  Focus on internal, context specificrelations: measures from the inside.•  Expands focus from currentlypredominant clusters to variations overtime.•  Allows to explore emergence, or‘pockets of innovation’ (Callon, 1983).•  Multi-dimensional, topological orderingacross various clusters.
  5. 5. #2 Post-social method•  Moving beyond a purely social (citation/mention/link based) ordering.•  Co-word analysis as post-social method:Gives consideration to interplay of actors,devices, issues and formats (Knorr-Cetina,1997; Latour and L’Epinay, 2008).•  Specific, context-based, from the inside.•  Deploys analytical capacities of devices,platforms and internal relations.
  6. 6. #2 Concerns: Datareduction•  How to reduce relations to settleupon significant ones (Kostoff, 2003)?•  Distributed analytical capacities.•  Indexing - the ‘indexer effect’Kostoff, 2003).•  Word-pair frequency (Danowski).•  Working with predefined terms:Word-grabber (Danowski).•  How to balance between reductionfor significance and liveliness?•  Machine-settings: weighting,distance, frequency, breadth, depth?
  7. 7. #2 Concerns: Platformdependency•  Post-social methods as drawing onanalytical capacities of devices.•  But: How can we deploy co-wordmethods to study issue relations ratherthan analysing platforms themselves?•  Strategies to reduce platformdependency whilst taking technicity ofcontent and medium-specificity intoaccount.•  Demarcation of source sets: notscrape platforms but issue-relatedsource sets. (deploying the ‘indexereffect’ to positive effect?)•  Machine-settings: source setdefinition.
  8. 8. #2 Concerns: Technicity ofcontent•  Co-word analysis as measurement frominside the medium.•  Focus on socio-epistemic metrics.•  Take medium-specificity into account: nodis-embedding of content from contextand medium (Niederer and van Dijck,2010).•  Define key research questions:New emerging terms?Heating up of terms over time?Other dynamics of issuefication?Fluctuation of terms?•  Possibilities for issue-profiling (detectingthe partisanship not only of actors but ofissues).
  9. 9. #3 Live research•  Analytic and empirical focus on variationover time.•  Specific response to ‘real-timeresearch’ (monitoring live media for currency).•  Shifts attention from currency & liveness toliveliness and happening content (Marres andWeltevrede, 2012).•  Live research: moves beyond mere in/decrease of popularity to changes of internalcomposition of co-word networks.
  10. 10. #3 concerns: Static vs. Dynamic•  Identification of both static, stable termsand emergent, variable ones.•  Static vs Dynamics as thematic of socialtheory (social order vs social change) - co-word was originally on the dynamic end ofspectrum.•  Happening of content as interplay betweenstatic vs. dynamic?•  Mapping static terms as definition of issuecontexts?•  Intervals: When do terms become static?•  Machine settings: search for emergent/stable, determine intervals for stability.
  11. 11. #3 concerns: Intervals•  To study variations over time, a specific formof longitudinal analysis need to be defined.•  Cross-sectional, interval-based, continuousaccount of time (Uprichard, 2012)?•  What forms of analysis do different temporalintervals allow for?•  How to visualise longitudinal data and how toaccount for non-linear dynamics?•  Machine setting: interval selection, outputformats, visualisation guidelines.
  12. 12. Summary•  Co-word analysis as tracking theformation and variation of issue-formingword-associations.•  Post-social method: Re-embedding issue-term analysis in context and medium.•  Live research exploring ‘happeningcontent’.•  Measurement from the inside.•  Challenges: data-reduction &determining significance, balancingintervals, considering static & dynamicterms and exploring modes ofvisualisation.
  13. 13. Thank you. n.marres@gold.ac.uk c.gerlitz@gold.ac.uk