Conspiracy theories have become a prominent and concerning aspect of online discourse, posing challenges to information integrity and societal trust. As such, we address conspiracy theory detection as proposed by the ACTI @ EVALITA 2023 shared task. The combination of pre-trained sentence Transformer models and data augmentation techniques enabled us to secure first place in the final leaderboard of both sub-tasks. Our methodology attained F1 scores of 85.71% in the binary classification and 91.23% for the fine-grained conspiracy topic classification, surpassing other competing systems.

Sentiment analysis using big data from YouTube videos metadata can be conducted to analyze public opinions on various political figures who represent political parties. This is possible because YouTube has become one of the platforms for people to express themselves, including their opinions on various political figures. The resulting sentiment analysis can be useful for political executives to gain an understanding of public sentiment and develop appropriate and effective political strategies. This study aimed to build a sentiment analysis system leveraging YouTube videos metadata. The sentiment analysis system was built using Apache Kafka, Apache PySpark, and Hadoop for big data handling; TensorFlow for deep learning handling; and FastAPI for deployment on the server. The YouTube videos metadata used in this study is the video description. The sentiment analysis model was built using LSTM algorithm and produces two types of sentiments: positive and negative sentiments. The sentiment analysis results are then visualized in the form a simple web-based dashboard.

This paper presents a new decision support system offered for an in-depth analysis of semantic networks, which can provide insights for a better exploration of a brand's image and the improvement of its connectivity. In terms of network analysis, we show that this goal is achieved by solving an extended version of the Maximum Betweenness Improvement problem, which includes the possibility of considering adversarial nodes, constrained budgets, and weighted networks - where connectivity improvement can be obtained by adding links or increasing the weight of existing connections. We present this new system together with two case studies, also discussing its performance. Our tool and approach are useful both for network scholars and for supporting the strategic decision-making processes of marketing and communication managers.

Large language models (LLMs) have demonstrated human-level performance on a vast spectrum of natural language tasks. However, few studies have addressed the LLM threat and vulnerability from an ideology perspective, especially when they are increasingly being deployed in sensitive domains, e.g., elections and education. In this study, we explore the implications of GPT soft ideologization through the use of AI-self-consciousness. By utilizing GPT self-conversations, AI can be granted a vision to "comprehend" the intended ideology, and subsequently generate finetuning data for LLM ideology injection. When compared to traditional government ideology manipulation techniques, such as information censorship, LLM ideologization proves advantageous; it is easy to implement, cost-effective, and powerful, thus brimming with risks.

Spontaneous self-organization is ubiquitous in systems far from thermodynamic equilibrium. While organized structures that emerge dominate transport properties, universal representations that identify and describe these key objects remain elusive. Here, we introduce a theoretically-grounded framework for describing emergent organization that, via data-driven algorithms, is constructive in practice. Its building blocks are spacetime lightcones that embody how information propagates across a system through local interactions. We show that predictive equivalence classes of lightcones -- local causal states -- capture organized behaviors and coherent structures in complex spatiotemporal systems. Employing an unsupervised physics-informed machine learning algorithm and a high-performance computing implementation, we demonstrate automatically discovering coherent structures in two real world domain science problems. We show that local causal states identify vortices and track their power-law decay behavior in two-dimensional fluid turbulence. We then show how to detect and track familiar extreme weather events -- hurricanes and atmospheric rivers -- and discover other novel coherent structures associated with precipitation extremes in high-resolution climate data at the grid-cell level.

The increasing capabilities of large language models (LLMs) raise opportunities for artificial general intelligence but concurrently amplify safety concerns, such as potential misuse of AI systems, necessitating effective AI alignment. Reinforcement Learning from Human Feedback (RLHF) has emerged as a promising pathway towards AI alignment but brings forth challenges due to its complexity and dependence on a separate reward model. Direct Preference Optimization (DPO) has been proposed as an alternative, and it remains equivalent to RLHF under the reverse KL regularization constraint. This paper presents $f$-DPO, a generalized approach to DPO by incorporating diverse divergence constraints. We show that under certain $f$-divergences, including Jensen-Shannon divergence, forward KL divergences and $\alpha$-divergences, the complex relationship between the reward and optimal policy can also be simplified by addressing the Karush-Kuhn-Tucker conditions. This eliminates the need for estimating the normalizing constant in the Bradley-Terry model and enables a tractable mapping between the reward function and the optimal policy. Our approach optimizes LLMs to align with human preferences in a more efficient and supervised manner under a broad set of divergence constraints. Empirically, adopting these divergences ensures a balance between alignment performance and generation diversity. Importantly, $f$-DPO outperforms PPO-based methods in divergence efficiency, and divergence constraints directly influence expected calibration error (ECE).

Former U.S. Defense intel officer Rebekah Koffler discusses additional aid pledged to Ukraine and the U.S.'s decision to launch an unarmed ICBM in California. Last Friday, responding to questions about recent strikes on Crimea, Vice Prime Minister and Minister of Digital Transformation of Ukraine Mykhailo Fedorov, acknowledged, albeit indirectly, that Ukraine was behind them. He also warned that there will be more drone attacks on Russian warships. Drone warfare is a critical component to Ukrainian President Volodymyr Zelenskyy's new asymmetric strategy, likely intended to ensure that Ukrainian armed forces are able to stay in the fight, over the long run, even if they are unable to secure a clear military victory over their highly entrenched opponent. Zelenskyy probably calculates that by systematically employing small scale drone attacks, Ukraine may be able to frustrate, demoralize and exhaust the Russian forces and psychologically dislodge Russian civilians.

A landmark trial currently under way in Washington may well decide the future of the internet. In the dock is Google, the world's largest search engine. The United States Department of Justice has accused the search giant of muscling its way to dominance by paying other companies like Apple to be the default search engine on their devices. "Google pays billions of dollars each year to distributors -- including popular-device manufacturers such as Apple, LG, Motorola, and Samsung … to secure default status for its general search engine," the Justice Department's complaint says. This, the DOJ thinks, chokes off competition that includes other search engines like Microsoft's Bing, and privately held DuckDuckGo.

President Joe Biden's recent visit to India and Vietnam marked one of the administration's recent signals for economic and technological "de-risking" with China. The trip followed Biden's executive order, issued in August, that vowed to block U.S. venture capital and private equity investment in Chinese firms working on sensitive technologies such as semiconductors, artificial intelligence, and quantum computing. An era of global venture capital appears to be coming to a close. As Washington attempts to limit cross-border capital flows, however, America might be at risk of forfeiting its own access to Chinese technology and long-standing commitment to global investment. While the executive order is intended to be narrow and targeted at military acquisitions of key technology, it epitomizes a broader trend of increasing scrutiny on high-tech venture investment relationships between the U.S. and China. In a high-profile rebranding, Sequoia Capital recently spun off its highly successful China arm in June.

The death toll for Bahraini soldiers killed in an attack blamed on Yemen's Houthis has risen to three, Bahrain's state news agency said. Two Bahraini servicemen in Saudi Arabia were initially confirmed dead in Monday's drone attack, before a third soldier succumbed to his injuries on Wednesday, the Bahrain Defence Force said. The attack took place as the soldiers were patrolling Saudi Arabia's southern border with Yemen. "We mourn one of our men who was martyred today as a result of his serious injuries after the Houthi attack on the southern border of Saudi Arabia," the Bahrain Defence Force said on Wednesday, announcing the death of the third soldier. Bahrain's state agency identified the soldier as First Warrant Officer Adam Salem Naseeb, and said he had "valiantly gave his life in the line of duty".