TY - GEN
T1 - Exploring Interactions in Social Networks for Influence Discovery
AU - Rakoczy, Monika Ewa
AU - Bouzeghoub, Amel
AU - Wegrzyn-Wolska, Katarzyna
AU - Gancarski, Alda Lopes
N1 - Publisher Copyright:
© 2019, Springer Nature Switzerland AG.
PY - 2019/1/1
Y1 - 2019/1/1
N2 - Today’s social networks allow users to react to new contents such as images, posts and messages in numerous ways. For example, a user, impressed by another user’s post, might react to it by liking it and then sharing it forward to her friends. Therefore, a successful estimation of the influence between users requires models to be expressive enough to fully describe various reactions. In this article, we aim to utilize those direct reactive activities, in order to calculate users impact on others. Hence, we propose a flexible method that considers type, quality, quantity and time of reactions and, as a result, the method assesses the influence dependencies within the social network. The experiments conducted using two different real-world datasets of Facebook and Pinterest show the adequacy and flexibility of the proposed model that is adaptive to data having different features.
AB - Today’s social networks allow users to react to new contents such as images, posts and messages in numerous ways. For example, a user, impressed by another user’s post, might react to it by liking it and then sharing it forward to her friends. Therefore, a successful estimation of the influence between users requires models to be expressive enough to fully describe various reactions. In this article, we aim to utilize those direct reactive activities, in order to calculate users impact on others. Hence, we propose a flexible method that considers type, quality, quantity and time of reactions and, as a result, the method assesses the influence dependencies within the social network. The experiments conducted using two different real-world datasets of Facebook and Pinterest show the adequacy and flexibility of the proposed model that is adaptive to data having different features.
KW - Influence
KW - Influencers
KW - Social network analysis
KW - Social scoring
U2 - 10.1007/978-3-030-20482-2_3
DO - 10.1007/978-3-030-20482-2_3
M3 - Conference contribution
AN - SCOPUS:85068128215
SN - 9783030204815
T3 - Lecture Notes in Business Information Processing
SP - 23
EP - 37
BT - Business Information Systems - 22nd International Conference, BIS 2019, Proceedings
A2 - Abramowicz, Witold
A2 - Corchuelo, Rafael
PB - Springer Verlag
T2 - 22nd International Conference on Business Information Systems, BIS 2019
Y2 - 26 June 2019 through 28 June 2019
ER -