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Funding This work was supported by the Spanish Government grants FPU18/00304 and RYC-2015-18210, co-funded by the Euro-pean Social Fund.

Analysis of institutional authors

-Galindo, Javier PastorCorresponding Author

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Profiling users and bots in Twitter through social media analysis

Publicated to:Information Sciences. 613 161-183 - 2022-09-21 613(), DOI: 10.1016/j.ins.2022.09.046

Authors: -Galindo, Javier Pastor; Marmol, Felix Gomez; Perez, Gregorio Martinez

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Abstract

Social networks were designed to connect people online but have also been exploited to launch influence operations for manipulating society. The deployment of social bots has proven to be one of the most effective enablers to polarize and destabilize platforms. While automatic tools have been developed for their detection, the way to characterize these accounts and measure their impact is heterogeneous in the literature. In this work, we select metrics and algorithms from existing efforts to ensemble a data-driven methodology to profile groups of users and bots of Twitter from seven perspectives. We apply the framework to a dataset of Twitter retweets before the 10 November 2019 Spanish elections to characterize potential interferences. In this case study, Likely Bots (fully automated accounts) and Likely Semi-Bots (partially automated accounts) interacted with the same tendencies as Likely Humans (non-automated users), generating similar virality (information cascades) over time and without compromising the network connectivity. However, Likely Bots particularly stood out as close, visible, and reachable to other users. Likely Semi-Bots attracted particular attention, created proportionally more retweets, and were placed in strategically key positions in the core of the network. Results suggest that semi-automated accounts would be more threatening than fully automated ones. (c) 2022 The Author(s). Published by Elsevier Inc. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).

Keywords

Automatic toolsAutomationBotnetComparative analysiComparative analysisComparative analyzesData drivenFully automatedNetwork sciencePolarisPotential interferencesSocial botsSocial media analysisSocial networkSocial networking (online)Social networksUser profile

Quality index

Bibliometric impact. Analysis of the contribution and dissemination channel

The work has been published in the journal Information Sciences due to its progression and the good impact it has achieved in recent years, according to the agency WoS (JCR), it has become a reference in its field. In the year of publication of the work, 2022, it was in position 13/158, thus managing to position itself as a Q1 (Primer Cuartil), in the category Computer Science, Information Systems. Notably, the journal is positioned above the 90th percentile.

From a relative perspective, and based on the normalized impact indicator calculated from World Citations provided by WoS (ESI, Clarivate), it yields a value for the citation normalization relative to the expected citation rate of: 1.04. This indicates that, compared to works in the same discipline and in the same year of publication, it ranks as a work cited above average. (source consulted: ESI Nov 14, 2024)

This information is reinforced by other indicators of the same type, which, although dynamic over time and dependent on the set of average global citations at the time of their calculation, consistently position the work at some point among the top 50% most cited in its field:

  • Field Citation Ratio (FCR) from Dimensions: 6.81 (source consulted: Dimensions May 2025)

Specifically, and according to different indexing agencies, this work has accumulated citations as of 2025-05-31, the following number of citations:

  • WoS: 9
  • Scopus: 12
  • Google Scholar: 25
  • OpenCitations: 8

Impact and social visibility

From the perspective of influence or social adoption, and based on metrics associated with mentions and interactions provided by agencies specializing in calculating the so-called "Alternative or Social Metrics," we can highlight as of 2025-05-31:

  • The use, from an academic perspective evidenced by the Altmetric agency indicator referring to aggregations made by the personal bibliographic manager Mendeley, gives us a total of: 68.
  • The use of this contribution in bookmarks, code forks, additions to favorite lists for recurrent reading, as well as general views, indicates that someone is using the publication as a basis for their current work. This may be a notable indicator of future more formal and academic citations. This claim is supported by the result of the "Capture" indicator, which yields a total of: 67 (PlumX).

With a more dissemination-oriented intent and targeting more general audiences, we can observe other more global scores such as:

  • The Total Score from Altmetric: 1.
  • The number of mentions on the social network X (formerly Twitter): 1 (Altmetric).

Leadership analysis of institutional authors

There is a significant leadership presence as some of the institution’s authors appear as the first or last signer, detailed as follows: First Author (PASTOR GALINDO, JAVIER) .

the author responsible for correspondence tasks has been PASTOR GALINDO, JAVIER.