This document outlines a method for analyzing the dynamic semantic relatedness of terms through Twitter posts over time. It introduces the concept of Normalized Micropost Distance (NMD) as a measure of semantic similarity between terms based on the number of tweets containing each term individually and together. Example diagrams show how NMD can reveal changes in relatedness between terms like "ipad" and "sxsw" as well as "japan" and "nuclear" over a 5 day period. Potential applications are discussed along with plans for further improving and evaluating the approach.

1. Discovering the Dynamics of Terms’ Semantic Relatedness through Twitter Nikola Milikic, University of Belgrade, Serbia JelenaJovanovic, University of Belgrade, Serbia Milan Stankovic, STIH, Université Paris-Sorbonne, France

2. Outline Introduction Normalized Micropost Distance Scenarios of Use Example Diagrams – Tweet Dynamics Future Work

3. Introduction Micropost = Description of a Moment

4. Introduction Semantic Relatedness (SR) of terms is also a subject to temporal changes Mutual relationship change of terms is not directly evident from simple query results

5. Normalized Micropost Distance Normalized Micropost Distance (NMD) - semantic similarity measure derived from the number of microposts containing a given set of keywords Inspired by the Normalized Google Distance (NGD) Google search results vs.Microposts

6. Normalized Micropost Distance NMD formula x, y – terms f(x)t , f(y)t – number of microposts for x and y f(x, y)t– number of microposts containing both x and y t – time interval

7. Normalized Micropost Distance Detecting the significance of change - standard deviation of NMDs

8. Scenarios of Use Adapting Online Advertising Campaigns to the Changes in Term Relatedness Example: ‘sxsw’ and ‘ipad’ Facilitating Discovery of Relevant Resources in Organizations harmonizing the official and the actual vocabularies within an organization

9. Example Diagrams Tweet Dynamics – demo application NMD diagram for terms 'ipad' and 'sxsw' for the 5 days period

10. Example Diagrams Tweet Dynamics – demo application NMD diagram for terms ‘japan' and ‘nuclear' for the 5 days period

11. Future Work work in progress detection of good candidate term pairs computational efficiency and the limits of Twitter API comprehensive evaluation test on the mass amount of data compare to other approaches Google Correlate

12. Questions? Nikola Milikic @milikicn http://nikola.milikic.info