Exploring Alternative Approaches for TwitterForensics: Utilizing Social Network Analysis to Identify Key Actors and Potential Suspects

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Keywords: SNA, Twitterforensics, Secondary Key Actor, Key Actor

Abstract

SNA (Social Network Analysis) is a modeling method for users which is symbolized by points (nodes) and interactions between users are represented by lines (edges). This method is needed to see patterns of social interaction in the network starting with finding out who the key actors are. The novelty of this study lies in the expansion of the analysis of other suspects, not only key actors identified during this time. This method performs a narrowed network mapping by examining only nodes connected to key actors. Secondary key actors no longer use centrality but use weight indicators at the edges. A case study using the hashtag "Manchester United" on the social media platform Twitter was conducted in the study. The results of the Social Network Analysis (SNA) revealed that @david_ornstein accounts are key actors with centrality of 2298 degrees. Another approach found @hadrien_grenier, @footballforall, @theutdjournal accounts had a particularly high intensity of interaction with key actors. The intensity of communication between secondary actors and key actors is close to or above the weighted value of 50. The results of this analysis can be used to suspect other potential suspects who have strong ties to key actors by looking.

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Author Biographies

Irwan Sembiring, Universitas Kristen Satya Wacana

Faculty of Information Technology,
Universitas Kristen Satya Wacana

Ade Iriani, Universitas Kristen Satya Wacana

Faculty of Information Technology,
Universitas Kristen Satya Wacana

Suharyadi, Universitas Kristen Satya Wacana

Faculty of Information Technology,
Universitas Kristen Satya Wacana

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Published
2023-08-05
How to Cite
[1]
I. Sembiring, A. Iriani, and S. Suharyadi, “Exploring Alternative Approaches for TwitterForensics: Utilizing Social Network Analysis to Identify Key Actors and Potential Suspects”, intensif, vol. 7, no. 2, pp. 161-176, Aug. 2023.