Publication Type
Journal Article
Version
acceptedVersion
Publication Date
2-2026
Abstract
In a series of our prior works, we study influence and subscription maximization problems in social networks that are based on posts having influential content; as content we consider a set of features where each feature corresponds to a specific social network page, whereas influence and subscription relate to gaining the postlike and subscription-to-brand page of targeted users, respectively; subscription is conceptually achieved as repetitive influence on users. So, both influence and subscription depend on content that gains the likes of users; however, to be realistic, modeling and estimating such likes is a complex problem that has not been adequately studied via a computational way. In this article, we propose a novel perspective on the mentioned content-aware research that has the potential to really understand the user likes to social network posts. By the term feelit system, we refer to the combined result of this proposal with our previous content-aware research, so as to estimate the user likes in a much more analytical way than before. We provide realistic examples to clarify the operation of feelit, and we discuss a number of technical challenges associated with it. Our contributions are beneficial to several formulations of influence maximization in the literature since feelit provides an accurate and advanced way for brands to estimate the user likes to their posts.
Keywords
Social networking (online), Stress, Electronic mail, Computational modeling, Surveys, Soft sensors, Particle measurements, Content match advertising, influence maximization (IM), messaging, social networks, subscription maximization
Discipline
Databases and Information Systems | Social Media | Software Engineering
Research Areas
Data Science and Engineering
Publication
IEEE Transactions on Computational Social Systems
First Page
1
Last Page
15
ISSN
2329-924X
Identifier
10.1109/TCSS.2026.3655270
Publisher
IEEE
Embargo Period
2-23-2026
Citation
THEOCHARIDIS, Konstantinos; LAUW, Hady W.; and KARRAS, Panagiotis.
The Feelit System: Application content-aware perspectives and challenges on understanding user likes in social network posts. (2026). IEEE Transactions on Computational Social Systems. 1-15.
Available at: https://ink.library.smu.edu.sg/sis_research/11007
Copyright Owner and License
Authors
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-No Derivative Works 4.0 International License.
Additional URL
https://doi.org/10.1109/TCSS.2026.3655270
Included in
Databases and Information Systems Commons, Social Media Commons, Software Engineering Commons