Defining, Detecting, and Characterizing Power Users in Threads

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.

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Publication Details

Journal
Big Data and Cognitive Computing
Published
2025-03-16
DOI
https://doi.org/10.3390/bdcc9030069
Citations
3
Primary Topic
Big Data and Digital Economy
Type
article
Field-Weighted Citation Impact
9.74

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article

Defining, Detecting, and Characterizing Power Users in Threads

Luca Virgili, Enrico Corradini, Gianluca Bonifazi, Domenico Ursino et al.
3 citations
Big Data and Cognitive Computing
Big Data and Digital Economy
9.74
article

Defining, Detecting, and Characterizing Power Users in Threads

Luca Virgili, Enrico Corradini, Gianluca Bonifazi, Domenico Ursino, Michele Marchetti, Christopher Buratti, Federica Parlapiano
article en
3 citations

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.

Big Data and Cognitive ComputingVol. 9(3)
Marche Polytechnic University (IT)
European Commission
Openalex Percentile: Top 3%
Big Data and Digital Economy
9.74
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