Comparative studies of news coverage are challenging to conduct because methods to identify news articles about the same event in different languages require expertise that is difficult to scale. We introduce an AI-powered method for identifying news articles based on an event FINGERPRINT, which is a minimal set of metadata required to identify critical events. Our event coverage identification method, FINGERPRINT TO ARTICLE MATCHING FOR EVENTS (FAME), efficiently identifies news articles about critical world events, specifically terrorist attacks and several types of natural disasters. FAME does not require training data and is able to automatically and efficiently identify news articles that discuss an event given its fingerprint: time, location, and class (such as storm or flood). The method achieves state-of-the-art performance and scales to massive databases of tens of millions of news articles and hundreds of events happening globally. We use FAME to identify 27,441 articles that cover 470 natural disaster and terrorist attack events that happened in 2020. To this end, we use a massive database of news articles in three languages from MediaCloud, and three widely used, expert-curated databases of critical events: EM-DAT, USGS, and GTD. Our case study reveals patterns consistent with prior literature: coverage of disasters and terrorist attacks correlates to death counts, to the GDP of a country where the event occurs, and to trade volume between the reporting country and the country where the event occurred. We share our NLP annotations and cross-country media attention data to support the efforts of researchers and media monitoring organizations.

NATO member Turkey on Thursday carried out a second day of aerial attacks on what it said are Kurdish militant positions in Iraq and Syria, following a terrorist attack on a state-run defense agency this week in which five people were killed. Turkey's National Intelligence Organization reportedly targeted numerous "strategic locations" allegedly used by the Kurdistan Workers' Party (PKK) – which was deemed a terrorist organization in the U.S. in 1997 – as well as targets used by Syrian Kurdish militia affiliated with the militant group. Armed drones were used to hit military, intelligence, energy and infrastructure facilities and ammunition depots, The Associated Press reported. Smoke rises as emergency rescue teams and police officers attend outside Turkish Aerospace Industries Inc. on the outskirts of Ankara, Turkey, on Wednesday, Oct. 23, 2024. However, according to General Commander Mazloum Abdi of the Syrian Democratic Forces (SDF), who is Kurdish, the Turkish attacks have been "indiscriminate" and have targeted civilian areas and health centers.

After Apple's product launch event this week, WIRED did a deep dive on the company's new secure server environment, known as Private Cloud Compute, which attempts to replicate in the cloud the security and privacy of processing data locally on users' individual devices. The goal is to minimize possible exposure of data processed for Apple Intelligence, the company's new AI platform. In addition to hearing about PCC from Apple's senior vice president of software engineering, Craig Federighi, WIRED readers also received a first look at content generated by Apple Intelligence's "Image Playground" feature as part of crucial updates on the recent birthday of Federighi's dog Bailey. Turning to privacy protection of a very different kind in another new AI service, WIRED looked at how users of the social media platform X can keep their data from being slurped up by the "unhinged" generative AI tool from xAI known as Grok AI. And in other news about Apple products, researchers developed a technique for using eye tracking to discern passwords and PINs people typed using 3D Apple Vision Pro avatars--a sort of keylogger for mixed reality.

We introduce event-keyed summarization (EKS), a novel task that marries traditional summarization and document-level event extraction, with the goal of generating a contextualized summary for a specific event, given a document and an extracted event structure. We introduce a dataset for this task, MUCSUM, consisting of summaries of all events in the classic MUC-4 dataset, along with a set of baselines that comprises both pretrained LM standards in the summarization literature, as well as larger frontier models. We show that ablations that reduce EKS to traditional summarization or structure-to-text yield inferior summaries of target events and that MUCSUM is a robust benchmark for this task. Lastly, we conduct a human evaluation of both reference and model summaries, and provide some detailed analysis of the results.

The United States military announced on Sunday that three US soldiers were killed and at least 34 were wounded in a drone attack targeting Tower 22, a remote logistics outpost near the Jordan-Syrian border. The attack has elicited a strong reaction from Washington with President Joe Biden pledging to hold the attackers to account. The Islamic Resistance in Iraq, an umbrella group of Iran-backed armed groups in the region, claimed the attacks, saying it was in response to US support to Israel's war on Gaza, which has killed more than 26,000 people. Tower 22, which houses a small US logistics outpost, is located in Jordan's northeast close to the borders with Iraq and Syria. Public information about the outpost is limited.

Sen. McConnell supports President Biden's authority for Iran airstrikes, urging stronger action against terrorist threats. President Biden's administration warned Iran of an impending terrorist attack prior to a blast that killed 94 people in early January, a U.S. official tells Fox News Digital. The bombing attack took place at a memorial ceremony for Iranian General Qasem Soleimani, who was killed by a U.S. drone strike on Jan. 3, 2020 under former President Trump's administration. The U.S. official did not detail Iran's response to the warning. "Prior to ISIS' terrorist attack on January 3, 2024, in Kerman, Iran, the U.S. Government provided Iran with a private warning that there was a terrorist threat within Iranian borders," the official told Fox. "The U.S. Government followed a longstanding'duty to warn' policy that has been implemented across administrations to warn governments against potential lethal threats. We provide these warnings in part because we do not want to see innocent lives lost in terror attacks," the official added.

On Tuesday morning, at least eight attack drones entered Moscow's airspace before being shot down by the city's air defences, a few hitting residential buildings on the way down. The Russian government accused Ukraine of a "terrorist attack", which Kyiv officials wryly denied. "You know, we are being drawn into the era of artificial intelligence. Perhaps not all drones are ready to attack Ukraine and want to return to their creators and ask them questions like: 'Why are you sending us [to hit] the children of Ukraine? In Kyiv?'" Ukrainian presidential adviser Mykhailo Podolyak said on the YouTube breakfast show of exiled Russian journalist Alexander Plushev.

An apparent drone attack on the Kremlin this week has sparked fears of an escalation in Russia's brutal war in Ukraine. On Wednesday night, two remotely-operated devices flew towards the domed roof of the Kremlin before being shot down by Russian air defences, exploding but harming no one. After the incident, Moscow Mayor Sergey Sobyanin declared that flying drones by private citizens was now banned in Moscow. Russia said the United States masterminded the attack, claiming Ukraine carried it out. Washington and Kyiv have denied responsibility, insisting that Ukraine's war efforts are purely defensive.

President Joe Biden's surprise announcement Monday night--that a U.S. drone strike over the weekend killed Ayman al-Zawahiri, leader of al-Qaida and co-architect of the 9/11 terrorist attack--is both more and less significant than it might seem at first glance. On the one hand, mainly because of the West's counter-terrorism strategies, al-Qaida is far from the potent global force that it was a decade ago. Its presence has been muted, and Zawahiri himself has hidden so far out of sight that one prominent expert speculated back in November that he might have been killed already. On the other hand, one fact about this drone strike hints at a much larger finding: It took place in Afghanistan. It turns out Zawahiri was living with his family in a large safehouse in downtown Kabul--meaning he had to be there with the Taliban's full blessing. This means that, contrary to the Taliban's assurances, they have been plotting a revival of their alliance with al-Qaida--the alliance that Osama bin Laden formed at the turn of the century and that spawned the attack on the World Trade Center.

Terrorism is a major problem worldwide, causing thousands of fatalities and billions of dollars in damage every year. Toward the end of better understanding and mitigating these attacks, we present a set of machine learning models that learn from localized news data in order to predict whether a terrorist attack will occur on a given calendar date and in a given state. The best model--a Random Forest that learns from a novel variable-length moving average representation of the feature space--achieves area under the receiver operating characteristic scores $> .667$ on four of the five states that were impacted most by terrorism between 2015 and 2018. Our key findings include that modeling terrorism as a set of independent events, rather than as a continuous process, is a fruitful approach--especially when the events are sparse and dissimilar. Additionally, our results highlight the need for localized models that account for differences between locations. From a machine learning perspective, we found that the Random Forest model outperformed several deep models on our multimodal, noisy, and imbalanced data set, thus demonstrating the efficacy of our novel feature representation method in such a context. We also show that its predictions are relatively robust to time gaps between attacks and observed characteristics of the attacks. Finally, we analyze factors that limit model performance, which include a noisy feature space and small amount of available data. These contributions provide an important foundation for the use of machine learning in efforts against terrorism in the United States and beyond.