Abstract
We study the problem of election control through social influence when the manipulator is allowed to use the locations that she acquired on the network for sending both positive and negative messages on multiple candidates, widely extending the previous results available in the literature that study the influence of a single message on a single candidate. In particular, we provide a tight characterization of the settings in which the maximum increase in the margin of victory can be efficiently approximated and of those in which any approximation turns out to be impossible.
This work has been partially supported by the Italian MIUR PRIN 2017 Project ALGADIMAR “Algorithms, Games, and Digital Market”.
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References
Abouei Mehrizi, M., Corò, F., Cruciani, E., D’Angelo, G.: Election control through social influence with unknown preferences. In: Kim, D., Uma, R.N., Cai, Z., Lee, D.H. (eds.) COCOON 2020. LNCS, vol. 12273, pp. 397–410. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-58150-3_32
Auletta, V., Caragiannis, I., Ferraioli, D., Galdi, C., Persiano, G.: Minority becomes majority in social networks. In: WINE, pp. 74–88 (2015)
Auletta, V., Caragiannis, I., Ferraioli, D., Galdi, C., Persiano, G.: Information retention in heterogeneous majority dynamics. In: WINE, pp. 30–43 (2017)
Auletta, V., Caragiannis, I., Ferraioli, D., Galdi, C., Persiano, G.: Robustness in discrete preference games. In: AAMAS, pp. 1314–1322 (2017)
Auletta, V., Ferraioli, D., Greco, G.: Reasoning about consensus when opinions diffuse through majority dynamics. In: IJCAI, pp. 49–55 (2018)
Auletta, V., Ferraioli, D., Fionda, V., Greco, G.: Maximizing the spread of an opinion when tertium datur EST. In: AAMAS, pp. 1207–1215 (2019)
Auletta, V., Ferraioli, D., Greco, G.: On the effectiveness of social proof recommendations in markets with multiple products. In: ECAI, pp. 19–26 (2020)
Auletta, V., Ferraioli, D., Savarese, V.: Manipulating an election in social networks through edge addition. In: AI*IA, pp. 495–510 (2019)
Bredereck, R., Elkind, E.: Manipulating opinion diffusion in social networks. In: IJCAI (2017)
Castiglioni, M., Ferraioli, D., Gatti, N.: Election control in social networks via edge addition or removal. In: AAAI, pp. 1878–1885 (2020)
Corò, F., Cruciani, E., D’Angelo, G., Ponziani, S.: Exploiting social influence to control elections based on scoring rules. In: IJCAI, pp. 201–207 (2019)
Corò, F., Cruciani, E., D’Angelo, G., Ponziani, S.: Vote for me!: Election control via social influence in arbitrary scoring rule voting systems. In: AAMAS, pp. 1895–1897 (2019)
Kempe, D., Kleinberg, J., Tardos, E.: Maximizing the spread of influence through a social network. In: ACM SIGKDD, pp. 137–146 (2003)
Office of The Director of National Intelligence: Background to ‘Assessing Russian Activities and Intentions in Recent Elections’: The Analytic Process and Cyber Incident Attribution (2017)
Sina, S., Hazon, N., Hassidim, A., Kraus, S.: Adapting the social network to affect elections. In: AAMAS, pp. 705–713 (2015)
Wilder, B., Vorobeychik, Y.: Controlling elections through social influence. In: AAMAS, pp. 265–273 (2018)
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Castiglioni, M., Ferraioli, D., Gatti, N., Landriani, G. (2021). Election Manipulation on Social Networks with Messages on Multiple Candidates Extended Abstract. In: Heintz, F., Milano, M., O'Sullivan, B. (eds) Trustworthy AI - Integrating Learning, Optimization and Reasoning. TAILOR 2020. Lecture Notes in Computer Science(), vol 12641. Springer, Cham. https://doi.org/10.1007/978-3-030-73959-1_18
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DOI: https://doi.org/10.1007/978-3-030-73959-1_18
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