AI Insights

• The use of NLP techniques may introduce errors or omissions due to the complexity of human language. (ML: 0.99)👍👎
• The authors acknowledge that SMART has limitations and potential biases, including issues related to privacy and bias in social media data. (ML: 0.98)👍👎
• The authors also discuss the ethical considerations of using social media data for research purposes, including issues related to privacy and bias. (ML: 0.98)👍👎
• SMART is a useful tool for analyzing social media data related to social movements, but it has limitations and potential biases that must be considered when interpreting results. (ML: 0.98)👍👎
• Natural language processing (NLP): a subfield of artificial intelligence that deals with the interaction between computers and humans in natural language. (ML: 0.98)👍👎
• The use of NLP techniques in SMART can help researchers identify key themes and emotions in social media posts, but may also introduce errors or omissions due to the complexity of human language. (ML: 0.98)👍👎
• This paper presents SMART (Social Movement Analysis & Reasoning Tool), a tool for analyzing social media data related to social movements. (ML: 0.97)👍👎
• The authors use the #MeToo and #BlackLivesMatter movements as case studies to demonstrate the effectiveness of SMART. (ML: 0.96)👍👎
• The tool uses natural language processing techniques, including keyword extraction and sentiment analysis, to identify key themes and emotions in social media posts. (ML: 0.94)👍👎
• Sentiment analysis: the process of determining the emotional tone or attitude conveyed by a piece of text. (ML: 0.94)👍👎
• Social movement: a collective effort by individuals or groups to bring about change on a societal level. (ML: 0.88)👍👎

Abstract
Social movements supporting the UN's Sustainable Development Goals (SDGs) play a vital role in improving human lives. If journalists were aware of the relationship between social movements and external events, they could provide more precise, time-sensitive reporting about movement issues and SDGs. Our SMART system achieves this goal by collecting data from multiple sources, extracting emotions on various themes, and then using a transformer-based forecasting engine (DEEP) to predict quantity and intensity of emotions in future posts. This paper demonstrates SMART's Retrospective capabilities required by journalists via case studies analyzing social media discussions of the #MeToo and #BlackLivesMatter before and after the 2024 U.S. election. We create a novel 1-year dataset which we will release upon publication. It contains over 2.7M Reddit posts and over 1M news articles. We show that SMART enables early detection of discourse shifts around key political events, providing journalists with actionable insights to inform editorial planning. SMART was developed through multiple interactions with a panel of over 20 journalists from a variety of news organizations over a 2-year period, including an author of this paper.

Why we are recommending this paper?
Due to your Interest in Political Movements

This paper directly addresses social movements, a core interest, and utilizes a tool to analyze them. The case studies on prominent movements like #MeToo and #BlackLivesMatter align closely with your focus on activism and political movements.

AI Insights

• The release of ChatGPT, a widely adopted Large Language Model (LLM), has significantly intensified political polarization among users. (ML: 0.98)👍👎
• The findings contribute to a growing body of research exploring the societal consequences of generative AI and highlight the multifaceted nature of LLMs' impact on public discourse. (ML: 0.98)👍👎
• ChatGPT amplifies users' responses in ways that align with the prevailing narrative of each discussion thread, thereby reinforcing existing positions. (ML: 0.98)👍👎
• The increase in polarization does not stem from the creation of more persuasive or ideologically extreme original content, but rather from how ChatGPT-generated comments respond to existing posts. (ML: 0.97)👍👎
• The study's findings suggest that LLMs like ChatGPT can have a dual effect on public discourse, amplifying ideological signals while also reducing affective polarization. (ML: 0.97)👍👎
• Generative AI: Artificial intelligence that generates human-like content, such as text or images, based on patterns learned from large datasets. (ML: 0.95)👍👎
• Large Language Model (LLM): A type of artificial intelligence designed to process and generate human language, often used for tasks like chatbots or language translation. (ML: 0.95)👍👎
• Despite this increase in ideological polarization, indicators of hostility and toxicity declined following ChatGPT's release, suggesting a decoupling between political extremity and emotional animosity in online discourse. (ML: 0.94)👍👎
• Ideological polarization: The division of people into two groups with opposing views or ideologies, often leading to increased conflict and decreased cooperation. (ML: 0.87)👍👎
• Affective polarization: The emotional aspect of ideological polarization, characterized by increased hostility, anger, and resentment towards those with opposing views. (ML: 0.79)👍👎

Abstract
The emergence of large language models (LLMs) is reshaping how people engage in political discourse online. We examine how the release of ChatGPT altered ideological and emotional patterns in the largest political forum on Reddit. Analysis of millions of comments shows that ChatGPT intensified ideological polarization: liberals became more liberal, and conservatives more conservative. This shift does not stem from the creation of more persuasive or ideologically extreme original content using ChatGPT. Instead, it originates from the tendency of ChatGPT-generated comments to echo and reinforce the viewpoint of original posts, a pattern consistent with algorithmic sycophancy. Yet, despite growing ideological divides, affective polarization, measured by hostility and toxicity, declined. These findings reveal that LLMs can simultaneously deepen ideological separation and foster more civil exchanges, challenging the long-standing assumption that extremity and incivility necessarily move together.

Why we are recommending this paper?
Due to your Interest in Political Philosophy

This research investigates the impact of large language models on political discourse, a critical area given your interest in democratic processes and political theory. Analyzing shifts in ideological and emotional patterns within online forums is highly relevant to your concerns about political engagement.

AI Insights

• Moral foundations theory suggests that individuals rely on different sets of moral principles when making judgments about robot abuse. (ML: 0.98)👍👎
• Results show that those who score high in the 'care/harm' foundation tend to be more empathetic towards robots, while those who score high in the 'loyalty/betrayal' foundation tend to be less empathetic. (ML: 0.98)👍👎
• The study finds that people's responses to robot abuse are influenced by their individual differences in moral foundations and anthropomorphism. (ML: 0.97)👍👎
• Anthropomorphism is a key factor in determining empathy towards robots, with more human-like features leading to increased empathy. (ML: 0.97)👍👎
• Moral Foundations Theory: A psychological framework that suggests people rely on different sets of moral principles when making judgments about right and wrong. (ML: 0.97)👍👎
• The study highlights the importance of considering both anthropomorphism and moral foundations theory when designing robots and developing policies related to robot abuse. (ML: 0.96)👍👎
• The study examines how people respond to robot abuse, focusing on the role of anthropomorphism and moral foundations theory. (ML: 0.96)👍👎
• Empathy: The ability to understand and share the feelings of another. (ML: 0.95)👍👎
• Anthropomorphism: The attribution of human characteristics or behavior to non-human entities, such as objects or animals. (ML: 0.95)👍👎
• Uncanny Valley: A concept in aesthetics describing the feeling of eeriness or revulsion that can occur when something that is not quite human-like is perceived as being too close to being human-like. (ML: 0.90)👍👎

Abstract
As robots become increasingly integrated into daily life, understanding responses to robot mistreatment carries important ethical and design implications. This mixed-methods study (N = 201) examined how anthropomorphic levels and moral foundations shape reactions to robot abuse. Participants viewed videos depicting physical mistreatment of robots varying in humanness (Spider, Twofoot, Humanoid) and completed measures assessing moral foundations, anger, and social distance. Results revealed that anthropomorphism determines whether people extend moral consideration to robots, while moral foundations shape how they reason about such consideration. Qualitative analysis revealed distinct reasoning patterns: low-progressivism individuals employed character-based judgments, while high-progressivism individuals engaged in future-oriented moral deliberation. Findings offer implications for robot design and policy communication.

Why we are recommending this paper?
Due to your Interest in Human Rights

Exploring responses to robot mistreatment connects to your interest in human rights and ethical considerations surrounding technology. Understanding how moral foundations influence reactions is a valuable perspective for your broader study of political philosophy.

AI Insights

• The framework yields five testable propositions that discipline the empirical analysis. (ML: 0.98)👍👎
• Fragility beliefs: The probability of extreme negative outcomes. (ML: 0.98)👍👎
• Proposition 2 shows that persistent increases in fragility beliefs lead to persistent declines in consumer confidence, even if near-term activity indicators partially recover. (ML: 0.94)👍👎
• Stabilization: Policy responses, learning, international support, and institutional adaptation. (ML: 0.94)👍👎
• Proposition 3 describes a panic-adaptation dynamic in business tendency trajectories, where they drop sharply at the time of the shock and subsequently recover partially as disruption decays and mobilization persists. (ML: 0.92)👍👎
• Risk premia: The extra return demanded by investors for taking on risk. (ML: 0.92)👍👎
• Proposition 5 highlights global financial cycle dominance in venture capital, where large global repricing of growth capital can generate declines across many countries simultaneously. (ML: 0.89)👍👎
• Mobilization: Fiscal expansion and sectoral reallocation. (ML: 0.88)👍👎
• Proposition 4 demonstrates risk-growth decoupling, where an increase in fragility beliefs raises sovereign spreads while an increase in mobilization and stabilization raises the composite leading indicator (CLI). (ML: 0.82)👍👎
• Proposition 1 states that sovereign risk premia rise sharply at the time of the shock and remain elevated relative to their counterfactual path. (ML: 0.76)👍👎

Abstract
Extreme political shocks may reshape economies not only through contemporaneous disruption but by altering beliefs about the distribution of future states. We study how such belief ruptures affect the cost of capital, expectations, and macroeconomic dynamics, using the October 7, 2023 attack on Israel as a precisely timed shock. Leveraging monthly data from 2008 to 2025 and a donor pool of advanced economies, we estimate counterfactual paths using a matrix completion design with rolling-window cross-validation and placebo-based inference, corroborated by synthetic difference-in-differences. We document three core findings. First, long-horizon sovereign risk of Israel is persistently repriced. Ten-year yields and spreads relative to the United States rise sharply and remain elevated. Second, household welfare beliefs deteriorate durably, as reflected in consumer confidence. Third, medium-run momentum improves, captured by a strong rise in the OECD composite leading indicator. These patterns reveal risk-growth decoupling where tail-risk premia rise even as medium-horizon activity expectations strengthen. Our results highlight belief-driven channels as a central mechanism through which extreme ruptures shape macro-financial outcomes.

Why we are recommending this paper?
Due to your Interest in Political Economy

This paper examines the economic consequences of extreme political shocks, aligning with your interest in political economy. Analyzing how these events impact beliefs and macroeconomic dynamics provides a crucial framework for understanding political movements' effects.

AI Insights

• The study examines the relationship between electoral polls and different sources of uncertainty during the 2020 and 2024 US presidential elections. (ML: 0.98)👍👎
• The study's findings have implications for understanding the relationship between electoral polls and economic uncertainty, highlighting the importance of considering the broader economic and political environment when studying electoral polls. (ML: 0.97)👍👎
• The findings highlight the importance of considering the broader economic and political environment when studying electoral polls, as correlations may change substantially across periods characterized by different types and magnitudes of shocks. (ML: 0.96)👍👎
• Conditional Correlation models: A type of multivariate GARCH model used to analyze the relationship between different variables. (ML: 0.96)👍👎
• The study suggests that market expectations and voter sentiment may interact more strongly in contexts of heightened uncertainty, while such links can vanish in calmer environments or when external and exceptional events directly influence the electoral campaign regardless of economic uncertainty. (ML: 0.94)👍👎
• GARCH (Generalized Autoregressive Conditional Heteroskedasticity) model: A statistical model used to analyze time series data with changing volatility. (ML: 0.93)👍👎
• The Conditional Correlation models used in the study reveal striking differences between the two electoral periods, with correlations being time-varying and highly sensitive to exogenous shocks and political events in 2020, but close to zero, stable, and largely unresponsive to news in 2024. (ML: 0.92)👍👎
• Multivariate GARCH model: An extension of the GARCH model that allows for multiple variables and their interactions. (ML: 0.90)👍👎
• Markov Switching model: A type of nonlinear model that captures regime shifts in a time series. (ML: 0.86)👍👎
• Smooth Transition model: A type of nonlinear model that captures gradual changes in a time series. (ML: 0.86)👍👎

Abstract
This paper examines the dynamic relationship between electoral polls and indicators of economic and financial uncertainty during the last two U.S. presidential elections (2020 and 2024). Using daily polling data on Donald Trump and measures such as the Aruoba-Diebold-Scotti Business Conditions Index, the 5-year Breakeven Inflation Rate, the Trade Policy Uncertainty index, and the VIX, we estimate conditional correlation models to capture time-varying interactions. The analysis reveals that in 2020, correlations between polls and uncertainty measures were highly dynamic and event-driven, reflecting the influence of exogenous shocks (COVID-19, oil price collapse) and political milestones (primaries, debates). In contrast, during the 2024 campaign, correlations remained close to zero, stable, and largely unresponsive to shocks, suggesting that entrenched polarization and non-economic events (e.g., assassination attempt, candidate changes) muted the economic channel. The study highlights how the interplay between voter sentiment, financial markets, and uncertainty varies across electoral contexts, offering a methodological contribution through the application of Dynamic Conditional Correlation models to political data and policy-relevant insights on the conditions under which economic fundamentals influence electoral dynamics.

Why we are recommending this paper?
Due to your Interest in Political Economy

This research directly investigates the relationship between electoral polls and economic uncertainty, a key area for understanding democratic systems and political behavior. Analyzing data from recent elections provides valuable insights into the dynamics of political processes.