Generic process for extracting user profiles from social media using hierarchical knowledge bases

Citation

Große-Bölting G, Nishioka C & Scherp A (2015) Generic process for extracting user profiles from social media using hierarchical knowledge bases. In: Proceedings of the 2015 IEEE 9th International Conference on Semantic Computing. 2015 IEEE 9th International Conference on Semantic Computing (IEEE ICSC 2015), Anaheim, CA, USA, 07.02.2015-09.02.2015. Piscataway, NJ, USA: Institute of Electrical and Electronics Engineers, pp. 197-200. https://doi.org/10.1109/ICOSC.2015.7050806

Abstract
We present the design and application of a generic approach for semantic extraction of professional interests from social media using a hierarchical knowledge-base and spreading activation theory. By this, we can assess to which extend a user's social media life reflects his or her professional life. Detecting named entities related to professional interests is conducted by a taxonomy of terms in a particular domain. It can be assumed that one can freely obtain such a taxonomy for many professional fields including computer science, social sciences, economics, agriculture, medicine, and so on. In our experiments, we consider the domain of computer science and extract professional interests from a user's Twitter stream. We compare different spreading activation functions and metrics to assess the performance of the obtained results against evaluation data obtained from the professional publications of the Twitter users. Besides selected existing activation functions from the literature, we also introduce a new spreading activation function that normalises the activation w.r.t. to the outdegree of the concepts.

Keywords
Economics; Twitter; information services; electronic publishing; internet; labeling;

Journal
Proceedings of the 2015 IEEE 9th International Conference on Semantic Computing, IEEE ICSC 2015