Paper Type
Complete
Paper Number
1675
Description
Individuals are increasingly relying on social media for information exchange, which has led to a corresponding increased adoption of social media platforms for communication. However, there is also a growing presence of malicious users looking to drive the spread of false information. As a result, it is necessary to investigate and develop a technique for studying influence on social media. This study adopts a multi-pronged approach featuring network analysis and CFSA to detect influential users and focal structures (key users capable of coordinating events) in Instagram data for the South China Sea (SCS) narrative. The dataset utilized spanned a period between 2011 and 2023, which led to over 70,000 records of relevant SCS Instagram data. Based on the relevant topics, the network analysis methods and CFSA model detected influential users and contextual focal structures within the network as well as the key SCS-related topics they post about.
Recommended Citation
Nwana, Lotenna; Nwashili, Anulika; Alassad, Dr. Mustafa; and Agarwal, Nitin, "Detecting Influential Nodes within the South China Sea on Instagram" (2024). PACIS 2024 Proceedings. 22.
https://aisel.aisnet.org/pacis2024/track17_socmedia/track17_socmedia/22
Detecting Influential Nodes within the South China Sea on Instagram
Individuals are increasingly relying on social media for information exchange, which has led to a corresponding increased adoption of social media platforms for communication. However, there is also a growing presence of malicious users looking to drive the spread of false information. As a result, it is necessary to investigate and develop a technique for studying influence on social media. This study adopts a multi-pronged approach featuring network analysis and CFSA to detect influential users and focal structures (key users capable of coordinating events) in Instagram data for the South China Sea (SCS) narrative. The dataset utilized spanned a period between 2011 and 2023, which led to over 70,000 records of relevant SCS Instagram data. Based on the relevant topics, the network analysis methods and CFSA model detected influential users and contextual focal structures within the network as well as the key SCS-related topics they post about.
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