A Survey on Computational Propaganda Detection

By Giovanni Da San Martino, Stefano Cresci, Alberto Barrón-Cedeño, Seunghak Yu, Roberto Di Pietro, Preslav Nakov. 2020

216 citations recorded by the source corpus.

Propaganda campaigns aim at influencing people's mindset with the purpose of advancing a specific agenda. They exploit the anonymity of the Internet, the micro-profiling ability of social networks, and the ease of automatically creating and managing coordinated networks of accounts, to reach millions of social network users with persuasive messages, specifically targeted to topics each individual user is sensitive to, and ultimately influencing the outcome on a targeted issue. In this survey, we review the state of the art on computational propaganda detection from the perspective of Natural Language Processing and Network Analysis, arguing about the need for combined efforts between these communities. We further discuss current challenges and future research directions.

Subfields: AI Ethics, Fairness & Society

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In AI Ethics, Fairness & Society

Citations in the corpus

A decade of social bot detection (2020, background): “Indeed, the possibility for malicious accounts to leverage deepfake texts, profile pictures, and videos is worrying, and worthy of increased attention [10].”
SemEval-2020 Task 11: Detection of Propaganda Techniques in News Articles (2020, background): “See (Da San Martino et al., 2020a) for a recent survey on computational propaganda detection.”
Can Large Language Models Transform Computational Social Science? (2023, background): “Understanding persuasive strategies is central to behavioral economics and the psychology of advertising and propaganda (Martino et al., 2020).”
Faking Fake News for Real Fake News Detection: Propaganda-Loaded Training Data Generation (2022, background): “However, classiﬁers trained on such datasets may only learn to recognize the bias of each news source instead of propa-ganda (Da San Martino et al., 2020).”