Political hazard: misinformation in the 2019 Indian general election campaign

Akbar, Syeda Zainab; Panda, Anmol; Pal, Joyojeet

doi:10.1080/19472498.2022.2095596

Cited by 2 publications

(1 citation statement)

References 38 publications

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“…In the 2017 UK general election, the Labour Party used bots to spread electoral messages on Tinder 4 , while the incumbent candidate Jair Bolsonaro massively used social media to spread targeted information in the 2018 Brazilian presidential election 8 . Both the ruling Bharatiya Janata Party and the Indian National Congress widely used online misinformation in their campaign strategies in the 2019 Indian general election 9 . Last but not least, false claims of democrats stealing the election were disseminated by the incumbent candidate Donald Trump and his supporters, following the 2020 US presidential election.…”

Section: Introductionmentioning

confidence: 99%

On the robustness of democratic electoral processes to computational propaganda

Givi,

Delabays,

Jacquemet

et al. 2024

Sci Rep

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There is growing evidence of systematic attempts to influence democratic elections by controlled and digitally organized dissemination of fake news. This raises the question of the intrinsic robustness of democratic electoral processes against external influences. Particularly interesting is to identify the social characteristics of a voter population that renders it more resilient against opinion manipulation. Equally important is to determine which of the existing democratic electoral systems is more robust to external influences. Here we construct a mathematical electoral model to address these two questions. We find that, not unexpectedly, biased electorates with clear-cut elections are overall quite resilient against opinion manipulations, because inverting the election outcome requires to change the opinion of many voters. More interesting are unbiased or weakly biased electorates with close elections. We find that such populations are more resilient against opinion manipulations (i) if they are less polarized and (ii) when voters interact more with each other, regardless of their opinion differences, and that (iii) electoral systems based on proportional representation are generally the most robust. Our model qualitatively captures the volatility of the US House of Representatives elections. We take this as a solid validation of our approach.

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Section: Introductionmentioning

confidence: 99%