Projects / Programmes source: ARRS

Modelling the influence of individuals' and network characteristics on dissemination of fake news in a social network

Research activity

Code Science Field Subfield
5.03.00  Social sciences  Sociology   

Code Science Field
5.04  Social Sciences  Sociology 
fake news, social networks, news dissemination, network modelling, agent based modelling
Evaluation (rules)
source: COBISS
Data for the last 5 years (citations for the last 10 years) on March 23, 2023; A3 for period 2017-2021
Data for ARRS tenders ( 04.04.2019 – Programme tender, archive )
Database Linked records Citations Pure citations Average pure citations
WoS  172  6,309  5,946  34.57 
Scopus  192  6,804  6,413  33.4 
Researchers (6)
no. Code Name and surname Research area Role Period No. of publications
1.  35875  MSc Marjeta Grahek    Technician  2021 - 2023 
2.  56778  MSc Maja Kocjan    Researcher  2022 - 2023 
3.  27800  PhD Zoran Levnajić  Physics  Researcher  2021 - 2023  123 
4.  31670  PhD Borut Lužar  Computer intensive methods and applications  Researcher  2021 - 2023  175 
5.  38768  PhD Boris Podobnik  Information science and librarianship  Researcher  2021 - 2023  105 
6.  20934  PhD Blaž Rodič  Administrative and organisational sciences  Principal Researcher  2021 - 2023  189 
Organisations (1)
no. Code Research organisation City Registration number No. of publications
1.  2784  Faculty of Information Studies in Novo mesto  Novo mesto  3375650  4,089 
The viral spread of digital misinformation has become so severe that the World Economic Forum (2018) considers it among the main threats to human society. There is worldwide concern over false news and the possibility that it can influence political, economic, and social well-being (Törnberg, 2018). The scale and rapidity of sharing fake news and misinformation is having an impact on democratic processes. False news can drive the misallocation of resources during terror attacks and natural disasters, the misalignment of business investments, and can misinform elections (Vosoughi et al., 2018). In order to curtail the negative influence of the fake news as an evolving phenomenon, we should continuously strive to understand it better, and study the mechanisms underlying the rapid diffusion of fake news in social networks. Most of existing research on fake news and related phenomena focuses on the analysis of past events by examining the spread of topics in social networks. While the analysis of large datasets (e.g. 500 milion tweets in Yang and Leskovec, 2010) is able to provide significant insight and enable the development of statistical models, and we have an understanding of the cognitive biases influencing individuals spreading fake news, we lack models that would allow us develop and test new theories to explain and predict this complex social phenomenon using rules that are at work at the level of individuals. We thus intend to fill the gap by developing a new, original ABM model to develop and test theories on robust rules that influence the dissemination of fake news in a social network at the level of individuals. Objective of the proposed research is to develop and test new theories on rules that influence the dissemination of fake news in a social network at the level of individuals, using a new, original ABM model. New theories will provide a better understanding of the fake news phenomenon, while the novel ABM model will facilitate understanding of the individual and social dynamics present in the social networks where fake news proliferate and allow us and other researchers develop and test new theories. We intend to develop a set of experiments to research the relationship between relative success (domination in news cycle) of fake news and a set of factors, i.e. individual and network characteristics, e.g. cognitive biases, political bias, connectedness, fact-checking time, presence of hubs or ‘influencer’ nodes and echo chambers. We intend to use existing research on the fake news phenomenon and agent based modelling to develop and test the theories, and validate them by comparing model results and large datasets from main social network and news websites.
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