The Disinformation Countermeasures and Machine Learning (DisCoML) workshop at ICML 2022 in Baltimore will address machine learning techniques to counter disinformation. Today, disinformation is an important challenge that all governments and their citizens face, affecting politics, public health, financial markets, and elections. Specific examples such as lynchings catalyzed by disinformation spread over social media highlight that the threat it poses crosses social scales and boundaries. This threat even extends into the realm of military combat, as a recent NATO StratCom experiment highlighted. Machine learning plays a central role in the production and propagation of dissemination. Bad actors scale disinformation operations by using ML-enabled bots, deepfakes, cloned websites, and forgeries. The situation is exacerbated by proprietary algorithms of search engines and social media platforms, driven by advertising models, that can effectively isolate internet users from alternative information and viewpoints. In fact, social media's business model, with its behavioral tracking algorithms, is arguably optimized for launching a global pandemic of cognitive hacking. Machine learning is also essential for identifying and inhibiting the spread of disinformation at internet speed and scale, but DisCoML welcomes approaches that contribute to countering disinformation in a broad sense. While the "cybersecurity paradox"–i.e. increased technology spending has not equated to an improved security posture–also applies to disinformation and indicates the need to address human behavior, there is an arms race quality to both problems. This suggests that technology, and ML in particular, will play a central role in countering disinformation well into the future. DisCoML will provide a forum for bringing leading researchers together and enabling stakeholders and policymakers to get up to date on the latest developments in the field.
Sat 6:00 a.m. - 6:10 a.m.
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Opening Remarks
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Opening remarks
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Sat 6:10 a.m. - 6:40 a.m.
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The Need for Intentions Behind Disinformation
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Talk
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SlidesLive Video » |
Eugene Santos 🔗 |
Sat 6:40 a.m. - 7:00 a.m.
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Networked Restless Bandits with Positive Externalities
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Talk
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SlidesLive Video » Many user- and article-level interventions have been proposed to combat the dissemination of misinformation over social networks, including inoculation (exposing users to weaker forms of misinformation), shifting user attention to accuracy, publicizing consensus, fact-checking, using source credibility (while controlling for political polarization), and flagging or providing warnings about an article. Cost pressures and user-engagement risks typically preclude the widespread application of such interventions and motivate a constrained resource allocation-based approach. Restless bandits have been used to model such allocation problems; however, prior work assumes that individual arms only benefit if they receive the resource directly. We note that misinformation-related intervention allocation tasks occur within communities and may be characterized by externalities that allow arms to derive partial benefit when their neighbor(s) receive the resource. We introduce networked restless bandits, a novel multi-armed bandit setting in which arms are both restless and embedded within a directed graph. We then present Greta, a graph-aware, Whittle index-based heuristic algorithm that can be used to efficiently construct a constrained reward-maximizing action vector at each timestep. |
Christine Herlihy 🔗 |
Sat 7:00 a.m. - 7:30 a.m.
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Break
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Sat 7:30 a.m. - 8:00 a.m.
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TBD
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Talk
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SlidesLive Video » |
Ceren Budak 🔗 |
Sat 8:00 a.m. - 8:40 a.m.
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Disrupting Disinformation
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Talk
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SlidesLive Video » |
Hany Farid 🔗 |
Sat 8:40 a.m. - 9:10 a.m.
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Disinformation in the Russia-Ukraine War
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Panel Discussion
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SlidesLive Video » |
Andrii Shapovalov · Ludmilla Huntsman 🔗 |
Sat 9:10 a.m. - 10:00 a.m.
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Lunch break
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Sat 10:00 a.m. - 10:30 a.m.
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Defense against Disinformation on Social Media and Its Challenges
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Talk
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SlidesLive Video » |
Huan Liu 🔗 |
Sat 10:30 a.m. - 11:00 a.m.
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TBD
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Talk
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SlidesLive Video » |
V.S. Subrahmanian 🔗 |
Sat 11:00 a.m. - 11:20 a.m.
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Multilingual Disinformation Detection for Digital Advertising
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Talk
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SlidesLive Video » In today's world, the spread of disinformation and propaganda online is more widespread than ever. Most of the publisher's revenue comes from advertising, therefore placing ads on these web pages directly funds the publisher, which has been brought under scrutiny by various media. The question of how to remove the publishers from advertising inventory has long been ignored, despite the negative consequences on the open internet. In this work, we make the first step to quickly detect and red-flag the publishers that potentially manipulate the public with disinformation or falsehoods. We build a machine learning model based on multilingual text embeddings that first detects the topic of interest and then estimates the likelihood of the page being malicious. Our systems empower internal teams to proactively, rather than defensively, blacklist unsafe content. |
Maryline CHEN 🔗 |
Sat 11:20 a.m. - 12:00 p.m.
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Progress, Problems, and Prospects for Countering Disinformation Using ML
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Panel Discussion
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SlidesLive Video » |
Hany Farid · Eugene Santos · Rand Waltzman · Anatolii Marushchak · George Cybenko 🔗 |
Sat 12:00 p.m. - 12:30 p.m.
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Break
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Sat 12:30 p.m. - 1:00 p.m.
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Learning News Outlet Veracity Using Relationship Graphs
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Talk
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SlidesLive Video » |
Ben Horne 🔗 |
Sat 1:00 p.m. - 1:30 p.m.
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Early Detection of Fake News on Social Media Through Propagation Path Classification
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Talk
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SlidesLive Video » |
Yang Liu 🔗 |
Sat 1:30 p.m. - 1:50 p.m.
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Privacy, Security, and Obfuscation in Reporting Technologies
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Talk
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Crowdsourced reporting technologies rely on the public to provide critical information for public decision-making. This work examines security, privacy, and obfuscation in the context of reporting technologies. We show that widespread use of reporting platforms comes with unique security and privacy implications, and introduce a parallel threat model and corresponding taxonomy to outline some of the many attack vectors in this space. We then perform an empirical analysis of a dataset of call logs from a controversial, real-world reporting hotline and identify coordinated obfuscation strategies that are intended to hinder the platform’s legitimacy. We propose a variety of statistical measures to quantify the strength of this obfuscation strategy with respect to the structural and semantic characteristics of the reporting attacks in our dataset. |
Benjamin Laufer 🔗 |
Sat 1:50 p.m. - 2:20 p.m.
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TBA
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Invited Talk
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Evanna Hu 🔗 |
Sat 2:20 p.m. - 2:50 p.m.
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TBA
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Invited Talk
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SlidesLive Video » |
J.D. Maddox 🔗 |
Sat 2:50 p.m. - 3:00 p.m.
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Closing Remarks
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Closing remarks
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George Cybenko 🔗 |