Climate change mitigation has been one of the world’s most salient issues for the past three decades. However, global policy attention has been partially diverted to address the COVID-19 pandemic for the past two years. Here, we explore the impact of the pandemic on the frequency and content of climate change discussions on Twitter for the period of 2019 – 2021. Consistent with the “finite pool of worry” hypothesis, both at the annual level and on a daily basis, a larger number of COVID-19 cases and deaths is associated with a smaller number of “climate change” tweets. Climate change discussion on Twitter decreased despite (a) a larger Twitter daily active usage in 2020 and 2021, (b) greater coverage of climate change in traditional media in 2021, (c) a larger number of North Atlantic ocean hurricanes, and (d) a larger wildland fires area in the United States in 2020 and 2021. Further evidence supporting the finite pool of worry is the significant relationship between daily COVID-19 cases/deaths on the one hand and the public sentiment and emotional content of “climate change” tweets on the other. In particular, increasing COVID-19 numbers decrease negative sentiment in climate change tweets and the emotions related to worry and anxiety, such as fear and anger.

This study examines the relationship between vaccine mandates and public sentiment towards vaccines and health officials on Twitter. I analyzed 6.6 million vaccine-related tweets from July 2021 to February 2022 in the United States. Leveraging a large language model, BERT, I identified tweets discussing vaccine mandates even when lacking explicit keywords. Compared to non-mandate tweets, those mentioning mandates exhibit greater negativity, anger, and freedom-related language. Furthermore, increased state-level discussion of mandates correlates with rising levels of negativity toward both vaccines and public health officials and rising leves of anger toward vaccines. Finally, greater disparity in vaccination progress across counties within a state is associated with increased anger in tweets directed towards both.

Building on the deservingness heuristic–evaluating recipients based on need and effort–from evolutionary psychology, this study integrates it with the conditional altruism model from political science and economics to understand individual similarities and differences in redistributive preferences. We found that, when a recipient’s effort is known, most participants’ choices could be explained by the need and effort effect derived from the model. Furthermore, participants mainly fall into three categories: highly responsive to effort, less responsive, and self-interested. These categories reflect how much weight individuals place on recipient effort in the utility function. However, when effort information is indirect and partial, income becomes the primary factor, even if effort can be partially inferred.

We propose that experiencing extreme income inequality should change attitudes and behaviors regarding redistribution. However, measuring experiences is an empirical challenge. Therefore, we use an experimental economic game to test for differences in redistributive behavior by experimentally manipulating the level of inequality experienced. In our game, subjects are given an initial distribution of tokens but are allowed to propose redistribution. Because the literature suggests that people prefer less redistribution if inequality is determined through a fair process, we also manipulate the source of inequality. For some of our players, the initial distribution of tokens is determined through performance on a slider task (merit condition). For the rest, the initial distribution is randomly generated (luck condition). We find that subjects care about merit even at extreme inequality—those who experience merit-based inequality create equality less than their counterparts who experience luck-based inequality. However, we find that most of our subjects that experience any inequality redistribute to create equal outcomes but those exposed to extreme levels of inequality want more equality. For example, people who experience extreme inequality, even when merit-based, still want more equality than those who experience non-extreme types of inequality. Overall, we find that this relationship between extreme inequality and meritocracy is more complex than at first glance.

Approximately one-third of global greenhouse gas emissions are attributed to food systems, and adopting a plant-rich diet is an effective way to mitigate climate change by individuals. Nevertheless, the entire population will benefit from this individual’s efforts to adopt a plant-rich diet, resulting in an incentive for individuals to free-ride. Despite the fact that adopting a plant-rich diet is effective in reducing greenhouse gas emissions, it remains unpopular with Americans. In the current proposal, we design a vignette experiment to examine how others’ behavior and perceptions of reciprocity influence Americans’ willingness to adopt plant-rich diets and support policies that promote plant-rich diets, such as federal diet guidelines, meat taxes, the proposed PLANT Act, and school meals. Our pilot study found that the dynamic trend treatment and the reciprocity cue influenced several policy attitudes promoting a plant-rich diet.

Economic sanctions have become a major instrument of international politics. However, the effectiveness of economic sanctions depends on both public opinions in the targeted country and the country that imposes sanctions. We explored whether certain emotions—anxiety and anger—were associated with specific attitudes towards the sanctions against Russia in 2022. Existing literature in psychology and political science has demonstrated that anxiety and anger provoke different risk perceptions and risk attitudes and, in turn, change political attitudes, such as support for a war. Therefore, we proposed that the fear of the consequences of the economic sanctions, such as inflation and shortage, resulted in negative attitudes toward the sanctions among Americans and negative attitudes toward the invasion among Russians. In contrast, the American citizens angered by the invasion were more likely to support the sanctions, and the Russian citizens angered by the sanctions were more likely to support the invasion. Using the data from Twitter and VKontakte, we applied emotion analysis and automated text classification to measure the emotions and attitudes toward the sanctions in Americans and Russians. In the preliminary analysis, we found that the pro-sanction tweets were angrier than the anti-sanction tweets. In contrast, the anti-sanction tweets were more anxious than the pro-sanction tweets.