Location via proxy:   [ UP ]  
[Report a bug]   [Manage cookies]                
skip to main content
10.1109/CoG52621.2021.9618993guideproceedingsArticle/Chapter ViewAbstractPublication PagesConference Proceedingsacm-pubtype
research-article

The Propaganda Machine: Generating Biased Reports about Risk Games

Published: 17 August 2021 Publication History

Abstract

In this work we present a system that generates reports for a game of Risk. These reports, far from being “neutral”, aim instead at mimicking propaganda, and try to influence the opinions of the readers about the performance of a Risk player. The system, while limited in scope, was able to persuade some test subjects in a qualitative evaluation, hinting at the abilities that a more sophisticated system might have.

References

[1]
L. Gatti, M. Guerini, O. Stock, and C. Strapparava, “Sentiment variations in text for persuasion technology,” in Proceedings of the 9th International Conference on Persuasive Technology (PERSUASIVE 2014). Springer, 2014, pp. 106–117.
[2]
G. Carenini and J. D. Moore, “Generating and evaluating evaluative arguments,” Artificial Intelligence, vol. 170, no. 11, pp. 925–952, 2006.
[3]
V. Munigala, A. Mishra, S. G. Tamilselvam, S. Khare, R. Dasgupta, and A. Sankaran, “PersuAIDE! An adaptive persuasive text generation system for fashion domain,” in Companion Proceedings of the The Web Conference 2018 (WWW), 2018, pp. 335–342.
[4]
G. S. Jowett and V. O'Donnell, Propaganda & persuasion. Sage publications, 2018.
[5]
T. J. M. Bench-Capon and P. E. Dunne, “Argumentation in artificial intelligence,” Artificial intelligence, vol. 171, no. 10–15, pp. 619–641, 2007.
[6]
M. Miceli, F. De Rosis, and I. Poggi, “Emotional and non-emotional persuasion,” Applied Artificial Intelligence, vol. 20, no. 10, pp. 849–879, 2006.
[7]
G. D. S. Martino, S. Cresci, A. Barron-Cedeno, S. Yu, R. D. Pietro, and P. Nakov, “A survey on computational propaganda detection,” in Proceedings of the 29th International Joint Conference on Artificial Intelligence (IJCAI-20), 2020.
[8]
G. DaSan Martino, A. Barron-Cedeno, H. Wachsmuth, R. Petrov, and P. Nakov, “SemEval-2020 task 11: Detection of propaganda techniques in news articles,” in Proceedings of the 14th Workshop on Semantic Evaluation (SemEval), 2020, pp. 1377–1414.
[9]
R. Zellers, A. Holtzman, H. Rashkin, Y. Bisk, A. Farhadi, F. Roesner, and Y. Choi, “Defending against neural fake news,” in Advances in Neural Information Processing Systems 32 (NeurIPS 2019), H. Wallach, H. Larochelle, A. Beygelzimer, F. D’ Alche Buc, E. Fox, and R. Garnett, Eds. Curran Associates, Inc., 2019, pp. 9054–9065. [Online]. Available: http://papers.nips.cc/paper/9106-defending-against-neural-fake-news.pdf.
[10]
P. Gervas, “Targeted storyfying: Creating stories about particular events.” in Proceedings of the 9th International Conference on Computational Creativity (ICCC ‘18), 2018, pp. 232–239.
[11]
R. Doust and P. Gervas, “Content determination for chess as a source for suspenseful narratives,” in Proceedings of the 3rd Workshop on Computational Creativity in Natural Language Generation (CC-NLG 2018), 2018, pp. 26–33.
[12]
J. Kowalski, L. Zarczyriski, and A. Kisielewicz, “Evaluating chess-like games using generated natural language descriptions,” in Proceedings of the 15th International Conference on Advances in Computer Games (ACG 2017), M. H. Winands, H. J. Van Den Herik, and W. A. Kosters, Eds. Cham: Springer International Publishing, 2017, pp. 127–139.
[13]
A. Tapscott, C. Leon, and P. Gervas, “Generating stories using role-playing games and simulated human-like conversations,” in Proceedings of the 3rd Workshop on Computational Creativity in Natural Language Generation (CC-NLG 2018),2018, pp. 34–42.
[14]
C. R. Strong, M. Mehta, K. Mishra, A. Jones, and A. Ram, “Emotionally driven natural language generation for personality rich characters in interactive games,” in Proceedings of the 3rd AAAI Conference on Artificial Intelligence for Interactive Digital Entertainment (AIIDE-07), 2007.
[15]
U. Ehsan, P. Tambwekar, L. Chan, B. Harrison, and M. O. Riedl, “Learning to generate natural language rationales for game playing agents,” in Joint Proceedings of the AIIDE 2018 Workshops (AIIDE-WS 2018), 2018.
[16]
A. Gatt and E. Krahmer, “Survey of the state of the art in natural language generation: Core tasks, applications and evaluation,” Journal of Artificial Intelligence Research, vol. 61, pp. 1–64, jan 2018.
[17]
yura.net, “Domination,” 2020. [Online]. Available: https://domination.sourceforge.net/.
[18]
H. D. Lasswell, “The Theory of Political Propaganda,” American Political Science Review, vol. 21, no. 3, pp. 627–631, aug 1927.
[19]
R. Bytwerk, “German propaganda archive,” 1998. [Online]. Available: https://research.calvin.edu/german-propaganda-archive/.
[20]
A. Graefe, “Guide to Automated Journalism,” Tow Center for Digital Journalism Report, 2016.

Index Terms

  1. The Propaganda Machine: Generating Biased Reports about Risk Games
    Index terms have been assigned to the content through auto-classification.

    Recommendations

    Comments

    Information & Contributors

    Information

    Published In

    cover image Guide Proceedings
    2021 IEEE Conference on Games (CoG)
    Aug 2021
    1113 pages

    Publisher

    IEEE Press

    Publication History

    Published: 17 August 2021

    Qualifiers

    • Research-article

    Contributors

    Other Metrics

    Bibliometrics & Citations

    Bibliometrics

    Article Metrics

    • 0
      Total Citations
    • 0
      Total Downloads
    • Downloads (Last 12 months)0
    • Downloads (Last 6 weeks)0
    Reflects downloads up to 10 Nov 2024

    Other Metrics

    Citations

    View Options

    View options

    Get Access

    Login options

    Media

    Figures

    Other

    Tables

    Share

    Share

    Share this Publication link

    Share on social media