Abstract
Online Social Networks (OSNs) are comprehensive media that help individuals to be connected through social networking sites (SNS) such as Twitter, Instagram, etc. People share their interests, activities and can exchange ideas. OSNs are typically large in size and complex as those media have an enormous number of users and multi kind relationships among them. Users reveal their interests in diverse topics in OSN and mostly, users' degree of topical interest changes over time. Tracking users' interests from such SNS and grouping users having similar interests based on that becomes significant for various domains. In this paper, we pay attention to identify and track users' topical interests on Twitter over time. Next, we group users with similar degrees of interest in different clusters. We perform experiments on real datasets and got interesting results.
| Original language | English |
|---|---|
| Title of host publication | 2020 IEEE Region 10 Symposium, TENSYMP 2020 |
| Publisher | Institute of Electrical and Electronics Engineers Inc. |
| Pages | 1054-1057 |
| Number of pages | 4 |
| ISBN (Electronic) | 9781728173665 |
| DOIs | |
| State | Published - 5 Jun 2020 |
| Externally published | Yes |
Publication series
| Name | 2020 IEEE Region 10 Symposium, TENSYMP 2020 |
|---|
Bibliographical note
Publisher Copyright:© 2020 IEEE.
Keywords
- Active user
- Online Social Network
- Topical Clusters
ASJC Scopus subject areas
- Artificial Intelligence
- Computer Networks and Communications
- Computer Vision and Pattern Recognition
- Signal Processing
- Energy Engineering and Power Technology
- Biomedical Engineering
- Electrical and Electronic Engineering
- Health Informatics
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