The use of cryptocurrency in sales of human beings for prostitution and scam compounds nearly doubled in 2025, according to a conservative estimate. Many of the deals are happening in plain sight. Cryptocurrency's frictionless, transnational, low-regulation transactions have long promised the ability to pay anyone in the world for anything. More than ever before, that anything includes human beings: victims of human trafficking forced into scam compounds and the sex trade on an industrial scale, bought and sold in crypto deals carried out with impunity, often in full public view. In new research published today, crypto-tracing firm Chainalysis found that crypto-funded transactions for human trafficking--largely forced laborers trapped in compounds across Southeast Asia and coerced into working as online scammers, as well as sex-trafficking prostitution rings--grew explosively in 2025.

In a number of instances, investigation showed that while one Telegram channel had been shut down, another with a near-identical name remained active. In a number of instances, investigation showed that while one Telegram channel had been shut down, another with a near-identical name remained active. Millions of people around the world are creating and sharing deepfake nudes on the secure messaging app Telegram, a Guardian analysis has shown, as the spread of advanced AI tools industrialises the online abuse of women. The Guardian has identified at least 150 Telegram channels - large encrypted group chats popular for their secure communication - that appear to have users in many countries, from the UK to Brazil, China to Nigeria, Russia to India. Some of them offer "nudified" photos or videos for a fee: users can upload a photo of any woman, and AI will produce a video of that woman performing sexual acts.

In a world where ICE agents are shooting US citizens on the street, the need for militias and extremist groups like the Proud Boys to support far-right interests has evaporated. Whether it was protesting Covid lockdowns, attending school board meetings, or facing off against Black Lives Matter protesters, the far-right Proud Boys were always on hand to support Donald Trump's first term in office. When Trump left office in 2021, the group's leaders languished in jail for their role in the January 6 attack on the Capitol. With reported infighting destabilizing the movement, it looked like the group's glory days were behind it. But Trump's return a year ago, and his release of all January 6 prisoners, signaled that a Proud Boy comeback could be in the cards.

Capable of creating "nearly perfect" face swaps during live video chats, Hoatian has made millions, mainly via Telegram. But its main channel vanished after WIRED's inquiry into scammers using the app. The Chinese-language artificial intelligence app Haotian is so effective that it's made millions of dollars selling its face-swapping technology on Telegram . The service integrates easily with messaging platforms like WhatsApp and WeChat and claims that users can tweak up to 50 settings--including the ability to adjust things like cheekbone size and eye position--to help mimic the face they are impersonating. But while Haotian is a robust and versatile platform, researchers and WIRED's own analysis have found that the service has been marketing to so-called "pig butchering" scammers and those running online fraud operations in Southeast Asia.

Cyber Threat Intelligence (CTI) has become indispensable for security analysts, enabling them to identify, collect, manage, and disseminate information on vulnerabilities and attacks, and to respond proactively to emerging threats [6]. Within the CTI lifecycle, data collection encompassing sources such as security alerts and threat intelligence reports from the web represents a critical foundational stage [3]. In this context, one challenge is that not all threat intelligence is published in standard CTI databases or integrated into commercial security platforms. V aluable CTI is often disseminated through unstructured channels such as blogs, social media posts, or reports from security companies and independent experts. To capture these dispersed insights, multiple online sources can be leveraged as early signals of emerging cyber threats. Information gathering thus becomes the first and most critical step, enabling the collection of relevant data on newly discovered vulnerabilities, active exploits, security alerts, threat intelligence reports, and security tool configurations. Curating CTI datasets requires addressing key challenges, including data sourcing from heterogeneous streams, ensuring data reliability, preserving privacy, and mitigating bias. A well-designed CTI dataset not only accelerates the advancement of automated threat intelligence systems but also strengthens global cyber defense capabilities through knowledge sharing and standardized evaluation frameworks. While platforms like Twitter [20] have been widely explored for their CTI potential, other communication ecosystems remain underexamined.

Overnight Russian drone attacks on east, south and central Ukraine damaged civilian infrastructure and businesses in the Poltava region and injured civilians in the Odesa region, Ukrainian officials said early on Wednesday. Odesa came under a "massive attack" by Russian drones overnight on Tuesday, wounding at least three people, the head of the regional administration, Oleh Kiper, wrote on his Telegram page. A residential building in a densely populated urban area of Odesa, civilian infrastructure and an educational facility were hit, he said. Air defence units repelled Russian air attacks on the Kyiv region and Ukraine's second largest city of Kharkiv, regional governors said in posts on Telegram channels. Russian forces said they have retaken St Nicholas Belogorsky monastery in the village of Gornal in Russia's Kursk region, where Ukrainian troops had been based, Russia's TASS news agency quoted a security source as saying.

Telegram is one of the most used instant messaging apps worldwide. Some of its success lies in providing high privacy protection and social network features like the channels -- virtual rooms in which only the admins can post and broadcast messages to all its subscribers. However, these same features contributed to the emergence of borderline activities and, as is common with Online Social Networks, the heavy presence of fake accounts. Telegram started to address these issues by introducing the verified and scam marks for the channels. Unfortunately, the problem is far from being solved. In this work, we perform a large-scale analysis of Telegram by collecting 35,382 different channels and over 130,000,000 messages. We study the channels that Telegram marks as verified or scam, highlighting analogies and differences. Then, we move to the unmarked channels. Here, we find some of the infamous activities also present on privacy-preserving services of the Dark Web, such as carding, sharing of illegal adult and copyright protected content. In addition, we identify and analyze two other types of channels: the clones and the fakes. Clones are channels that publish the exact content of another channel to gain subscribers and promote services. Instead, fakes are channels that attempt to impersonate celebrities or well-known services. Fakes are hard to identify even by the most advanced users. To detect the fake channels automatically, we propose a machine learning model that is able to identify them with an accuracy of 86%. Lastly, we study Sabmyk, a conspiracy theory that exploited fakes and clones to spread quickly on the platform reaching over 1,000,000 users.

The paper proposes an advanced approach for identifying disinformation on Telegram channels related to the Russo-Ukrainian conflict, utilizing state-of-the-art (SOTA) deep learning techniques and transfer learning. Traditional methods of disinformation detection, often relying on manual verification or rule-based systems, are increasingly inadequate in the face of rapidly evolving propaganda tactics and the massive volume of data generated daily. To address these challenges, the proposed system employs deep learning algorithms, including LLM models, which are fine-tuned on a custom dataset encompassing verified disinformation and legitimate content. The paper's findings indicate that this approach significantly outperforms traditional machine learning techniques, offering enhanced contextual understanding and adaptability to emerging disinformation strategies.

Telegram is a popular platform for public communication, generating large amounts of messages through its channels. The library offers key features such as easy message retrieval, detailed channel information, engagement metrics, and topic identification using advanced modeling techniques. By simplifying data extraction and analysis, pytopicgram allows users to understand how content spreads and how audiences interact on Telegram. This paper describes the design, main features, and practical uses of pytopicgram, showcasing its effectiveness for studying public conversations on Telegram. Messaging platforms like Telegram have become critical spaces for information exchange, social mobilization, and digital communities. With features such as public channels, unlimited subscribers, and a degree of anonymity, Telegram has emerged as a valuable source of unstructured data reflecting various social, political, and cultural dynamics [1].

The Israeli-Palestinian conflict started on 7 October 2023, have resulted thus far to over 48,000 people killed including more than 17,000 children with a majority from Gaza, more than 30,000 people injured, over 10,000 missing, and over 1 million people displaced, fleeing conflict zones. The infrastructure damage includes the 87\% of housing units, 80\% of public buildings and 60\% of cropland 17 out of 36 hospitals, 68\% of road networks and 87\% of school buildings damaged. This conflict has as well launched an online discussion across various social media platforms. Telegram was no exception due to its encrypted communication and highly involved audience. The current study will cover an analysis of the related discussion in relation to different participants of the conflict and sentiment represented in those discussion. To this end, we prepared a dataset of 125K messages shared on channels in Telegram spanning from 23 October 2025 until today. Additionally, we apply the same analysis in two publicly available datasets from Twitter containing 2001 tweets and from Reddit containing 2M opinions. We apply a volume analysis across the three datasets, entity extraction and then proceed to BERT topic analysis in order to extract common themes or topics. Next, we apply sentiment analysis to analyze the emotional tone of the discussions. Our findings hint at polarized narratives as the hallmark of how political factions and outsiders mold public opinion. We also analyze the sentiment-topic prevalence relationship, detailing the trends that may show manipulation and attempts of propaganda by the involved parties. This will give a better understanding of the online discourse on the Israel-Palestine conflict and contribute to the knowledge on the dynamics of social media communication during geopolitical crises.