--Foreign information operations conducted by Russian and Chinese actors exploit the United States' permissive information environment. These campaigns threaten democratic institutions and the broader Westphalian model. Y et, existing detection and mitigation strategies often fail to identify active information campaigns in real time. This paper introduces ChestyBot, a pragmatics-based language model that detects unlabeled foreign malign influence tweets with up to 98.34% accuracy. The model supports a novel framework to disrupt foreign influence operations in their formative stages. Foreign influence campaigns--particularly those attributed to Russia during the 2016 U.S. Presidential Election--demonstrated how state-sponsored social media operations can destabilize democratic societies [1]. During that campaign, social media posts emanating from one state - Russia - probably represented an intentional effort to influence the internal affairs of another country - the United States. Though these efforts may not have changed election outcomes, they nonetheless constitute an erosion of the Westphalian state model itself [2]. In recent years, China has attempted to use social media to influence foreign perceptions of internal matters such as the Beijing 2022 Winter Olympics, the origins of COVID-19, and the human rights abuses in Xinjiang [3]. Despite these initiatives, China has (as far as we can tell at the time of this writing) not performed a successful large-scale disinformation campaign directed against U.S. internal interests.

Russia is targeting the Paris Olympics with a disinformation campaign that includes deploying a deepfake Tom Cruise to narrate a documentary criticising the organisation behind the games, according to a new report from Microsoft. Microsoft said a network of Russia-affiliated groups are running "malign influence campaigns" against France, Emmanuel Macron, the International Olympic Committee (IOC) and the Paris Games with the event less than 80 days away. Russia has been banned from the 2024 Olympics, although a small number of Russian athletes may compete as neutrals. The fake Cruise video, which appeared on the Telegram messaging platform last year, is called Olympics Has Fallen and uses artificial intelligence-generated audio of the film star's voice to present a "strange, meandering script" disparaging the IOC. The documentary, whose title riffs on the Gerard Butler action film Olympus Has Fallen, also claims falsely to have been produced by Netflix and is promoted with bogus five-star reviews from the New York Times and the BBC.

Artificial intelligence company OpenAI has announced that it disrupted covert influence campaigns originating from Russia, China, Israel and Iran. The ChatGPT maker said on Thursday that it identified five campaigns involving "deceptive attempts to manipulate public opinion or influence political outcomes without revealing the true identity or intentions of the actors behind them". The campaigns used OpenAI's models to generate text and images that were posted across social media platforms such as Telegram, X, and Instagram, in some cases exploiting the tools to produce content with "fewer language errors than would have been possible for human operators," OpenAI said. Open AI said it terminated accounts associated with two Russian operations, dubbed Bad Grammer and Doppelganger; a Chinese campaign known as Spamouflage; an Iranian network called International Union of Virtual Media; and an Israeli operation dubbed Zero Zeno. "We are committed to developing safe and responsible AI, which involves designing our models with safety in mind and proactively intervening against malicious use," the California-based start-up said in a statement posted on its website.

Today, OpenAI released its first threat report, detailing how actors from Russia, Iran, China, and Israel have attempted to use its technology for foreign influence operations across the globe. The report named five different networks that OpenAI identified and shut down between 2023 and 2024. In the report, OpenAI reveals that established networks like Russia's Doppleganger and China's Spamoflauge are experimenting with how to use generative AI to automate their operations. And while it's a modest relief that these actors haven't mastered generative AI to become unstoppable forces for disinformation, it's clear that they're experimenting, and that alone should be worrying. The OpenAI report reveals that influence campaigns are running up against the limits of generative AI, which doesn't reliably produce good copy or code.

We propose a novel clustering pipeline to detect and characterize influence campaigns from documents. This approach clusters parts of document, detects clusters that likely reflect an influence campaign, and then identifies documents linked to an influence campaign via their association with the high-influence clusters. Our approach outperforms both the direct document-level classification and the direct document-level clustering approach in predicting if a document is part of an influence campaign. We propose various novel techniques to enhance our pipeline, including using an existing event factuality prediction system to obtain document parts, and aggregating multiple clustering experiments to improve the performance of both cluster and document classification. Classifying documents after clustering not only accurately extracts the parts of the documents that are relevant to influence campaigns, but also captures influence campaigns as a coordinated and holistic phenomenon. Our approach makes possible more fine-grained and interpretable characterizations of influence campaigns from documents.

The detection of state-sponsored trolls operating in influence campaigns on social media is a critical and unsolved challenge for the research community, which has significant implications beyond the online realm. To address this challenge, we propose a new AI-based solution that identifies troll accounts solely through behavioral cues associated with their sequences of sharing activity, encompassing both their actions and the feedback they receive from others. Our approach does not incorporate any textual content shared and consists of two steps: First, we leverage an LSTM-based classifier to determine whether account sequences belong to a state-sponsored troll or an organic, legitimate user. Second, we employ the classified sequences to calculate a metric named the "Troll Score", quantifying the degree to which an account exhibits troll-like behavior. To assess the effectiveness of our method, we examine its performance in the context of the 2016 Russian interference campaign during the U.S. Presidential election. Our experiments yield compelling results, demonstrating that our approach can identify account sequences with an AUC close to 99% and accurately differentiate between Russian trolls and organic users with an AUC of 91%. Notably, our behavioral-based approach holds a significant advantage in the ever-evolving landscape, where textual and linguistic properties can be easily mimicked by Large Language Models (LLMs): In contrast to existing language-based techniques, it relies on more challenging-to-replicate behavioral cues, ensuring greater resilience in identifying influence campaigns, especially given the potential increase in the usage of LLMs for generating inauthentic content. Finally, we assessed the generalizability of our solution to various entities driving different information operations and found promising results that will guide future research.

Last week, Amazon announced it was integrating AI into a number of products--including smart glasses, smart home systems, and its voice assistant, Alexa--that help users navigate the world. This week, Meta will unveil its latest AI and extended reality (XR) features, and next week Google will reveal its next line of Pixel phones equipped with Google AI. If you thought AI was already "revolutionary," just wait until it's part of the increasingly immersive responsive, personal devices that power our lives. AI is already hastening technology's trend toward greater immersion, blurring the boundaries between the physical and digital worlds and allowing users to easily create their own content. When combined with technologies like augmented or virtual reality, it will open up a world of creative possibilities, but also raise new issues related to privacy, manipulation, and safety.

Machine generated text is increasingly difficult to distinguish from human authored text. Powerful open-source models are freely available, and user-friendly tools that democratize access to generative models are proliferating. ChatGPT, which was released shortly after the first edition of this survey, epitomizes these trends. The great potential of state-of-the-art natural language generation (NLG) systems is tempered by the multitude of avenues for abuse. Detection of machine generated text is a key countermeasure for reducing abuse of NLG models, with significant technical challenges and numerous open problems. We provide a survey that includes both 1) an extensive analysis of threat models posed by contemporary NLG systems, and 2) the most complete review of machine generated text detection methods to date. This survey places machine generated text within its cybersecurity and social context, and provides strong guidance for future work addressing the most critical threat models, and ensuring detection systems themselves demonstrate trustworthiness through fairness, robustness, and accountability.

The first time Captain Kirk had a conversation with the ship's computer was in 1966 during Episode 13 of Season 1 in the classic Star Trek series. Calling it a "conversation" is quite generous, for it was really a series of stiff questions from Kirk, each prompting an even stiffer response from the computer. There was no conversational back-and-forth, no questions from the AI asking for elaboration or context. And yet, for the last 57 years, computer scientists have not been able to exceed this stilted 1960s vision of human-machine dialog. Even platforms like Siri and Alexa, created by some of the world's largest companies at great expense have not allowed for anything that feels like real-time natural conversation.

A new report released Monday reveals that the Internet Research Agency, the troll farm linked to Russian intelligence, actually increased its social media activity after the 2016 election. The report, which took seven months to complete and is the most comprehensive of its kind to date, comes from researchers at Oxford University and analytics firm Graphika. Their data shows the volume of IRA activity doubling between 2016 and 2017 on Facebook, Instagram, and Twitter, even as the number of ads purchased by the agency decreased. The amount of activity increased the most on Facebook-owned Instagram, where it more than doubled from 2,611 posts in 2016 to 5,956 posts in 2017. The research is based on Facebook data from 2015-2017, Twitter data from 2009-2018, and YouTube data from 2014-2018 that was provided by the companies to the Senate Intelligence Committee and relayed to the researchers.