Cuesta College

Global socio-political discourse on social media is often heavily colored by its highly volatile and divisive nature. Nonetheless, social media remains central to political communication, becoming its own public square with the unique distinction of also intersecting with the multiverse of public squares across nations and platforms. While the sociological value of studying this discourse is undeniable, successfully deciphering the meaning of large corpora of polarized political content remains elusive. AI machine learning methods can tackle large datasets but are limited in their abilities to decipher highly convoluted and nuanced meanings associated with language. Using a historical dataset of Donald Trump-related tweets from 2016 to 2017 (immediately before and after his inauguration in January 2017), this study evaluated ChatGPT compared with humans in effectively discerning sentiment from controversial political discourse. Using human coding of sentiment and 10 sociolinguistic attributes, we found that ChatGPT and humans achieved similarly low levels of agreement-not because AI has reached human-level proficiency, but because both struggled considerably with this type of polarized political content. Human coding itself fell well short of acceptable reliability standards, meaning AI's comparable performance reflects a shared deficiency rather than a shared strength. The agreement between human and machine coders (kappa = 0.308) fell well short of expectations and should not be interpreted as evidence of AI adequacy. We highlight the most problematic areas for machine learning such as its inability to analyze tweets because of special characters, tweet length, and links. For humans, things like misspellings and abbreviations brought confusion. Our findings urge caution in deploying a large language model (LLM)-based sentiment analysis of politically divisive discourse in the near future.

Public information officers (PIOs) are responsible for collecting, analyzing, verifying, and communicating risk, crisis, and recovery information to a wide variety of people across the "whole community." Along with the challenges inherent in fulfilling their responsibilities, PIOs now have a new emerging constraint-an enhanced lack of time. The rise, and now arguably dominance, of social media has created the necessity for PIOs to respond to crises in ever shorter amounts of time. An incident that historically a PIO could respond to in an hour must now be addressed within a matter of minutes. The purpose of this exploratory study was to measure to what extent there was consensus among PIOs regarding their perspective of the ideal capability targets for specific emergency communication tasks after notification of an incident. This was a limited study. The findings from this study could be used as a call for regional or nationwide research to develop more robust data. Ultimately, this wider dataset may be used to propose standard capability targets that PIOs can use to develop and measure their own capability targets based on crisis communication plans.

She a civil engineer with a background in infrastructure design and management, and project management.Her consulting experience spanned eight years

Santa Maria and Guadalupe are neighboring cities in northern Santa Barbara County that have a lower socioeconomic profile than the county overall, are >75

This work-in-progress paper/poster describes the development and initial implementation of a new approach to mentoring and advising students that we call "strengths training from a social justice perspective in engineering and computer science as context." This approach to mentoring and advising is part of a larger California-based Multi-Institutional S-STEM project called "Engineering Neighbors: Gaining Access, Growing Engineers" (ENGAGE, NSF DUE 1834128, 1834154). ENGAGE is designed to increase the number of low-income, academically talented students with demonstrated financial need who begin their engineering education at two Hispanic-Serving California Community Colleges; transfer to a highly-selective, predominantly white public institution; and then are retained in and graduate with a B.S. degree in engineering, and enter the STEM workforce or graduate program. The broader project also seeks to transform our institutions and the relationships between them to prioritize transfer student success via the utilization of the Essential Transfer Practices framework created by the Community College Research Center at Teachers College and the Aspen Institute [1]. The identified Essential Transfer Practices are designed to strengthen the implementation of practices that 1) make transfer student success a priority; 2) create clear programmatic pathways with aligned high-quality instruction; and 3) provide tailored transfer student advising to create sustainable change. As part of describing the development and initial implementation of this new model of mentoring and advising, this work-in-progress paper/poster shares information about student and faculty workshops and ongoing grant-related activities and support systems for mentors and mentees. Preliminary results related to student experiences and outcomes utilizing the "strengths training from a social justice perspective in engineering and computer science as context" framework during COVID-19 are included. The approach to ongoing research focused on the intersections of strengths, social identity, context, and social networks as related to this model of mentoring and advising is introduced.