Networks in Polarized Times: What Americans Talk about, with Whom and When?

What do American people talk about, with whom, and when? What people talk about influences and is influenced by who they talk with, and when they talk structures the interdependency between discussion topics and relationships. This context-alter-topic interdependency provides an opportunity to identify contextual mechanisms by which social and political networks are activated and deactivated in response to salient social events and polarized contexts. Do people talk about important matters with fewer people than ever before? Do people organize their political belief systems in ideological ways using the single liberal-conservative dimension? Do people discuss politics with more people who are more politically diverse in contested elections? This dissertation answers these and related questions by revisiting the same survey data that others have found useful in the past, but with a new and fresh lens. Do people talk about important matters with fewer people than ever before? The 2004 General Social Survey (GSS) reported significant increases in social isolation and significant decreases in ego-network size relative to previous periods. These results have been repeatedly challenged, though none precisely identify the cause of decreased ego-network size. The second chapter shows that it matters that the 2004 GSS -- unlike other GSS surveys -- was fielded during a highly polarized election period. I show that political priming induced by presidential election events makes people frame "important matters" as political matters, and political polarization further suppresses network size especially for non-partisans. Do people organize their political belief system in ideological ways using the single liberal-conservative dimension? By considering a set of interrelated political beliefs as a network of belief systems, the third chapter seeks to resolve theoretical puzzles concerning the organization of political belief systems, and address competing accounts of the role of political ideology and core values. I compare results from the American National Election Studies and General Social Surveys, and show the strong contextual influence on belief systems. I find that belief systems, often thought to be relatively stable, need to be "activated" by certain social cues. Do people discuss politics with more people who are more politically diverse during contested elections? The fourth chapter focuses on battleground states to investigate the mutual interrelationship between political discussion partners and topics: who people discuss politics with depends on which issues they discuss and vice versa. I propose that increases in the salience of politics and exposure to opposing views contribute to the activation of interpersonal political echo chambers. I present evidence to support this claim based on statistical analysis of the 1992, 2000, and 2008 National Election Studies. My dissertation shows, throughout three empirical chapters, why we need to seriously consider socio-temporal context in studies of social and political networks. I use survey timing, exogenous events, and battleground states to show how political situations induced by political events activate ideological thinking, which in turn deactivates our core discussion networks, and ultimately activates interpersonal political echo chambers. In sum, I discover situational activation of network processes

Social networks in COVID-19 America: Americans remotely together but politically apart

The COVID-19 pandemic has presented a social dilemma; "social distancing" was required to stop the spread of disease, but close social contacts were needed more than ever to collectively overcome the unprecedented challenges of the crisis. How did Americans mobilize their social ties in response to the pandemic? Drawing from a nation-wide daily online survey of 36,345 Americans from April 2020 through April 2021, we examine the characteristics of Americans' core networks within which people discuss "important matters." Comparing the COVID-19 networks to those previously collected in eight national core network surveys from 1985 to 2016, we observe remarkable stability in the size and relationship composition of core networks during COVID-19. In contrast to the robust nature of core networks, we discover a significant rise in racial homophily among kin ties, and political homophily among non-kin ties. Simultaneously, our study reveals a significant surge in the adoption of remote communication technology to connect with individuals who are geographically distant. We demonstrate that the changing mode of communication contributes to increases in racial and political homophily. These results suggest that the COVID-19 pandemic may bring people remotely together but only with the like-minded, deepening social divides in American society

Important Matters in Political Context

The 2004 General Social Survey (GSS) reported significant increases in social isolation and significant decreases in ego network size relative to previous periods. These results have been repeatedly challenged. Critics have argued that malfeasant interviewers, coding errors, or training effects lie behind these results. While each critique has some merit, none precisely identify the cause of decreased ego network size. In this article, we show that it matters that the 2004 GSS—unlike other GSS surveys—was fielded during a highly polarized election period. We find that the difference in network size between nonpartisan and partisan voters in the 2004 GSS is larger than in all other GSS surveys. We further discover that core discussion network size decreases precipitously in the period immediately around the first (2004) presidential debate, suggesting that the debate frames “important matters” as political matters. This political priming effect is stronger where geographic polarization is weaker and among those who are politically interested and talk about politics more often. Combined, these findings identify the specific mechanism for the reported decline in network size, indicate that inferences about increased social isolation in America arising from the 2004 GSS are unwarranted, and suggest the emergence of increased political isolation

Field implementation feasibility study of cumulative travel-time responsive (CTR) traffic signal control algorithm

The cumulative travel-time responsive (CTR) algorithm determines optimal green split for the next time interval by identifying the maximum cumulative travel time (CTT) estimated under the connected vehicle environment. This paper enhanced the CTR algorithm and evaluated its performance to verify a feasibility of field implementation in a near future. Standard Kalman filter (SKF) and adaptive Kalman filter (AKF) were applied to estimate CTT for each phase in the CTR algorithm. In addition, traffic demand, market penetration rate (MPR), and data availability were considered to evaluate the CTR algorithm's performance. An intersection in the Northern Virginia connected vehicle test bed is selected for a case study and evaluated within vissim and hardware in the loop simulations. As expected, the CTR algorithm's performance depends on MPR because the information collected from connected vehicle is a key enabling factor of the CTR algorithm. However, this paper found that the MPR requirement of the CTR algorithm could be addressed (i) when the data are collected from both connected vehicle and the infrastructure sensors and (ii) when the AKF is adopted. The minimum required MPRs to outperform the actuated traffic signal control were empirically found for each prediction technique (i.e., 30% for the SKF and 20% for the AKF) and data availability. Even without the infrastructure sensors, the CTR algorithm could be implemented at an intersection with high traffic demand and 50-60% MPR. The findings of this study are expected to contribute to the field implementation of the CTR algorithm to improve the traffic network performance. © 2017 John Wiley & Sons, Ltd.1

AI-Augmented Surveys: Leveraging Large Language Models for Opinion Prediction in Nationally Representative Surveys

How can we use large language models (LLMs) to augment surveys? This paper investigates three distinct applications of LLMs fine-tuned by nationally representative surveys for opinion prediction -- missing data imputation, retrodiction, and zero-shot prediction. We present a new methodological framework that incorporates neural embeddings of survey questions, individual beliefs, and temporal contexts to personalize LLMs in opinion prediction. Among 3,110 binarized opinions from 68,846 Americans in the General Social Survey from 1972 to 2021, our best models based on Alpaca-7b excels in missing data imputation (AUC = 0.87 for personal opinion prediction and $\rho$ = 0.99 for public opinion prediction) and retrodiction (AUC = 0.86, $\rho$ = 0.98). These remarkable prediction capabilities allow us to fill in missing trends with high confidence and pinpoint when public attitudes changed, such as the rising support for same-sex marriage. However, the models show limited performance in a zero-shot prediction task (AUC = 0.73, $\rho$ = 0.67), highlighting challenges presented by LLMs without human responses. Further, we find that the best models' accuracy is lower for individuals with low socioeconomic status, racial minorities, and non-partisan affiliations but higher for ideologically sorted opinions in contemporary periods. We discuss practical constraints, socio-demographic representation, and ethical concerns regarding individual autonomy and privacy when using LLMs for opinion prediction. This paper showcases a new approach for leveraging LLMs to enhance nationally representative surveys by predicting missing responses and trends

Transformation of social relationships in COVID-19 America: Remote communication may amplify political echo chambers

<p>The COVID-19 pandemic, with millions of Americans compelled to stay home and work remotely, presented an opportunity to explore the dynamics of social relationships in a predominantly remote world. Using the 1972-2022 General Social Surveys, we found that the pandemic significantly disrupted the patterns of social gatherings with family, friends, and neighbors, but only momentarily. Drawing from the nationwide ego-network surveys of 41,033 Americans from 2020 to 2022, we found that the size and composition of core networks remained stable, though political homophily increased among non-kin relationships compared to previous surveys between 1985 and 2016. Critically, heightened remote communication during the initial phase of the pandemic was associated with increased interaction with the same partisans, though political homophily decreased during the later phase of the pandemic when in-person contacts increased. These results underscore the crucial role of social institutions and social gatherings in promoting spontaneous encounters with diverse political backgrounds.</p&gt

Sustainable Transportation Infrastructures in Iowa—Goals and Practices

The need to incorporate sustainability principles and practices is increasing for environmental and economic reasons. It is imperative to identify and operationalize sustainability strategies into core administrative, planning, design, construction, operational, and maintenance activities for the transportation infrastructure systems by integrating sustainability into decision-making processes. The primary goal of this study is to develop an implementation plan for achieving more sustainable transportation infrastructure systems in Iowa. This research aims to guide the adoption of sustainable strategies, balancing cost, performance, and environmental impact in transportation infrastructure development. This paper presents efforts to develop a methodology for identifying the best sustainable practices for implementation in transportation infrastructure practices in Iowa by surveying state DOTs to learn about their sustainability goals and practices, identifying existing sustainability attributes and sustainable practices, and developing a GIS database where construction, materials and performance data of sustainable practices can be stored and analyzed