TY - GEN
T1 - Dynamic Analysis of Influencer Impact on Opinion Formation in Social Networks
AU - Berjawi, Omran
AU - Cavaliere, Danilo
AU - Fenza, Giuseppe
AU - Khatoun, Rida
N1 - Publisher Copyright:
© The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.
PY - 2025/1/1
Y1 - 2025/1/1
N2 - The rapid proliferation of social media platforms has transformed communication, enabling individuals to share opinions and influence others on an unprecedented scale. This paper addresses the challenge of quantifying the ability of social media influencers to change opinions over time. Traditional metrics, such as follower counts or engagement rates, offer a limited view of an influencer’s true impact. To face this challenge, this study provides a nuanced framework based on Friedkin-Johnsen model and Sentiment Analysis for analyzing how people’s opinions propagate through social networks and how influencers can affect these dynamics. The methodology consists in building interaction network graphs, detecting communities, and identifying key influencers using classic topology metrics. Then, it applies Sentiment Analysis to capture users’ opinions, which are injected into the Friedkin-Johnsen model to study their evolution over time. The results show the effectiveness of the proposed approach in determining the dynamics of social influence and opinion change.
AB - The rapid proliferation of social media platforms has transformed communication, enabling individuals to share opinions and influence others on an unprecedented scale. This paper addresses the challenge of quantifying the ability of social media influencers to change opinions over time. Traditional metrics, such as follower counts or engagement rates, offer a limited view of an influencer’s true impact. To face this challenge, this study provides a nuanced framework based on Friedkin-Johnsen model and Sentiment Analysis for analyzing how people’s opinions propagate through social networks and how influencers can affect these dynamics. The methodology consists in building interaction network graphs, detecting communities, and identifying key influencers using classic topology metrics. Then, it applies Sentiment Analysis to capture users’ opinions, which are injected into the Friedkin-Johnsen model to study their evolution over time. The results show the effectiveness of the proposed approach in determining the dynamics of social influence and opinion change.
KW - Dynamic Opinion Analysis
KW - Emotional Classification
KW - Friedkin-Johnsen model
KW - Influencers
KW - Online Social Behaviors
UR - https://www.scopus.com/pages/publications/105000405273
U2 - 10.1007/978-981-96-1483-7_32
DO - 10.1007/978-981-96-1483-7_32
M3 - Conference contribution
AN - SCOPUS:105000405273
SN - 9789819614820
T3 - Lecture Notes in Computer Science
SP - 394
EP - 408
BT - Web Information Systems Engineering – WISE 2024 PhD Symposium, Demos and Workshops - WEB-for-GOOD 2024, AIWDA 2024, SWIFT-AG 2024, Proceedings
A2 - Barhamgi, Mahmoud
A2 - Wang, Hua
A2 - Wang, Xin
A2 - Aïmeur, Esma
A2 - Mrissa, Michael
A2 - Chikhaoui, Belkacem
A2 - Boukadi, Khouloud
A2 - Grati, Rima
A2 - Maamar, Zakaria
PB - Springer Science and Business Media Deutschland GmbH
T2 - PhD Symposium, Posters, Demos, and A Web for more inclusive, sustainable and prosperous societies, WEB-for-GOOD 2024 and 1st International Workshop on AI and Web Data Analytics, AIWDA 2024, SWIFT-AG 2024 form the 25th International Conference on Web Information Systems Engineering, WISE 2024
Y2 - 2 December 2024 through 5 December 2024
ER -