Axios
There is a clear and growing link between Russian propaganda and online far-right extremism globally, according to a new study from researchers at the George Washington University.
Science of online ‘anti-X’ and a scalable solution: From vaccine misinformation to extremism
Keynote talk at the International Workshop on Cyber Social Threats (CySoc) June 2022, Atlanta, GA: https://cysoc2022.github.io/
Using Neural Architectures to Model Complex Dynamical Systems
Advances in Artificial Intelligence and Machine Learning
The natural, physical and social worlds abound with feedback processes that make the challenge of modeling the underlying system an extremely complex one. This paper proposes an end-to-end deep learning approach to modelling such so-called complex systems which addresses two problems: (1) scientific model discovery when we have only incomplete/partial knowledge of system dynamics; (2) integration of graph-structured data into scientific machine learning (SciML) using graph neural networks. It is well known that deep learning (DL) has had remarkable success in leveraging large amounts of unstructured data into downstream tasks such as clustering, classification, and regression. Recently, the development of graph neural networks has extended DL techniques to graph structured data of complex systems. However, DL methods still appear largely disjointed with established scientific knowledge, and the contribution to basic science is not always apparent. This disconnect has spurred the development of physics-informed deep learning, and more generally, the emerging discipline of SciML. Modelling complex systems in the physical, biological, and social sciences within the SciML framework requires further considerations. We argue the need to consider heterogeneous, graph-structured data as well as the effective scale at which we can observe system dynamics. Our proposal would open up a joint approach to the previously distinct fields of graph representation learning and SciML.
Nicholas Gabriel, Neil F. Johnson
Softening online extremes organically and at scale
Calls are escalating for social media platforms to do more to mitigate extreme online communities whose views can lead to real-world harms, e.g., mis/disinformation and distrust that increased Covid-19 fatalities, and now extend to monkeypox, unsafe baby formula alternatives, cancer, abortions, and climate change; white replacement that inspired the 2022 Buffalo shooter and will likely inspire others; anger that threatens elections, e.g., 2021 U.S. Capitol attack; notions of male supremacy that encourage abuse of women; anti-Semitism, anti-LGBQT hate and QAnon conspiracies. But should ‘doing more’ mean doing more of the same, or something different? If so, what? Here we start by showing why platforms doing more of the same will not solve the problem. Specifically, our analysis of nearly 100 million Facebook users entangled over vaccines and now Covid and beyond, shows that the extreme communities’ ecology has a hidden resilience to Facebook’s removal interventions; that Facebook’s messaging interventions are missing key audience sectors and getting ridiculed; that a key piece of these online extremes’ narratives is being mislabeled as incorrect science; and that the threat of censorship is inciting the creation of parallel presences on other platforms with potentially broader audiences. We then demonstrate empirically a new solution that can soften online extremes organically without having to censor or remove communities or their content, or check or correct facts, or promote any preventative messaging, or seek a consensus. This solution can be automated at scale across social media platforms quickly and with minimal cost.
Elvira Maria Restrepo, Martin Moreno, Lucia Illari, Neil F. Johnson
New Science to tackle Misinformation and Disinformation at Scale
- Keynote presentation at the American Physical Society’s 2022 Annual Meeting January 2022, Virtual: https://leadership.aps.org/
- Keynote presentation at the American Association for the Advancement of Science’s 2022 Annual Meeting February 2022, Virtual: https://aaas.confex.com/aaas/2022/meetingapp.cgi
- Keynote presentation at The National Academies for Sciences, Engineering, and Medicine’s 2022 Strategic Council April 2022, Virtual: https://www.nationalacademies.org/event/04-21-2022/the-strategic-council-for-research-excellence-integrity-and-trust-meeting-3
Machine Learning Reveals Adaptive COVID-19 Narratives in Online Anti-Vaccination Network
Proceedings of the 2021 Conference of The Computational Social Science Society of the Americas
The COVID-19 pandemic sparked an online “infodemic” of potentially dangerous misinformation. We use machine learning to quantify COVID-19 content from opponents of establishment health guidance, in particular vaccination. We quantify this content in two different ways: number of topics and evolution of keywords. We find that, even in the early stages of the pandemic, the anti-vaccination community had the infrastructure to more effectively garner support than their pro-vaccination counterparts by exhibiting a broader array of discussion topics. This provided an advantage in terms of attracting new users seeking COVID-19 guidance online. We also find that our machine learning framework can pick up on the adaptive nature of discussions within the anti-vaccination community, tracking distrust of authorities, opposition to lockdown orders, and an interest in early vaccine trials. Our approach is scalable and hence tackles the urgent problem facing social media platforms of having to analyze huge volumes of online health misinformation. With vaccine booster shots being approved and vaccination rates stagnating, such an automated approach is key in understanding how to combat the misinformation that slows the eradication of the pandemic.
Richard Sear, Rhys Leahy, Nicholas Johnson Restrepo, Yonatan Lupu, Neil Johnson
Dynamic Latent Dirichlet Allocation Tracks Evolution of Online Hate Topics
Advances in Artificial Intelligence and Machine Learning
Not only can online hate content spread easily between social media platforms, but its focus can also evolve over time. Machine learning and other artificial intelligence (AI) tools could play a key role in helping human moderators understand how such hate topics are evolving online. Latent Dirichlet Allocation (LDA) has been shown to be able to identify hate topics from a corpus of text associated with online communities that promote hate. However, applying LDA to each day’s data is impractical since the inferred topic list from the optimization can change abruptly from day to day, even though the underlying text and hence topics do not typically change this quickly. Hence, LDA is not well suited to capture the way in which hate topics evolve and morph. Here we solve this problem by showing that a dynamic version of LDA can help capture this evolution of topics surrounding online hate. Specifically, we show how standard and dynamical LDA models can be used in conjunction to analyze the topics over time emerging from extremist communities across multiple moderated and unmoderated social media platforms. Our dataset comprises material that we have gathered from hate-related communities on Facebook, Telegram, and Gab during the time period January-April 2021. We demonstrate the ability of dynamic LDA to shed light on how hate groups use different platforms in order to propagate their cause and interests across the online multiverse of social media platforms.
Richard Sear, Rhys Leahy, Nicholas Johnson Restrepo, Yonatan Lupu, Neil F. Johnson
Facebook Parenting Groups Are The New Target For Extremist Misinformation
Forbes
Any parent knows that Facebook mom groups are a confusing mix of support, criticism and downright bad advice. But they were still mainstream communities where parents could find connection around what can often be the lonely job of raising kids.
Preventing the Spread of Online Harms: Physics of Contagion across Multi-Platform Social Media and Metaverses
We present a minimal yet empirically-grounded theory for the spread of online harms (e.g. misinformation, hate) across current multi-platform social media and future Metaverses. New physics emerges from the interplay between the intrinsic heterogeneity among online communities and platforms, their clustering dynamics generated through user-created links and sudden moderator shutdowns, and the contagion process. The theory provides an online `R-nought’ criterion to prevent system-wide spreading; it predicts re-entrant spreading phases; it establishes the level of digital vaccination required for online herd immunity; and it can be applied at multiple scales.
Chen Xu, Pak Ming Hui, Om K. Jha, Chenkai Xia, Neil F. Johnson
How Social Media Machinery Pulled Mainstream Parenting Communities Closer to Extremes and Their Misinformation During Covid-19
IEEE
We reveal hidden social media machinery that has allowed misinformation to thrive among mainstream users, but which is missing from current policy discussions. Specifically, we show how mainstream parenting communities on Facebook have been subject to a powerful, two-pronged misinformation machinery during the pandemic, that has pulled them closer to extreme communities and their misinformation. The first prong involves a strengthening of the bond between mainstream parenting communities and pre-Covid conspiracy theory communities that promote misinformation about climate change, fluoride, chemtrails and 5G. Alternative health communities have acted as the critical conduits. The second prong features an adjacent core of tightly bonded, yet largely under-the-radar, anti-vaccination communities that continually supplied Covid-19 and vaccine misinformation to the mainstream parenting communities. Our findings show why Facebook’s own efforts to post reliable information about vaccines and Covid-19 have not been efficient; why targeting the largest communities does not work; and how this machinery could generate new pieces of misinformation perpetually. We provide a simple yet exactly solvable mathematical theory for the system’s dynamics. It predicts a new strategy for controlling mainstream community tipping points. Our conclusions should be applicable to any social media platform with in-built community features, and open up a new engineering approach to addressing online misinformation and other harms at scale.
Nicholas J. Restrepo, Lucia Illari, Rhys Leahy, Richard Sear, Yonatan Lupu, Neil F. Johnson