Projects / Programmes
Modelling the influence of individuals' and network characteristics on dissemination of fake
news in a social network
| 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
Data for the last 5 years (citations for the last 10 years) on
April 18, 2024;
A3 for period
2018-2022
Data for ARIS tenders (
04.04.2019 – Programme tender,
archive
)
| Database |
Linked records |
Citations |
Pure citations |
Average pure citations |
| WoS |
180 |
6,857 |
6,473 |
35.96 |
| Scopus |
198 |
7,439 |
7,026 |
35.48 |
Researchers (7)
| no. |
Code |
Name and surname |
Research area |
Role |
Period |
No. of publicationsNo. of publications |
| 1. |
35875 |
Marjeta Grahek |
|
Technical associate |
2021 - 2022 |
0 |
| 2. |
56778 |
MSc Maja Kocjan |
|
Technical associate |
2022 |
0 |
| 3. |
27800 |
PhD Zoran Levnajić |
Physics |
Researcher |
2021 - 2024 |
135 |
| 4. |
31670 |
PhD Borut Lužar |
Computer intensive methods and applications |
Researcher |
2021 - 2024 |
183 |
| 5. |
38768 |
PhD Boris Podobnik |
Information science and librarianship |
Researcher |
2021 - 2024 |
106 |
| 6. |
20934 |
PhD Blaž Rodič |
Administrative and organisational sciences |
Head |
2021 - 2024 |
199 |
| 7. |
57138 |
Petra Roginić |
|
Technical associate |
2023 - 2024 |
0 |
Organisations (1)
Abstract
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.
