public reaction
Interpreting Public Sentiment in Diplomacy Events: A Counterfactual Analysis Framework Using Large Language Models
Diplomatic events consistently prompt widespread public discussion and debate. Public sentiment plays a critical role in diplomacy, as a good sentiment provides vital support for policy implementation, helps resolve international issues, and shapes a nation's international image. Traditional methods for gauging public sentiment, such as large-scale surveys or manual content analysis of media, are typically time-consuming, labor-intensive, and lack the capacity for forward-looking analysis. We propose a novel framework that identifies specific modifications for diplomatic event narratives to shift public sentiment from negative to neutral or positive. First, we train a language model to predict public reaction towards diplomatic events. To this end, we construct a dataset comprising descriptions of diplomatic events and their associated public discussions. Second, guided by communication theories and in collaboration with domain experts, we predetermined several textual features for modification, ensuring that any alterations changed the event's narrative framing while preserving its core facts.We develop a counterfactual generation algorithm that employs a large language model to systematically produce modified versions of an original text. The results show that this framework successfully shifted public sentiment to a more favorable state with a 70\% success rate. This framework can therefore serve as a practical tool for diplomats, policymakers, and communication specialists, offering data-driven insights on how to frame diplomatic initiatives or report on events to foster a more desirable public sentiment.
- Asia > North Korea (0.14)
- Asia > China (0.05)
- Asia > India (0.04)
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- Media > News (1.00)
- Health & Medicine (1.00)
- Government > Foreign Policy (1.00)
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- Information Technology > Data Science (1.00)
- Information Technology > Communications > Social Media (1.00)
- Information Technology > Artificial Intelligence > Natural Language > Large Language Model (1.00)
- Information Technology > Artificial Intelligence > Machine Learning > Neural Networks > Deep Learning (1.00)
Exploring the topics, sentiments and hate speech in the Spanish information environment
LOPEZ, ALEJANDRO BUITRAGO, Pastor-Galindo, Javier, Ruipérez-Valiente, José Antonio
In societies valuing freedom of expression, individuals now frequently express and share their opinions, integrating this practice as a natural part of their routines. Unfortunately, this new social and informational landscape has favored an unprecedented amplification of cyber threats such as hate speech and disinformation, posing significant risks to democratic systems Office of Science and Technology of the Congress of Deputies (Office C) (2023). This situation has intensified and drawn substantial attention from the research community, governmental bodies, and the general public, particularly following extensive disinformation campaigns associated with recent events, including the COVID-19 pandemic Kim and Kesari (2021), the Russia-Ukraine war Pierri et al. (2022), and the Israel-Palestine conflict Aljazeera (2024). Consequently, a structured model encapsulating the key actors, dynamics, and resulting societal impacts is proposed to understand and contextualize the environment being worked on. Figure 1 illustrates our threat model with three main components. In blue, the media and audience as actors in the model, providing the information environment with online news and social network posts that people can read, react to, and comment on. In orange, the content is considered potentially harmful due to intrinsic hateful narratives of today's ecosystem (particularly, public reactions that will be the focus of this research work). In red, the online situation leads to polarization, extremism, and heightened tension, creating a vulnerable environment for society OSMUNDSEN et al. (2021); Cinelli et al. (2021); Pastor-Galindo et al. (2021). In fact, this agitated context serves as a vector for disinformation to become more effective Kim and Kesari (2021).
- Research Report > New Finding (1.00)
- Research Report > Experimental Study (0.93)
- Media > News (1.00)
- Information Technology > Security & Privacy (1.00)
- Health & Medicine > Therapeutic Area > Infections and Infectious Diseases (1.00)
- (3 more...)
- Information Technology > Security & Privacy (1.00)
- Information Technology > Data Science > Data Mining (1.00)
- Information Technology > Communications > Social Media (1.00)
- (4 more...)
Emotion Detection And Analysis
Emotion Detection and Analysis is a web application developed by the team The Mystic Forces as their final project of the AI5: productionizing AI course at Univ.ai under the guidance of Pavlos Protopapas (Scientific Program Director at the Institute for Applied Computational Science (IACS) at Harvard University) & Shivas Jayaram (Research @Harvard IACS Deep Learning Researcher, Educator, and Practitioner). The web application is an end-to-end implemented deep learning project. Public Speaking is not just a skill but an art which is not easily mastered. It has become an essential for every individual. In this digital world, where your office is your computer screen and online meeting platforms are the places to connect, one is asked to deliver presentations, briefings, and do meetings regularly.