https://orcid.org/0000-0001-8019-1035
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ABSTRACT
Over the last few years, a proliferation of attempts to define, understand and fight the spread of problematic information in contemporary media ecosystems emerged. Most of these attempts focus on false content and/or bad actors detection. In this paper, we argue for a wider ecological focus. Using the frame of media manipulation and a revised version of the ‘coordinated inauthentic behavior’ original definition, the paper presents a study based on an unprecedented combination of Facebook data, accessed through the CrowdTangle API, and two datasets of Italian political news stories published in the run-up to the 2018 Italian general election and 2019 European election. By focusing on actors’ collective behavior, we identified several networks of pages, groups, and verified public profiles (‘entities’), that shared the same political news articles on Facebook within a very short period of time. Some entities in our networks were openly political, while others, despite sharing political content too, deceptively presented themselves as entertainment venues. The proportion of inauthentic entities in a network affects the wideness of the range of news media sources they shared, thus pointing to different strategies and possible motivations. The paper has both theoretical and empirical implications: it frames the concept of ‘coordinated inauthentic behavior’ in existing literature, introduces a method to detect coordinated link sharing behavior and points out different strategies and methods employed by networks of actors willing to manipulate the media and public opinion.
Disclosure statement
No potential conflict of interest was reported by the author(s).
Notes on contributors
Fabio Giglietto, is Associate Professor at the Department of Communication Sciences, Humanities and International Studies at the University of Urbino Carlo Bo. His main research interests are theory of information, communication, and society with a specific focus on the relationship between social systems and digital technologies. On these topics, he has published extensively in journals such as the Journal of Communication, Information, Communication and Society, the Journal of Broadcasting and Electronic Media, Social Media + Society, and the International Journal of Communication.
Nicola Righetti, Ph.D., is a postdoctoral research fellow at the Department of Communication Sciences, Humanities and International Studies at the University of Urbino Carlo Bo. His research interests are in the field of sociology of culture and communication, with a focus on digital sociology, digital methods, data and text mining.
Luca Rossi, is Associate Professor in the Department of Digital Design at the IT University of Copenhagen. He is active in the field of digital methods for social sciences. His research is interdisciplinary trying to connect traditional sociological approaches with computational approaches. He is working on extending social network analysis techniques for social media analysis, on new approaches for unstructured communities detection, and mapping based on the study of multiplex networks.
Giada Marino, Ph.D. She is mainly interested in authenticity and ephemerality topics, with specific reference to social media affordances and user behaviors. She has contributed as junior research assistant to several academic research projects, such as Mapping Italian News Media Political Coverage in the Lead-up of 2018 General Election.
ORCID
Fabio Giglietto http://orcid.org/0000-0001-8019-1035
Nicola Righetti http://orcid.org/0000-0002-9257-5113
Luca Rossi http://orcid.org/0000-0002-3629-2039
Notes
1 Training the algorithm with content flagged as false by professional fact-checker (supervised machine learning) sounds like a promising compromise. However, even computationally and financially resourceful companies such as Google, Facebook or Twitter are still experimenting with this approach when it comes to misinformation.
2 Even if we introduce this stream of research as second, it is probably fair to say that, from a chronological point of view, it can easily be traced back to research that is older than those focused on actor identification.
3 Please see https://help.crowdtangle.com/en/articles/1140930-what-is-crowdtangle-tracking for an overview of what CrowdTangle is tracking. For this study, only Facebook and Instagram platforms have been used.
4 The algorithm is developed in R and the code is available at https://github.com/fabiogiglietto/CooRnet.
5 The Risk Ratio (or Relative Risk) is the ratio between the proportion of occurrence in groups exposed and non-exposed to a risk variable (here the coordinated/non-coordinated link sharing behavior). The value is statistically significant when its 95% confidence interval (CI) does not include 1.
6 The Gini coefficient is a measure of the degree of concentration (inequality) of a variable in a distribution. It ranges between 0 and 1: the more nearly equal the distribution, the lower its Gini index.
7 Spearman’s correlation is a measure of the strength and direction of a monotonic relationship between two variables. It ranges between 1 (perfect association) and −1 (perfect negative association), whereas a value of 0 indicates no association between variables.
