Extracting skill endorsements from personal communication data

Abstract

People are increasingly communicating and collaborating via digital platforms, such as email and messaging applications. Data exchanged on these digital communication platforms can be a treasure trove of information on people who participate in the discussions: who they are collaborating with, what they are working on, what their expertise is, and so on. Yet, personal communication data is very rarely analyzed due to the sensitivity of the information it contains. In this paper, we mine personal communication data with the goal of generating skill endorsements of the type "person A endorses person B on skill X." To address privacy concerns, we consider that each person has access only to their own data (i.e., conversations with their peers). By using our method, they can generate endorsements for their peers, which they can inspect and opt to publish. To identify meaningful skills we use a knowledge base created from the StackExchange q&a forum. We study two different approaches, one based on building a skillgraph, and one based on information retrieval techniques. We find that the latter approach outperforms the graph-based algorithms when tested on a dataset of user profiles from StackOverflow. We also conduct a user study on email data and find that the information retrieval-based approach achieves a MAP@10 score of 0.617.