@inproceedings{d331be22a84a4152a2eec80124f4ca91,
title = "Engagement capacity and engaging team formation for reach maximization of online social media platforms",
abstract = "The challenges of assessing the {"}health{"} of online social media platforms and strategically growing them are recognized by many practitioners and researchers. For those platforms that primarily rely on user-generated content, the reach - the degree of participation referring to the percentage and involvement of users - is a key indicator of success. This paper lays a theoretical foundation for measuring engagement as a driver of reach that achieves growth via positive externality effects. The paper takes a game theoretic approach to quantifying engagement, viewing a platform's social capital as a cooperatively created value and finding a fair distribution of this value among the contributors. It introduces engagement capacity, a measure of the ability of users and user groups to engage peers, and formulates the Engaging Team Formation Problem (EngTFP) to identify the sets of users that {"}make a platform go{"}. We show how engagement capacity can be useful in characterizing forum user behavior and in the reach maximization efforts. We also stress how engagement analysis differs from influence measurement. Computational investigations with Twitter and Health Forum data reveal the properties of engagement capacity and the utility of EngTFP.",
keywords = "Engagement, Influence maximization, Reach, Social networks, Team formation problem",
author = "Alexander Nikolaev and Shounak Gore and Venu Govindaraju",
note = "Publisher Copyright: {\textcopyright} 2016 ACM.; 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD 2016 ; Conference date: 13-08-2016 Through 17-08-2016",
year = "2016",
month = aug,
day = "13",
doi = "10.1145/2939672.2939681",
language = "English",
series = "Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining",
publisher = "Association for Computing Machinery ",
pages = "225--234",
booktitle = "KDD 2016 - Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining",
address = "United States",
}