Affiliation:
1. Department of Informatics, Ionian University, 7 Tsirigoti Square, 49100 Corfu, Greece
2. Department of History and Philosophy of Sciences, National and Kapodistrian University of Athens, 15771 Athens, Greece
Abstract
In the dynamic landscape of digital information, the rise of misinformation and fake news presents a pressing challenge. This paper takes a completely new approach to verifying news, inspired by how quantum actors can reach agreement even when they are spatially spread out. We propose a radically new—to the best of our knowledge—algorithm that uses quantum “entanglement” (think of it as a special connection) to help news aggregators “sniff out” bad actors, whether they are other news sources or even fact-checkers trying to spread misinformation. This algorithm does not rely on quantum signatures; it merely uses basic quantum technology which we already have, in particular, special pairs of particles called “EPR pairs” that are much easier to create than other options. More elaborate entangled states are like juggling too many balls—they are difficult to make and slow things down, especially when many players are involved. So, we adhere to Bell states, the simplest form of entanglement, which are easy to generate no matter how many players are involved. This means that our algorithm is faster to set up, works for any number of participants, and is more practical for real-world use. Additionally, as a “bonus point”, it finishes in a fixed number of steps, regardless of how many players are involved, making it even more scalable. This new approach may lead to a powerful and efficient way to fight misinformation in the digital age, using the weird and wonderful world of quantum mechanics.
Reference47 articles.
1. Campan, A., Cuzzocrea, A., and Truta, T.M. (2017, January 11–14). Fighting fake news spread in online social networks: Actual trends and future research directions. Proceedings of the 2017 IEEE International Conference on Big Data (Big Data), Boston, MA, USA.
2. Zhou, X., Zafarani, R., Shu, K., and Liu, H. (2019, January 11–15). Fake News: Fundamental Theories, Detection Strategies and Challenges. Proceedings of the Twelfth ACM International Conference on Web Search and Data Mining, WSDM ’19, Melbourne, VIC, Australia.
3. Vorhies, W. (2024, January 14). Using Algorithms to Detect Fake News—The State of the Art—DataScienceCentral.com. Available online: https://www.datasciencecentral.com/using-algorithms-to-detect-fake-news-the-state-of-the-art/.
4. Fake News Detection on Social Media: A Data Mining Perspective;Shu;ACM SIGKDD Explor. Newsl.,2017
5. Rashkin, H., Choi, E., Jang, J.Y., Volkova, S., and Choi, Y. (2017, January 7–11). Truth of Varying Shades: Analyzing Language in Fake News and Political Fact-Checking. Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing, Copenhagen, Denmark.