Publication Type : Conference Paper
Publisher : Elsevier
Source : 5th International Conference on Innovative Data Communication Technologies and Application
Url : https://www.sciencedirect.com/science/article/pii/S1877050924005878?ref=pdf_download&fr=RR-2&rr=89e6aba85cc13f97
Campus : Amritapuri
School : School of Computing
Year : 2024
Abstract : Social media is a digital environment where users openly share their opinions and engage in debates and discussions on various
topics. Social media has amassed an enormous quantity of accessible data due to its constantly expanding and highly active user
base. This data is a valuable resource for researching a variety of topics. An example of such a research problem is identifying
user similarities by analyzing their data. This article explores combining comments, textual posts, and likes for comments to create
keywords or tags and group the users. These tags are extracted from user data and utilized in constructing a tag network. The
tag network facilitates the formation of communities consisting of users with similar interests. User grouping is achieved based
on the tags extracted from the posts. The proposed methods employ TF-IDF (term frequency-inverse document frequency) and
TextRank algorithms to extract the tags. Kernel diffusion determines the similarities between tags in the tag network. Finally, an
aggregation-hierarchical clustering algorithm is employed to group social media users based on these tags.
Cite this Research Publication : Lekshmi S Nair, and Jo Cheriyan. ”From Posts to Personas: Classifying Users in the Social Media Landscape.” Procedia Computer Science 233 (2024): 381-390.