Home > Published Issues > 2023 > Volume 14, No. 1, 2023 >
JAIT 2023 Vol.14(1): 145-152
doi: 10.12720/jait.14.1.145-152

Algorithm for Safety Decisions in Social Media Feeds Using Personification Patterns

Prema Pandurang Gawade* and Sarang Achyut Joshi
Department of Computer Engineering, SCTR’S Pune Institute of Computer Technology, Savitribai Phule Pune University (SPPU), Pune, India
*Correspondence: prema.gawade12@gmail.com

Manuscript received July 18, 2022; revised September 17, 2022; accepted September 28, 2022; published February 27, 2023.

Abstract—For safety decisions in social media applications, it is necessary to classify personification patterns. The paper proposes using video material to apply machine learning to select, and extract significant feature qualities and grasp the semantics of feature space connection to comprehend the personification of a certain user. The feature traits are based on a computer vision-based approach and a natural language-based approach. A strong belief is calculated from language descriptions and persona traits. These traits are then used to determine the overlap of feature space using various ML algorithms to deduce the intrinsic relationships. The proposed goal is validated by this algorithm and user personification is an important aspect that can be captured through video analytics. Using this personification-based method, better decisions can be made in the given domain space.

Keywords—persona identification, safety, pattern, machine learning

Cite: Prema Pandurang Gawade and Sarang Achyut Joshi, "Algorithm for Safety Decisions in Social Media Feeds Using Personification Patterns," Journal of Advances in Information Technology, Vol. 14, No. 1, pp. 145-152, February 2023.

Copyright © 2023 by the authors. This is an open access article distributed under the Creative Commons Attribution License (CC BY-NC-ND 4.0), which permits use, distribution and reproduction in any medium, provided that the article is properly cited, the use is non-commercial and no modifications or adaptations are made.

This paper belongs to Topic Collection: 
Machine Learning in Computer and Information Systems.