Academic Journal
Signals of propaganda-Detecting and estimating political influences in information spread in social networks.
| Title: | Signals of propaganda-Detecting and estimating political influences in information spread in social networks. |
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| Authors: | Sela A; Agricultural Engineering Department, The Volcani Agricultural Research Organization (ARO), Bet Dagan, Israel.; Department Industrial Engineering, Ariel University, Ariel, Israel., Neter O; Department of Computer Science, Bar Ilan University, Tel Aviv, Israel.; Microsoft Security Research, R&D Center, Herzeliya, Israel., Lohr V; Department of Information Technologies, Faculty of Economics and Management, Czech University of Life Sciences Prague, Prague, Czech Republic., Cihelka P; Department of Information Technologies, Faculty of Economics and Management, Czech University of Life Sciences Prague, Prague, Czech Republic., Wang F; Department of Computer Science, Bar Ilan University, Tel Aviv, Israel., Zwilling M; Department of Economics and Business Administration, Ariel University, Ariel, Israel., Phillip Sabou J; Department of Information Technologies, Faculty of Economics and Management, Czech University of Life Sciences Prague, Prague, Czech Republic., Ulman M; Department of Information Technologies, Faculty of Economics and Management, Czech University of Life Sciences Prague, Prague, Czech Republic. |
| Source: | PloS one [PLoS One] 2025 Jan 30; Vol. 20 (1), pp. e0309688. Date of Electronic Publication: 2025 Jan 30 (Print Publication: 2025). |
| Publication Type: | Journal Article |
| Language: | English |
| Journal Info: | Publisher: Public Library of Science Country of Publication: United States NLM ID: 101285081 Publication Model: eCollection Cited Medium: Internet ISSN: 1932-6203 (Electronic) Linking ISSN: 19326203 NLM ISO Abbreviation: PLoS One Subsets: MEDLINE |
| Imprint Name(s): | Original Publication: San Francisco, CA : Public Library of Science |
| MeSH Terms: | Information Dissemination*/methods , Social Media*/trends , Social Networking* , Propaganda* , Politics*, Large Language Models/trends ; Search Engine |
| Abstract: | Social networks are a battlefield for political propaganda. Protected by the anonymity of the internet, political actors use computational propaganda to influence the masses. Their methods include the use of synchronized or individual bots, multiple accounts operated by one social media management tool, or different manipulations of search engines and social network algorithms, all aiming to promote their ideology. While computational propaganda influences modern society, it is hard to measure or detect it. Furthermore, with the recent exponential growth in large language models (L.L.M), and the growing concerns about information overload, which makes the alternative truth spheres more noisy than ever before, the complexity and magnitude of computational propaganda is also expected to increase, making their detection even harder. Propaganda in social networks is disguised as legitimate news sent from authentic users. It smartly blended real users with fake accounts. We seek here to detect efforts to manipulate the spread of information in social networks, by one of the fundamental macro-scale properties of rhetoric-repetitiveness. We use 16 data sets of a total size of 13 GB, 10 related to political topics and 6 related to non-political ones (large-scale disasters), each ranging from tens of thousands to a few million of tweets. We compare them and identify statistical and network properties that distinguish between these two types of information cascades. These features are based on both the repetition distribution of hashtags and the mentions of users, as well as the network structure. Together, they enable us to distinguish (p - value = 0.0001) between the two different classes of information cascades. In addition to constructing a bipartite graph connecting words and tweets to each cascade, we develop a quantitative measure and show how it can be used to distinguish between political and non-political discussions. Our method is indifferent to the cascade's country of origin, language, or cultural background since it is only based on the statistical properties of repetitiveness and the word appearance in tweets bipartite network structures. (Copyright: © 2025 Sela et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.) |
| Competing Interests: | The authors have declared that no competing interests exist. |
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| Entry Date(s): | Date Created: 20250130 Date Completed: 20250722 Latest Revision: 20250722 |
| Update Code: | 20260130 |
| PubMed Central ID: | PMC11781619 |
| DOI: | 10.1371/journal.pone.0309688 |
| PMID: | 39883667 |
| Database: | MEDLINE |
| ISSN: | 1932-6203 |
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| DOI: | 10.1371/journal.pone.0309688 |