Recollection bias (RB) refers to the phenomenon whereby after an adverse event people report that their risk assessment about a similar future event is presently no higher than their recollection of their pre‐event risk assessment. While previous research has outlined this theoretical construct and generated important empirical findings, there were some limitations. We design and employ a new national representative survey to address these limitations in this study. We examine the existence and persistence of RB among the general public in the context of a number of domestic and international terrorist attacks. We further examine the socioeconomic and political base of RB and the influences of RB on a wide range of citizens’ counterterrorism policy preferences. Our data analyses reveal strong evidence showing the occurrence of RB and its persistence across various forms of terrorism risk. With regard to the socioeconomic and political base, we find that females, older people, political conservatives, and Republicans are less likely to be subject to RB. For the effects of RB on public counterterrorism policy preferences, our analyses demonstrate that this bias significantly dampens public support for a wide range of preventive policy measures and government anti‐terrorism spending. Overall, our study, based on a national representative sample and an extended survey design, provides robust evidence of RB in terrorism risk assessment, and adds further evidence to support the idea that RB is likely a generalizable phenomenon. Implications and suggestions for future research are discussed in the conclusion.
Freedom of the Press and Public Responsiveness
Public responsiveness to policy is contingent on there being a sufficient amount of clear and accurate information about policy available to citizens. It is of some significance then, that there are increasing concerns about limits being placed on media outlets around the world. We examine the impact of these limits on the public’s ability to respond meaningfully to policy by analyzing cross-national variation in the opinion–policy link. Using new measures on spending preferences from Wave 4 of the Comparative Study of Electoral Systems, merged with OECD data on government spending and Freedom House measures of press freedom, we assess the role of mass media in facilitating public responsiveness. We find evidence that when media are weak, so too is public responsiveness to policy. These results highlight the critical role that accurate, unfettered media can play in modern representative democracy.
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- Award ID(s):
- 1728792
- NSF-PAR ID:
- 10176336
- Date Published:
- Journal Name:
- Perspectives on Politics
- ISSN:
- 1537-5927
- Page Range / eLocation ID:
- 1 to 13
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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Abstract -
Douglas Kriner (Ed.)more » « less
Abstract Transparency is essential for public awareness of government activity and in holding officials accountable for their behavior and decisions. However, executives understandably have a desire to maintain autonomy over the flow of information outside of the executive branch, which can sometimes lead to a clash between government openness and executive control over information. This article investigates executive branch transparency along two specific dimensions: threats to the preservation of government information, and the judicial monitoring of executive branch transparency. Federal law directs executive branch actors to preserve government information. Statutes and regulations detail governing policies as well as guidelines that apply to every presidential administration. However, we consider each rule and recommendation through the lens of the Trump administration to gain greater clarity and precision. The Trump administration proves a useful focus for our empirical analysis because of its recency as well as multiple reports of its improper handling and preservation of records. We lay out and develop a typology of threats to record preservation. Preservation conflicts have emerged across multiple presidential administrations and have become increasingly common and visible. Judicial monitoring of executive branch transparency has become a crucial tool to address the withholding of records. We show that federal judges can play a vital role in defining the boundaries of government transparency through their decisions in Freedom of Information Act (FOIA) litigation. The U.S. Department of Justice oversees and often leads the government's defense against FOIA actions. We find that federal judges are most likely to require executive branch responsiveness and disclosure when the judges' underlying policy preferences are most distant from that of the administration. Our findings should inform reforms to protect government transparency.
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null (Ed.)Background The COVID-19 pandemic has caused several disruptions in personal and collective lives worldwide. The uncertainties surrounding the pandemic have also led to multifaceted mental health concerns, which can be exacerbated with precautionary measures such as social distancing and self-quarantining, as well as societal impacts such as economic downturn and job loss. Despite noting this as a “mental health tsunami”, the psychological effects of the COVID-19 crisis remain unexplored at scale. Consequently, public health stakeholders are currently limited in identifying ways to provide timely and tailored support during these circumstances. Objective Our study aims to provide insights regarding people’s psychosocial concerns during the COVID-19 pandemic by leveraging social media data. We aim to study the temporal and linguistic changes in symptomatic mental health and support expressions in the pandemic context. Methods We obtained about 60 million Twitter streaming posts originating from the United States from March 24 to May 24, 2020, and compared these with about 40 million posts from a comparable period in 2019 to attribute the effect of COVID-19 on people’s social media self-disclosure. Using these data sets, we studied people’s self-disclosure on social media in terms of symptomatic mental health concerns and expressions of support. We employed transfer learning classifiers that identified the social media language indicative of mental health outcomes (anxiety, depression, stress, and suicidal ideation) and support (emotional and informational support). We then examined the changes in psychosocial expressions over time and language, comparing the 2020 and 2019 data sets. Results We found that all of the examined psychosocial expressions have significantly increased during the COVID-19 crisis—mental health symptomatic expressions have increased by about 14%, and support expressions have increased by about 5%, both thematically related to COVID-19. We also observed a steady decline and eventual plateauing in these expressions during the COVID-19 pandemic, which may have been due to habituation or due to supportive policy measures enacted during this period. Our language analyses highlighted that people express concerns that are specific to and contextually related to the COVID-19 crisis. Conclusions We studied the psychosocial effects of the COVID-19 crisis by using social media data from 2020, finding that people’s mental health symptomatic and support expressions significantly increased during the COVID-19 period as compared to similar data from 2019. However, this effect gradually lessened over time, suggesting that people adapted to the circumstances and their “new normal.” Our linguistic analyses revealed that people expressed mental health concerns regarding personal and professional challenges, health care and precautionary measures, and pandemic-related awareness. This study shows the potential to provide insights to mental health care and stakeholders and policy makers in planning and implementing measures to mitigate mental health risks amid the health crisis.more » « less
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Yang, Chaowei (Ed.)The COVID-19 pandemic has profoundly impacted the economy and human lives worldwide, particularly the vulnerable low-income population. We employ a large panel data of 5.6 million daily transactions from 2.6 million debit cards owned by the low-income population in the U.S. to quantify the joint impacts of the state lockdowns and stimulus payments on this population’s spending along the inter-temporal, geo-spatial, and cross-categorical dimensions. Leveraging the difference-in-differences analyses at the per card and zip code levels, we uncover three key findings. (1) Inter-temporally, the state lockdowns diminished the daily average spending relative to the same period in 2019 by $3.9 per card and $2,214 per zip code, whereas the stimulus payments elevated the daily average spending by $15.7 per card and $3,307 per zip code. (2) Spatial heterogeneity prevailed: Democratic zip codes displayed much more volatile dynamics, with an initial decline three times that of Republican zip codes, followed by a higher rebound and a net gain after the stimulus payments; also, Southwest exhibited the highest initial decline whereas Southeast had the largest net gain after the stimulus payments. (3) Across 26 categories, the stimulus payments promoted spending in those categories that enhanced public health and charitable donations, reduced food insecurity and digital divide, while having also stimulated non-essential and even undesirable categories, such as liquor and cigar. In addition, spatial association analysis was employed to identify spatial dependency and local hot spots of spending changes at the county level. Overall, these analyses reveal the imperative need for more geo- and category-targeted stimulus programs, as well as more effective and strategic policy communications, to protect and promote the well-being of the low-income population during public health and economic crises.more » « less
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Association for the Advancement of Artificial Intelligence (Ed.)Given the heightened global awareness and attention to the negative externalities of plastics use, many state and local governments are considering legislation that will limit single-use plastics for consumers and retailers under extended producer responsibility laws. Considering the growing momentum of these climate regulations globally, there is a need for reliable and cost-effective measures of the public response to this rulemaking for inference and prediction. Automated computational approaches such as generative AI could enable real-time discovery of consumer preferences for regulations but have yet to see broad adoption in this domain due to concerns about evaluation costs and reliability across large-scale social data. In this study, we leveraged the zero and few-shot learning capabilities of GPT-4 to classify public sentiment towards regulations with increasing complexity in expert prompting. With a zero-shot approach, we achieved a 92% F1 score (s.d. 1%) and 91% accuracy (s.d. 1%), which resulted in three orders of magnitude lower research evaluation cost at 0.138 pennies per observation. We then use this model to analyze 5,132 tweets related to the policy process of the California SB-54 bill, which mandates user fees and limits plastic packaging. The policy study reveals a 12.4% increase in opposing public sentiment immediately after the bill was enacted with no significant changes earlier in the policy process. These findings shed light on the dynamics of public engagement with lower cost models for research evaluation. We find that public opposition to single-use plastics regulation becomes evident in social data only when a bill is effectively enacted.more » « less
- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.1017/S1537592719003852
