This document discusses theories of latent semantics and social interaction. It outlines latent semantic analysis (LSA) and social network analysis (SNA) as methods to analyze meaning and interactions. It proposes meaningful interaction analysis (MIA) as a technique that combines LSA and SNA to study associative closeness structures and social relations in latent semantic spaces. Examples of applying MIA to analyze forum postings, virtual meeting attendance, and blog subscriptions are provided.

1. December 7 th , 2010, Shanghai, China Latent Semantics and Social Interaction Fridolin Wild KMi, The Open University

2. Outline Context & Framing Theories Latent Semantic Analysis (LSA) (Social) Network Analysis (S/NA) Meaningful Interaction Analysis (MIA) Outlook: Analysing Practices

3. Context & Theories

4. Information what is Meaning could be the quality of a certain signal. Meaning could be a logical abstractor = a release mechanism. (96dpi) meaning

5. meaning is social To develop a shared understanding is a natural and necessary process, because language underspecifies meaning: future understanding builds on it Network effects make a network of shared understandings more valuable with growing size: allowing e.g. ‘distributed cognition’. At same time: linguistic relativity (Sapir-Whorf hypothesis): our own language culture restricts our thinking

7. Concepts & Competence things we can (not) construct from language Tying shoelaces Douglas Adams’ ‘meaning of liff’: Epping : The futile movements of forefingers and eyebrows used when failing to attract the attention of waiters and barmen. Shoeburyness : The vague uncomfortable feeling you get when sitting on a seat which is still warm from somebody else's bottom I have been convincingly Sapir-Whorfed by this book.

8. A “Semantic Community” LSA SNA Associative Closeness Concept (disambiguated term) Person Social relation

9. LSA

10. Latent Semantic Analysis Two-Mode factor analysis of the co-occurrences in the terminology Results in a latent-semantic vector space “ Humans learn word meanings and how to combine them into passage meaning through experience with ~paragraph unitized verbal environments.” “ They don’t remember all the separate words of a passage; they remember its overall gist or meaning.” “ LSA learns by ‘reading’ ~paragraph unitized texts that represent the environment.” “ It doesn’t remember all the separate words of a text it; it remembers its overall gist or meaning.” -- Landauer, 2007

11. latent-semantic space Singular values (factors, dims, …) Term Loadings Document Loadings

12. (Landauer, 2007)

13. Associative Closeness You need factor stability? > Project using fold-ins! Term 1 Document 1 Document 2 Angle 2 Angle 1 Y dimension X dimension

14. Example: Classic Landauer { M } = Deerwester, Dumais, Furnas, Landauer, and Harshman (1990): Indexing by Latent Semantic Analysis, In: Journal of the American Society for Information Science, 41(6):391-407 Only the red terms appear in more than one document, so strip the rest. term = feature vocabulary = ordered set of features TEXTMATRIX

15. Reconstructed, Reduced Matrix m4: Graph minors : A survey

16. doc2doc - similarities Unreduced = pure vector space model - Based on M = TSD’ - Pearson Correlation over document vectors reduced - based on M 2 = TS 2 D’ - Pearson Correlation over document vectors

17. (S)NA

18. Social Network Analysis Existing for a long time (term coined 1954) Basic idea: Actors and Relationships between them (e.g. Interactions) Actors can be people (groups, media, tags, …) Actors and Ties form a Graph (edges and nodes) Within that graph, certain structures can be investigated Betweenness, Degree of Centrality, Density, Cohesion Structural Patterns can be identified (e.g. the Troll)

19. Constructing a network from raw data forum postings incidence matrix IM adjacency matrix AM IM x IM T

20. Visualization: Sociogramme

21. Measuring Techniques (Sample) Degree Centrality number of (in/out) connections to others Closeness how close to all others Betweenness how often intermediary Components e.g. kmeans cluster (k=3)

22. Example: Joint virtual meeting attendance (Flashmeeting co-attendance in the Prolearn Network of Excellence)

23. Example: Subscription structures in a blogging network (2 nd trial of the iCamp project)

24. MIA

25. Meaningful Interaction Analysis (MIA) Combines latent semantics with the means of network analysis Allows for investigating associative closeness structures at the same time as social relations In latent-semantic spaces only or in spaces with additional and different (!) relations

26. The mathemagics behind Meaningful Interaction Analysis

27. Network Analysis

28. MIA of the classic Landauer

32. Applications

33. Capturing traces in text: medical student case report

34. Internal latent-semantic graph structure (MIA output)

36. Evaluating Chats with PolyCAFe

38. Conclusion

39. Conclusion Both LSA and SNA alone are not sufficient for a modern representation theory MIA provides one possible bridge between them It is a powerful technique And it is simple to use (in R)

40. #eof.

41. Contextualised Doc & Term Vectors T k = left-hand sided matrix = ‚term loadings‘ on the singular value D k = right-hand sided matrix = ‚document loadings‘ on the singular value Multiply them into same space V T = T k S k V D = D k T S k Cosine Closeness Matrix over ... = adjacency matrix = a graph More: e.g. add author vectors V A through cluster centroids or vector addition of their publication vectors Speed: use existing space and fold in e.g. author vectors latent-semantic space

42. Influencing Parameters (LSA) Pearson(eu, österreich) Pearson(jahr, wien)