137 research outputs found
Social media crowdsourcing for rapid damage assessment following sudden-onset earthquakes
Rapid appraisal of damages related to hazard events is important to first responders, government agencies, insurance industries, and other private and public organizations. While satellite monitoring, ground-based sensor systems, inspections, and other technologies provide data to inform post-disaster response, crowdsourcing through social media is an additional and novel data source. In this study, the use of social media data, principally Twitter postings, is investigated to make approximate but rapid early assessments of damages following earthquake disasters. The goal is to explore the potential utility of using social media data for rapid damage assessment after sudden-onset hazard events and to identify insights related to potential challenges. This study defines a text-based damage assessment scale for earthquake damages and then develops a text classification model for rapid damage assessment. The 2019 Ridgecrest, California earthquake sequence is mainly investigated as the case study. Results reveal that Twitter users rapidly responded to this sudden-onset event, and the damage estimation shows temporal and spatial characteristics. The generalization ability of the model is validated through the investigation of damage assessment for another five earthquake events. Although the accuracy remains a challenge compared to ground-based instrumental readings and inspections, the proposed damage assessment model features rapidity with large amounts of data at spatial densities that exceed those of conventional sensor networks
"HOT" ChatGPT: The promise of ChatGPT in detecting and discriminating hateful, offensive, and toxic comments on social media
Harmful content is pervasive on social media, poisoning online communities
and negatively impacting participation. A common approach to address this issue
is to develop detection models that rely on human annotations. However, the
tasks required to build such models expose annotators to harmful and offensive
content and may require significant time and cost to complete. Generative AI
models have the potential to understand and detect harmful content. To
investigate this potential, we used ChatGPT and compared its performance with
MTurker annotations for three frequently discussed concepts related to harmful
content: Hateful, Offensive, and Toxic (HOT). We designed five prompts to
interact with ChatGPT and conducted four experiments eliciting HOT
classifications. Our results show that ChatGPT can achieve an accuracy of
approximately 80% when compared to MTurker annotations. Specifically, the model
displays a more consistent classification for non-HOT comments than HOT
comments compared to human annotations. Our findings also suggest that ChatGPT
classifications align with provided HOT definitions, but ChatGPT classifies
"hateful" and "offensive" as subsets of "toxic." Moreover, the choice of
prompts used to interact with ChatGPT impacts its performance. Based on these
in-sights, our study provides several meaningful implications for employing
ChatGPT to detect HOT content, particularly regarding the reliability and
consistency of its performance, its understand-ing and reasoning of the HOT
concept, and the impact of prompts on its performance. Overall, our study
provides guidance about the potential of using generative AI models to moderate
large volumes of user-generated content on social media
Thriving in a Pandemic: Lessons Learned from a Resilient University Program Seen Through the CoI Lens
In March 2020, college campuses underwent a sudden transformation to online
learning due to the COVID-19 outbreak. To understand the impact of COVID-19 on
students' expectations, this study conducted a three-year survey from ten core
courses within the Project Management Center for Excellence at the University
of Maryland. The study involved two main steps: 1) a statistical analysis to
evaluate students' expectations regarding "student," "class," "instructor," and
"effort;" and 2) a lexical salience-valence analysis (LSVA) through the lens of
the Community of Inquiry (CoI) framework to show the changes of students'
expectations. The results revealed that students' overall evaluations
maintained relatively consistent amid the COVID-19 teaching period. However,
there were significant shifts of the student expectations toward Cognitive,
Social and Teaching Presence course elements based on LSVA results. Also, clear
differences emerged between under-graduates and graduates in their expectations
and preferences in course design and delivery. These insights provide practical
recommendations for course instructors in designing effective online courses
DataChat: Prototyping a Conversational Agent for Dataset Search and Visualization
Data users need relevant context and research expertise to effectively search
for and identify relevant datasets. Leading data providers, such as the
Inter-university Consortium for Political and Social Research (ICPSR), offer
standardized metadata and search tools to support data search. Metadata
standards emphasize the machine-readability of data and its documentation.
There are opportunities to enhance dataset search by improving users' ability
to learn about, and make sense of, information about data. Prior research has
shown that context and expertise are two main barriers users face in
effectively searching for, evaluating, and deciding whether to reuse data. In
this paper, we propose a novel chatbot-based search system, DataChat, that
leverages a graph database and a large language model to provide novel ways for
users to interact with and search for research data. DataChat complements data
archives' and institutional repositories' ongoing efforts to curate, preserve,
and share research data for reuse by making it easier for users to explore and
learn about available research data.Comment: 6 pages, 2 figures, and 1 table. Accepted to the 86th Annual Meeting
of the Association for Information Science & Technolog
A Bibliometric Review of Large Language Models Research from 2017 to 2023
Large language models (LLMs) are a class of language models that have
demonstrated outstanding performance across a range of natural language
processing (NLP) tasks and have become a highly sought-after research area,
because of their ability to generate human-like language and their potential to
revolutionize science and technology. In this study, we conduct bibliometric
and discourse analyses of scholarly literature on LLMs. Synthesizing over 5,000
publications, this paper serves as a roadmap for researchers, practitioners,
and policymakers to navigate the current landscape of LLMs research. We present
the research trends from 2017 to early 2023, identifying patterns in research
paradigms and collaborations. We start with analyzing the core algorithm
developments and NLP tasks that are fundamental in LLMs research. We then
investigate the applications of LLMs in various fields and domains including
medicine, engineering, social science, and humanities. Our review also reveals
the dynamic, fast-paced evolution of LLMs research. Overall, this paper offers
valuable insights into the current state, impact, and potential of LLMs
research and its applications.Comment: 36 pages, 9 figures, and 4 table
Modeling Link-level Road Traffic Resilience to Extreme Weather Events Using Crowdsourced Data
Climate changes lead to more frequent and intense weather events, posing
escalating risks to road traffic. Crowdsourced data offer new opportunities to
monitor and investigate changes in road traffic flow during extreme weather.
This study utilizes diverse crowdsourced data from mobile devices and the
community-driven navigation app, Waze, to examine the impact of three weather
events (i.e., floods, winter storms, and fog) on road traffic. Three metrics,
speed change, event duration, and area under the curve (AUC), are employed to
assess link-level traffic change and recovery. In addition, a user's perceived
severity is computed to evaluate link-level weather impact based on
crowdsourced reports. This study evaluates a range of new data sources, and
provides insights into the resilience of road traffic to extreme weather, which
are crucial for disaster preparedness, response, and recovery in road
transportation systems
Edge-Mediated Skyrmion Chain and Its Collective Dynamics in a Confined Geometry
The emergence of a topologically nontrivial vortex-like magnetic structure,
the magnetic skyrmion, has launched new concepts for memory devices. There,
extensive studies have theoretically demonstrated the ability to encode
information bits by using a chain of skyrmions in one-dimensional nanostripes.
Here, we report the first experimental observation of the skyrmion chain in
FeGe nanostripes by using high resolution Lorentz transmission electron
microscopy. Under an applied field normal to the nanostripes plane, we observe
that the helical ground states with distorted edge spins would evolves into
individual skyrmions, which assemble in the form of chain at low field and move
collectively into the center of nanostripes at elevated field. Such skyrmion
chain survives even as the width of nanostripe is much larger than the single
skyrmion size. These discovery demonstrates new way of skyrmion formation
through the edge effect, and might, in the long term, shed light on the
applications.Comment: 7 pages, 3 figure
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