Shockwavelike Behavior across Social Media

Manrique, Pedro D.; Huo, Frank Yingjie; Oud, Sara El; Min, Zheng; Illari, Lucia; Johnson, Neil F.

doi:10.1103/physrevlett.130.237401

Cited by 5 publications

(4 citation statements)

References 42 publications

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“…Empirical evidence of this was reported recently in Ref. ( 66 ). These fusion–fission dynamics mean that any bad-actor community generating content continually with basic AI such as GPT-2 will be able to quickly and widely spread this content while also increasing its connectivity into the vulnerable mainstream.…”

Section: Resultssupporting

confidence: 57%

“…coalescence) of nodes into clusters and infrequent fission (i.e. fragmentation) of such clusters ( 66 ). Empirical evidence of this was reported recently in Ref.…”

Section: Resultsmentioning

confidence: 99%

“…To set the scene for our discussions, we quickly review here what the general online ecosystem actually looks like and how we map it out. The global online population of several billion comprises a dynamical network of interlinking in-built social media communities ( 66 ) (e.g. a VKontakte Club; a Facebook Page; a Telegram Channel; a Gab Group).…”

Section: Introductionmentioning

confidence: 99%

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Controlling bad-actor-artificial intelligence activity at scale across online battlefields

Johnson,

Sear,

Illari

2023

PNAS Nexus

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We consider the looming threat of bad actors using artificial intelligence (AI)/Generative Pretrained Transformer to generate harms across social media globally. Guided by our detailed mapping of the online multiplatform battlefield, we offer answers to the key questions of what bad-actor-AI activity will likely dominate, where, when—and what might be done to control it at scale. Applying a dynamical Red Queen analysis from prior studies of cyber and automated algorithm attacks, predicts an escalation to daily bad-actor-AI activity by mid-2024—just ahead of United States and other global elections. We then use an exactly solvable mathematical model of the observed bad-actor community clustering dynamics, to build a Policy Matrix which quantifies the outcomes and trade-offs between two potentially desirable outcomes: containment of future bad-actor-AI activity vs. its complete removal. We also give explicit plug-and-play formulae for associated risk measures.

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Section: Resultssupporting

confidence: 57%

“…coalescence) of nodes into clusters and infrequent fission (i.e. fragmentation) of such clusters ( 66 ). Empirical evidence of this was reported recently in Ref.…”

Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Controlling bad-actor-artificial intelligence activity at scale across online battlefields

Johnson,

Sear,

Illari

2023

PNAS Nexus

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“…Despite the urgency, a thorough description of online information dynamics, which would be essential to inform the formulation of public policies, is missing. Now Pedro Manrique and colleagues at George Washington University in Washington, DC, have tackled one important aspect of such dynamics [1] (Fig. 1).…”

mentioning

confidence: 99%