Version 1
: Received: 19 September 2023 / Approved: 20 September 2023 / Online: 20 September 2023 (04:51:16 CEST)
How to cite:
Mustafa, Z. U.; Amir, M.; Mustafa, M.; Anwar, M. A. From Divide to Debate: A Methodological Approach to Dismantling Polarization in Social Networks. Preprints2023, 2023091350. https://doi.org/10.20944/preprints202309.1350.v1
Mustafa, Z. U.; Amir, M.; Mustafa, M.; Anwar, M. A. From Divide to Debate: A Methodological Approach to Dismantling Polarization in Social Networks. Preprints 2023, 2023091350. https://doi.org/10.20944/preprints202309.1350.v1
Mustafa, Z. U.; Amir, M.; Mustafa, M.; Anwar, M. A. From Divide to Debate: A Methodological Approach to Dismantling Polarization in Social Networks. Preprints2023, 2023091350. https://doi.org/10.20944/preprints202309.1350.v1
APA Style
Mustafa, Z. U., Amir, M., Mustafa, M., & Anwar, M. A. (2023). From Divide to Debate: A Methodological Approach to Dismantling Polarization in Social Networks. Preprints. https://doi.org/10.20944/preprints202309.1350.v1
Chicago/Turabian Style
Mustafa, Z. U., Manal Mustafa and Muhammad Adnan Anwar. 2023 "From Divide to Debate: A Methodological Approach to Dismantling Polarization in Social Networks" Preprints. https://doi.org/10.20944/preprints202309.1350.v1
Abstract
Social media platforms are not serving only as tools of communication rather they are acting like intricate systems managing our interactions. A major setback to our interactions occur when the flow of information in these networks gets distorted through polarization. It resembles a society where every debate turns into divide. Our research focuses exclusively on quantifying polarization as well as solving this issue head-on. In order to accurately gauge polarization, we’ve developed a new method that takes into consideration majority of its influencing factors. This helps us in correctly quantifying the polarization value of a given network and then by proposing it as an optimization problem we are attempting to maximally decrease polarization while staying within the set budget. We test our method on synthetic and real-world data sets, and find out that polarization is declining, and diversity is increasing. This proves that our recommendation engine has a penchant for finding the ideal connections between the nodes to start a dialogue. Our research presents a successful tactic to quantifying and reducing polarization in social media networks. The results indicate that our novel metric and intervention technique are effective in dismantling echo chambers, thereby promoting diversity in these networks.
Keywords
echo chamber; filter bubble; digital social networks; social network analysis; polarization; diversity; community discovery
Subject
Computer Science and Mathematics, Computer Networks and Communications
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
