Abstract: Time-based online media, such as video, has been growing in importance. Still, there is limited research on information retrieval of time-coded media content. This work elaborates on the idea of extracting feature characteristics from time-based online content by means of users' interactions analysis instead of analyzing the content itself. Accordingly, a time series of users’ activity in online media is constructed and shown to exhibit rich temporal dynamics. Additionally it is demonstrated that is also possible to detect characteristic patterns in collective activity while accessing time-based media. Pattern detection of collective activity, as well as feature extraction of the corresponding pattern, is achieved by means of a time series clustering approach. This is demonstrated with the proposed approach featuring information-rich videos. It is shown that the proposed probabilistic algorithm effectively detects distinct shapes of the users’ time series, predicting correctly popularity dynamics, as well as their scale characteristics.

Ioannis Karydis, Markos Avlonitis, Phivos Mylonas and Spyros Sioutas, “Detection and Feature Extraction of Collective Activity in Human-Computer Interaction” International Journal of Advanced Computer Science and Applications(IJACSA), 7(3), 2016. http://dx.doi.org/10.14569/IJACSA.2016.070308

@article{Karydis2016,
title = {Detection and Feature Extraction of Collective Activity in Human-Computer Interaction},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2016.070308},
url = {http://dx.doi.org/10.14569/IJACSA.2016.070308},
year = {2016},
publisher = {The Science and Information Organization},
volume = {7},
number = {3},
author = {Ioannis Karydis and Markos Avlonitis and Phivos Mylonas and Spyros Sioutas}
}