What happens when weather forecasts do not match reality? How do the public emotionally respond when a disaster unfolds differently from what they expected? A research team led by Professor Jonghun Kam and Kiru Kim from the Department of Environmental Engineering at POSTECH investigated how forecast error types influenced public emotion during the landfall of Typhoon Khanun. Using an artificial intelligence (AI), Natural Language Processing (NPL), the researchers found that different types of forecast error (e.g., over/underestimation) triggered distinct emotional responses among the public. The study has been published in GeoHealth.

Weather forecasts are essential for disaster preparedness, but they are inherently uncertain. During extreme weather events such as typhoons, forecasts may either overestimate rainfall that never occurs or underestimate rainfall that turns out to be severe. The research team focused on how these mismatches between expectation and reality affect public perceptions and emotional responses before and after the landfall of Typhoon Khanun.

The researchers assessed the Korean Meteorological Administration’s prediction skill of Typhoon Khanun, which crossed the Korean Peninsula in August 2023 against observed rainfall records from 613 weather stations. They also analyzed more than 43,000 online posts from NAVER Report Talk using an AI-based natural language processing model.

The results revealed clear spatial differences in forecast performance. Forecasts tended to underestimate heavy rainfall in the eastern and southeastern regions of the Korean Peninusla while overestimating rainfall in the western and metropolitan areas. In regions where rainfall was overestimated, anxiety, worry, and fatigue became dominant emotion types in online discourses while the public from the regions with underestimated rainfall showed a high level of certain emotion types like confusion, embarrassment, and sadness.

The researchers also examined changes in regional emotion. In the eastern and southeastern regions, where actual rainfall exceeded forecast amounts, online discussions frequently expressed confusion, uncertainty, and anxiety as Typhoon Khanun passed by the Korean Peninsula. The western and metropolitan regions, where rainfall forecasts were higher than observed rainfall, showed dominant emotions of worry and concern, followed by increasing a level of emotion types, relief and reassurance, as the typhoon passed. These findings suggest that people respond not only to the disaster itself but also to the gap between what they expected and what they actually experienced.

Overall, approximately 55% of all online discourses expressed negative emotions, with anxiety and worry being the most common. The researchers also found that information-seeking activities peaked before the typhoon’s landfall, whereas online reporting and experience sharing surged during the event itself. This indicates that the public shift from passive information consumers to active information providers as disaster impacts become more immediate.

The findings demonstrate that forecast accuracy is not only a technical issue but also an important factor influencing public perception and emotional well-being. More importantly, the study highlights that the mismatch between anticipated risk and experienced reality plays a critical role in shaping public emotions and their perceived risk during disasters. The researchers suggest that communicating forecast uncertainty more effectively could improve public trust and reduce emotional distress during future extreme weather events.

Kiru Kim, the lead author, noted, “This study demonstrates that in disaster situations, it is important not only to improve forecast accuracy but also to develop risk communication strategies that effectively convey uncertainty to the public.”

Professor Jonghun Kam said, “This study demonstrates how AI can be used to analyze large-scale public discourse and monitor the emotional impacts of forecast errors. The findings provide new insights into how to develop effective risk communication strategies during landfalling typhoons and other natural disasters.”

Journal

GeoHealth

DOI

10.1029/2026GH001837

The NSF Graduate Research Fellowship Program is designed to nurture the next generation of science and engineering leaders by providing three years of financial support extended over a five-year period. Recipients receive an annual stipend, presently set at $37,000, along with education allowances aimed at bolstering research that pushes the boundaries of innovation. The program’s mission is to sustain and expand the breadth of the U.S. scientific workforce by empowering individuals with exceptional promise to make transformative contributions across diverse fields.

DeVito’s research navigates the sophisticated terrain of large language models (LLMs) leveraged to examine public discourse on social media platforms, focusing on how emerging AI technologies are perceived and discussed. Her doctoral work intricately compares advanced LLM-based methods with conventional NLP techniques, aiming to unravel nuanced sentiment and thematic structures in conversations surrounding generative AI (GAI) in educational contexts. This approach not only highlights technological trends but also informs the design of AI tools that could enhance learning outcomes.

Hailing from a strong academic foundation in both computer science and computer engineering, DeVito’s trajectory is distinguished by her rapid accumulation of degrees with stellar academic performance. Earning dual bachelor’s degrees with the highest GPA in her class, followed by a master’s degree in computer science with a focus on AI in just one year, she embodies the caliber of a scholar who blends intensity with interdisciplinary breadth. Her academic rigor is matched by her passion for leveraging technology to create inclusive educational environments.

Her Ph.D. work extends prior NSF-funded research that analyzed teacher and student discussions on Reddit, providing one of the most comprehensive assessments of GAI conversations in educational settings. The groundbreaking study examined nearly 15,000 posts and comments, utilizing natural language processing tools to parse complex narratives around AI adoption, ethical considerations, and pedagogical ramifications. This research sheds light on significant challenges, such as the widespread use of flawed AI-based cheating detectors, which have led to misjudgments and emotional distress for students.

Supported by faculty mentors Hari Kalva, Ph.D., and Hanqi Zhuang, Ph.D., DeVito’s investigation delves deeper by expanding the inquiry into multiple social media platforms. She meticulously analyzes content created predominantly by young women in STEM fields, extracting themes and emotional tones from posts tagged with identifiers like #WomenInSTEM. By harnessing both LLMs and traditional NLP techniques, her research dissects engagement patterns and sentiment dynamics, providing a rich empirical foundation to guide the development of AI-powered educational tools tailored to diverse learner profiles.

The implications of DeVito’s work are profound and far-reaching. By contrasting teacher and student perspectives, her analyses offer critical insights that inform policy recommendations and ethical guidelines for AI usage in schools. She emphasizes the need for transparency and fairness in AI adoption, advocating for systems that support rather than undermine student well-being and educational equity. These findings contribute urgently needed evidence to the evolving discourse on responsible AI integration in academic institutions.

DeVito’s commitment to research excellence is mirrored by her aspirations. She envisions a future as a professor leading a research laboratory dedicated to harnessing AI and NLP for educational advancements. Her focus on generative AI technologies aligns with a broader vision of transforming teaching and learning methodologies, fostering student engagement, and nurturing the pipeline of underrepresented groups in STEM. By aiming to develop AI applications that are both innovative and ethically grounded, she is poised to influence the educational landscape significantly.

The supportive environment at FAU’s College of Engineering and Computer Science plays a critical role in nurturing talents like DeVito. Renowned for its pioneering research and comprehensive academic programs, the College emphasizes interdisciplinary approaches to AI, computer engineering, and data science. Its national recognition and robust funding from major agencies such as the NSF, NIH, and Department of Defense highlight FAU’s commitment to fostering research that addresses real-world challenges through technology innovation.

Moreover, DeVito’s journey underscores the transformative potential of dual enrollment programs that allow high school students to engage with college-level coursework early. Graduating from FAU High School and A.D. Henderson University School, she entered higher education at the precocious age of sixteen, accelerating an academic path that few replicate. Her success story exemplifies how early exposure to advanced STEM curricula can cultivate leaders equipped to tackle complex scientific problems with creativity and depth.

The engagement with NSF-funded projects early in her career has given DeVito hands-on experience with data-driven research methodologies essential for AI investigations. Working alongside professors Kalva and Zhuang, she developed skills in managing large datasets, applying sophisticated computational models, and generating actionable insights. This background enhances her capacity to lead innovative research efforts that combine theoretical foundations with practical, impactful solutions.

As conversations around AI’s place in education rapidly evolve, DeVito’s work captures the critical intersection of technology, ethics, and pedagogy. Her research not only informs educators and policymakers about the benefits and pitfalls of generative AI but also builds a roadmap for future investigations into how AI systems can be responsibly integrated to promote equity and excellence among learners. In doing so, she contributes to shaping the next era of intelligent educational environments that empower all students, especially minorities and women pursuing STEM careers.

Enthusiastic support from FAU’s leadership further amplifies the significance of DeVito’s recognition. Stella Batalama, Ph.D., dean of the College, highlights how this fellowship also reflects the strength and innovation thriving within FAU’s academic community. The honor bestowed upon DeVito signals a bright future not only for her but also for the institution’s capacity to produce researchers who will impact fields ranging from AI ethics to educational technology development.

In summary, Paulina DeVito’s NSF Graduate Research Fellowship award heralds a promising new chapter for AI-driven educational research. Her exploration of social media discourse around generative AI, combined with rigorous computational analyses, paves the way for transformative tools designed to enhance STEM learning experiences. With her vision and dedication, DeVito stands at the forefront of a vital movement harnessing AI’s power to enrich education and foster inclusive scientific innovation.

Subject of Research: Artificial Intelligence and Natural Language Processing Applications in Education, Analysis of Public Discourse on Social Media Regarding Generative AI in Education

Article Title: Rising STEM Star Paulina DeVito Earns Prestigious NSF Fellowship for Pioneering AI Research in Education

News Publication Date: 2024

Web References:

• Florida Atlantic University College of Engineering and Computer Science: https://www.fau.edu/engineering/
• Florida Atlantic University: https://www.fau.edu/
• NSF Graduate Research Fellowship Program: https://www.nsfgrfp.org/

Image Credits: Florida Atlantic University

Keywords: Machine learning, Natural language processing, Generative AI, Social media, Education, Educational methods, Education policy, Education technology, College students, Doctoral students, Graduate students, Undergraduate students, Minority students, Science careers, Scientific organizations, Research organizations

The researchers’ focus on the stark rise in hate speech is backed by a rigorous methodological framework, employing established techniques to gauge the levels of English-language hate speech on X. During the analytical timeframe, which extended until June 2023, they discovered that spikes in hate speech, particularly prior to Musk’s acquisition, were not only sustained but also exacerbated. This trend reflected notable increases in specific types of slurs, particularly those rooted in homophobia, transphobia, and racism. The weekly hate speech rates remained significantly elevated, indicating an alarming landscape for social media discourse.

In detailing the findings, the researchers noted that the average number of "likes" associated with posts containing hate speech surged by an astonishing seventy percent, highlighting a worrying trend where more individuals were engaging with, and being exposed to, such harmful content. Such metrics reveal a concerning shift in user engagement patterns on the platform, fostering an environment where hate speech flourishes. Importantly, these developments run counter to Musk’s public declarations that aimed to reduce inauthentic activity and the harmful impact of bots on the platform, suggesting a systemic disengagement from the commitments he underscored shortly after taking control.

Prior research has established a connection between online hate speech and offline hate crimes, illuminating the potential consequences of rampant hate speech within digital spaces. The inability to curb the influence of bots and bot-like accounts is particularly disconcerting, particularly as these entities are known to exacerbate the spread of misinformation and spam. This oversight could compound the potential for societal harm, with implications that extend beyond the immediate realm of social media interactions.

Despite Musk’s pledge to diminish bot presence on X, the analysis reveals that the number of bot and other inauthentic accounts did not see a decline; in fact, there are indications that their presence may have grown. This stark inconsistency between claimed intentions and observed reality raises critical questions about the authenticity of moderation efforts under Musk’s leadership. The researchers emphasize the necessity for enhanced monitoring and regulatory intervention within social media platforms, underscoring that existing policies currently fail to effectively manage exposure to harmful content.

The implications of this analysis are far-reaching, prompting critical discussions about the role of social media companies in safeguarding user experiences and public discourse. Critics argue that platforms like X must take immediate action to improve moderation practices and protect users from exposure to hate speech and misinformation. The study also highlights the need for further research to explore the dynamics of hate speech across various social media platforms and the effectiveness of interventions.

Moreover, the continued increase in hate speech and the persistent challenge posed by bot activity necessitate a deeper understanding of how societal norms and values are reflected and refracted in digital spaces. This research initiative draws attention to the urgent need for legislative and societal interventions aimed at both technological and human behavioral aspects of online interactions.

As digital platforms continue to grow in influence, the call for responsible governance in monitoring hateful or misleading speech becomes paramount. Stakeholders from various sectors—including policymakers, researchers, and tech companies—must come together to formulate tangible solutions to address the toxic culture permeating social media, characterized by rampant hate speech and the proliferation of misinformation.

In conclusion, Hickey et al.’s findings provide an unsettling snapshot of the current state of X under Musk’s leadership, bringing to light critical issues surrounding hate speech, moderation, and the enduring presence of bots. Acknowledgment of these findings may catalyze further discussions regarding policy reform and the imperative for improved digital hygiene in the interconnected world. As we navigate an era where online platforms increasingly shape societal dialogues, it is essential to heed this research and advocate for a healthier digital ecosystem.

Subject of Research: People
Article Title: X under Musk’s leadership: Substantial hate and no reduction in inauthentic activity
News Publication Date: 12-Feb-2025
Web References: PLOS One
References: Hickey D, Fessler DMT, Lerman K, Burghardt K (2025) X under Musk’s leadership: Substantial hate and no reduction in inauthentic activity. PLoS ONE 20(2): e0313293.
Image Credits: Credit: Hickey et al., 2025, PLOS One, CC-BY 4.0
Keywords: Hate Speech, Social Media, Elon Musk, X, Bots, Digital Moderation, Misinformation, Public Health, Online Discourse, Policy Reform.