1. 15 Sep 2015 EunJeong Cheon i501: introduction to informatics Semiotic Dynamics and Collaborative Tagging Ciro Cattuto, Vittorio Loreto, and Luciano Pietronero

2. Contents 01 02 Implication 03 Class Discussion 04 Collaborative Tagging Research Overview

3. Collaborative Tagging 01 ; Social classification, social indexing, social tagging, social bookmarking and folksonomy Allow users to annotate, categorize and share their web content using short textual labels called tags The main purpose is to loosely clas sify resources based on end-user’s feedback, expressed in the form of free- text labels (i.e., tags).

4. Research Overview 02 Research Question: How the ‘‘microscopic’’ tagging behavior of users causes the emergence of the general feature in collaborative tagging. (from the perspective of complex systems) Research Method: Statistical analysis on data from Del.icio.us and Connotea and investigate the statistical properties of tag association. Extracting the resources associated with a given tag X and studying the statistic al distribution of tags cooccurring with X Two studies; Cooccurrence analysis between high and low-rank tags Memory-Based Yule-Simon Model

5. Cooccurrence analysis between high and low-rank tags 02 More semantically general tags (e.g. blog) will tend to cooccur with a larger number of o ther tags.  Low-rank tags do not trivially cooccur wit h most of the low-frequency high- rank tags; in the direction of a nontrivial hierarchical org anization emerging out of the collective taggi ng activity, and all such hierarchies merging i nto the overall power-law tail.

6. 02 A Yule-Simon Model with Memory

7. Implication 03 … users of collaborative tagging systems share universal behaviors that, despite the intricacies of personal categorization, tagging procedures, and user interactions, appear to follow simple activity patterns

8. In-class Discussion 04 Considering collaborative tagging is collaborative intelligence or crowed sourcing, which area or works that human collaboration could be better than artificial intelligence over time? (ex. crowed-sourced translation) In terms of research methods and the proposed model, any critique or limitation? (ex. tagging behavior of ‘average user’ not consider user’s background knowledge sublinear vocabulary growth )

9. Thank you.