1. The Opinion Evaluation Network Nikos Korfiatis Computer Technology Institute (CTI) University of Patras, Greece & Royal Institute of Technology (KTH), Stockholm

2. Presentation in the PhD Summer School / Prolearn My FOAF profile Ambjorn Naeve (Royal Institute of Technology, Stockholm) and Miltiades Lytras (University of Patras,Greece) Doing my master thesis on Interactive Systems Engineering (KTH, Stockholm) Soon to start my PhD on Computational Sociology and Semantic Web

3. Presentation in the PhD Summer School / Prolearn Computational Sociology JABW ( Just another Buzzword…) synonymous with Social Network Analysis but includes analytical simulation and experimental techniques over network data The development and evaluation of formal models on information acquired from social activities expressed via the use of information enhanced means Grounded on 1930’s by Jacob Moreno initially as a part of Experimental and Social Psychology

4. Presentation in the PhD Summer School / Prolearn Aspects of Computational Sociology Network Models  Clique Formation  Prestige and Centrality  Structural Equivalence Social Simulation  Micro and Macro Analytical Models for Simulation of Agents

5. Presentation in the PhD Summer School / Prolearn Background Hypothesis Social actors are independent proactive entities and the amount of social relationship among them has important consequences for every individual Social actors are independent proactive entities and the amount of social relationship among them has important consequences for every individual Famous Social Network Cases  The Six Degrees of Separation (also a famous movie). Derives from Miligram’s Experiment in 1970’s  The Case of Social Capacity (The magic number 150…)  Epidemics Research (with application to computer viruses)

6. Presentation in the PhD Summer School / Prolearn Information Overload and Filtering Too much Information  Learning Resources  Different Skills Context must be taken into account Effectiveness of user modeling and recommender systems

7. Presentation in the PhD Summer School / Prolearn Slashdot.org An already deployed opinion evaluation system My “Karma” reflects my prestige

8. Presentation in the PhD Summer School / Prolearn Opinion Evaluation Social Recommendation of what resource is useful for your current context of use and current needs But…. Also who is able to provide you a concrete answer on your problems (for instance a continuous competency analysis map) Provide a way to link people, interests, with values and artifacts (Books, Decisions but also Learning Objects)

9. Presentation in the PhD Summer School / Prolearn Methodology Computational Sociology (CS)  Social Network Analysis Centrality and Prestige Cliques and Cohesive Subgroups Flows and Cycles on Social Graphs  Social Simulation Predict how social structures are evolved Evaluate Use Cases and Activities Semantic Web  An Input for CS models ( Data )  FOAF Interactions  Provide meaning of Social Interactions

10. Presentation in the PhD Summer School / Prolearn Interdisciplinary Research Customer Behavior (Marketing)  Rater Agreement Statistics A class of statistical metrics that permits the use of alternative scales on evaluating the same research question Collaborative Filtering  Algorithms for finding common interests

11. Presentation in the PhD Summer School / Prolearn An Ontology of Social Values Let the raters agree on their rating scales (Rater Agreement Statistics)  Bipolar Scales (-1,1)  Likert Scales (-2,-1,0,1,2)  Etc,.. Let the raters publish their social values which are reflected using a rating scale (I like a book, I don’t like it) Deploy and built the mechanisms and the formal models to combine these values using a unified ontology Provide recommendations and deductions based on these models

12. Presentation in the PhD Summer School / Prolearn What can our research contribute to Prolearn A tool for evaluation and customization of learning objects based on social context  Context Information is difficult to be acquired User Modeling systems usually do not capture context ( eg. Activities Running Simultaneously and in asynchronous mode) We rely on relationships instead of metadata  Prestige and Centrality of who-is-who (Selfish, Collaborative) A tool for acquisition and emergent evaluation of Competencies Integration with existing tools (Edutella, teleconferencing)