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Exploring the Evolution of Global Warming Discourse: A Twitter-based Analysis Across the United States, United Kingdom, and India
Full text linkGlobal warming has gained increasing attention over the past decade, with public discourse intensifying on social media platforms, particularly on Twitter. This increased discussion stems from political controversies surrounding climate change and the rise in extreme weather events. This study explores the evolution of global warming discourse on Twitter, with a focus on the United States, United Kingdom, and India. This research used a dataset of historical tweets ranging from 2010 to 2023 containing the keyword global warming. Using natural language processing (NLP) techniques such as emotion analysis, word cloud visualization, and topic modeling (LDA), approximately twenty-eight million tweets were analyzed to explore national and regional differences in perception. The findings reveal that discourse in the United States is marked by political polarization, skepticism, and scientific awareness, while the United Kingdom displays a similar yet more policy and science-driven narrative. In contrast, India’s discourse is significantly action-oriented, emphasizing environmental initiatives like tree planting and cleanup campaigns. Emotion analysis showed higher levels of anger in the U.S. and greater optimism in India. Topic modeling further highlighted these differences, with the U.S. and U.K. centered on policy, skepticism, and scientific data, while India was focused on activism and public engagement. These results highlight the numerous ways global warming is framed and discussed across the United States, United Kingdom, and India, shaped by various cultural, political, and environmental factors
Impact of National Chain Restaurants on Small Family-Owned Restaurants
Full text linkThis Honors Thesis discusses the impact of national chain restaurants on small family-owned restaurants, emphasizing how the expansion of chain restaurants has negatively impacted small family-owned businesses. National chain restaurants are managed and owned by large corporations, which can potentially overpower traditional establishments, putting pressure on smaller restaurants to remain profitable and face the risk of closing. Despite the reduced pricing and greater convenience provided by chain restaurants, family-owned restaurants provide a sense of local community, a better dining experience, higher food quality, and a wider variety of food. In addition, this thesis also emphasizes the significance of customer satisfaction, the advantages of local restaurants, including personalized service, as well as the conveniency of fast-food chains. There is also discussion of the pricing and food quality differences between local restaurants and large national chains. For this Thesis, digital surveys, interviews, and focus groups will be held to prove my statement. Overall, this study highlights the necessity of supporting regional agriculture, local job growth, community development, and why supporting family-owned restaurants is essential for the local community
The Contemporaneous Impact of Unemployment and Income Inequality on Violent vs Property Crime Rates in New England: A Panel Data and Machine Learning Approach
Full text linkThis paper investigates the possible impact of unemployment and income inequality on property and or violent crimes in the New England region in the United States from 2003 to 2022. Using a panel data analysis looking at unemployment rates and factors of income inequality in New England states, Connecticut, Maine, Massachusetts, Rhode Island, New Hampshire, and Vermont, along with demographic factors like race and gender aim to find a relationship between unemployment and income inequality with crime rates. Economic distress, usually caused by unemployment, can cause an individual to commit crimes due to hard times. Using machine learning decision trees and cluster graphs, we then investigate the connection within different Massachusetts counties from 2008 to 2010 aiming to see if the independent variables can be used as predictors. Overall, there was significant evidence that unemployment rate, race being non-white and minimum wage affect whether someone is likely to commit a property and violent crime. The machine learning findings did find that demographic factors like race being Hispanic can be used as predictors but also found certain results to contradict regression findings
Empirical Analysis of The Impact of Macroeconomic Determinants on Equity REIT Performance
Full text linkThis paper investigates how equity Real Estate Investment Trusts (REITs) respond to key macroeconomic factors: stock market performance, interest rate changes, and inflation. Using annual data from 1972 to 2024, multiple linear regressions are conducted to assess which variables significantly explain REIT returns. The results reveal a strong and consistent relationship between equity REIT performance and S&P 500 returns, while interest rates and lagged inflation measures show no statistically significant impact. These findings suggest that REITs behave more like equities than fixed-income assets, particularly in the long term. While the models have limited explanatory power, the analysis supports the idea that investor sentiment and overall market trends play a greater role in driving REIT returns than traditional macroeconomic indicators
Day of Understanding 2025: Event Highlights, Impact, and What Comes Next
No full textFour page summary of about Day of Understanding 2025. Includes information about programming, organization, and statistics. Also includes how the event will change in the future
High Hopes, Hard Falls: Consumer Expectations and Reactions to AI-Human Collaboration in Advertising
No full textThis paper explores what people expect from AI-human collaboration in a creative domain and how they react when the outcomes fall short of those expectations. Study 1 utilized open-ended questions and content analysis to establish that consumers expect ads created through AI-human collaboration to be of superior quality compared to those created through AI-AI or human-human collaboration. This expectation arises from consumers’ beliefs in enhanced informational task management, the generation of innovative ideas, improved creative research, and greater efficiency in collaboration when AI and humans work together. Given these high expectations, Study 2, conducted in an experimental setting, reveals that consumers evaluate subpar ads produced through AI-human collaboration more negatively due to negative expectancy disconfirmation. Study 3 further examines individual differences as a moderating factor, demonstrating that the negative impact of expectancy disconfirmation is more pronounced among individuals with higher expectations of AI-human collaboration superiority