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Article

Defining, Detecting, and Characterizing Power Users in Threads

by
Gianluca Bonifazi
,
Christopher Buratti
,
Enrico Corradini
,
Michele Marchetti
,
Federica Parlapiano
,
Domenico Ursino
* and
Luca Virgili
DII, Polytechnic University of Marche, 60121 Ancona, Italy
*
Author to whom correspondence should be addressed.
Big Data Cogn. Comput. 2025, 9(3), 69; https://doi.org/10.3390/bdcc9030069
Submission received: 21 January 2025 / Revised: 11 March 2025 / Accepted: 14 March 2025 / Published: 16 March 2025

Abstract

Threads is a new social network that was launched by Meta in July 2023 and conceived as a direct alternative to X. It is a unique case study in the social network landscape, as it is content-based like X, but has an Instagram-based growth model, which makes it significantly different from X. As it was launched recently, studies on Threads are still scarce. One of the most common investigations in social networks regards power users (also called influencers, lead users, influential users, etc.), i.e., those users who can significantly influence information dissemination, user behavior, and ultimately the current dynamics and future development of a social network. In this paper, we want to contribute to the knowledge of Threads by showing that there are indeed power users in this social network and then attempt to understand the main features that characterize them. The definition of power users that we adopt here is novel and leverages the four classical centrality measures of Social Network Analysis. This ensures that our study of power users can benefit from the enormous knowledge on centrality measures that has accumulated in the literature over the years. In order to conduct our analysis, we had to build a Threads dataset, as none existed in the literature that contained the information necessary for our studies. Once we built such a dataset, we decided to make it open and thus available to all researchers who want to perform analyses on Threads. This dataset, the new definition of power users, and the characterization of Threads power users are the main contributions of this paper.
Keywords: Threads; power users; influencers; lead users; influential users; social network analysis; degree centrality; closeness centrality; betweenness centrality; eigenvector centrality Threads; power users; influencers; lead users; influential users; social network analysis; degree centrality; closeness centrality; betweenness centrality; eigenvector centrality

Share and Cite

MDPI and ACS Style

Bonifazi, G.; Buratti, C.; Corradini, E.; Marchetti, M.; Parlapiano, F.; Ursino, D.; Virgili, L. Defining, Detecting, and Characterizing Power Users in Threads. Big Data Cogn. Comput. 2025, 9, 69. https://doi.org/10.3390/bdcc9030069

AMA Style

Bonifazi G, Buratti C, Corradini E, Marchetti M, Parlapiano F, Ursino D, Virgili L. Defining, Detecting, and Characterizing Power Users in Threads. Big Data and Cognitive Computing. 2025; 9(3):69. https://doi.org/10.3390/bdcc9030069

Chicago/Turabian Style

Bonifazi, Gianluca, Christopher Buratti, Enrico Corradini, Michele Marchetti, Federica Parlapiano, Domenico Ursino, and Luca Virgili. 2025. "Defining, Detecting, and Characterizing Power Users in Threads" Big Data and Cognitive Computing 9, no. 3: 69. https://doi.org/10.3390/bdcc9030069

APA Style

Bonifazi, G., Buratti, C., Corradini, E., Marchetti, M., Parlapiano, F., Ursino, D., & Virgili, L. (2025). Defining, Detecting, and Characterizing Power Users in Threads. Big Data and Cognitive Computing, 9(3), 69. https://doi.org/10.3390/bdcc9030069

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