scholarly journals Identifying and Measuring Conditional Policy Preferences: The Case of Opening Schools During a Pandemic

2020 ◽  
Author(s):  
Jon Green ◽  
Matthew Baum ◽  
James Druckman ◽  
David Lazer ◽  
Katherine Ognyanova ◽  
...  
Keyword(s):  
Machine Learning ◽  
Public Opinion ◽  
Survey Research ◽  
Large Scale ◽  
Policy Preferences ◽  
General Phenomenon ◽  
Survey Experiments ◽  
The Status ◽  
Large Scale Survey ◽  
Conditional Preferences

An individual’s issue preferences are non-separable when they depend on other issue outcomes (Lacy 2001a), presenting measurement challenges for traditional survey research. We extend this logic to the broader case of conditional preferences, in which policy preferences depend on the status of conditions with inherent levels of uncertainty -- and are not necessarily policies themselves. We demonstrate new approaches for measuring conditional preferences in two large-scale survey experiments regarding the conditions under which citizens would support reopening schools in their communities during the COVID-19 pandemic. By drawing on recently-developed methods at the intersection of machine learning and causal inference, we identify which citizens are most likely to have school reopening preferences that depend on additional considerations. The results highlight the advantages of using such approaches to measure conditional preferences, which represent an underappreciated and general phenomenon in public opinion.

The subjective meaning of gender: how survey designs affect perceptions of femininity and masculinity

2020 ◽  
Author(s):  
Elias Markstedt ◽  
Lena Wängnerud ◽  
Maria Solevid ◽  
Monika Djerf-Pierre
Keyword(s):  
Survey Research ◽  
Political Ideology ◽  
Large Scale ◽  
Rating Scales ◽  
The Core ◽  
Femininity And Masculinity ◽  
Core Concepts ◽  
Large Scale Survey ◽  
Set Up ◽  
Survey Designs

The rationale for this study is that self-categorising rating scales are becoming increasingly popular in large-scale survey research moving beyond binary ways of measuring gender. We are referring here to the use of rating scales that are similar to graded scales capturing left–right or liberal–conservative political ideology, that is, scales that do not include predefinitions of the core concepts (femininity/masculinity, as compared to left/right or liberal/conservative). Yet, previous studies including such non-binary gender measures have paid little attention to potential effects of survey designs. Using an experimental set-up, we are able to show that sequencing of gender measurements influences the answers received. Men were especially affected by our treatments and rated themselves as significantly ‘less masculine’ when prompted to reason about the meaning of gender prior to self-categorisation on scales measuring degrees of femininity and masculinity. Moreover, self-categorising seems to trigger more biological understandings of gender than anticipated in theory.<br /><br />Key messages<ul><li>Sequencing of gender measurements influences answers received in a survey.</li><li>Men rate themselves as ‘less masculine’ when prompted to reason about gender prior to answering gender scales.</li><li>Self-categorising gender scales trigger more biological understandings of gender than anticipated in theory.</li></ul>

scholarly journals Stress Detection using Natural Language Processing and Machine Learning over social Interactions

2021 ◽  
Author(s):  
Tanya Nijhawan ◽  
Girija Attigeri ◽  
Ananthakrishna T
Keyword(s):  
Machine Learning ◽  
Social Interactions ◽  
Language Processing ◽  
Large Scale ◽  
Latent Dirichlet Allocation ◽  
Well Being ◽  
Machine Learning Algorithms ◽  
Textual Data ◽  
The Status ◽  
Micro Level

Abstract Cyberspace is a vast soapbox for people to post anything that they witness in their day-to-day lives. Subsequently, it can be used as a very effective tool in detecting the stress levels of an individual based on the posts and comments shared by him/her on social networking platforms. We leverage large-scale datasets with tweets to successfully accomplish sentiment analysis with the aid of machine learning algorithms. We take the help of a capable deep learning pre-trained model called BERT to solve the problems which come with sentiment classification. The BERT model outperforms a lot of other well-known models for this job without any sophisticated architecture. We also adopted Latent Dirichlet Allocation which is an unsupervised machine learning method that’s skilled in scanning a group of documents, recognizing the word and phrase patterns within them, and gathering word groups and alike expressions that most precisely illustrate a set of documents. This helps us predict which topic is linked to the textual data. With the aid of the models suggested, we will be able to detect the emotion of users online. We are primarily working with Twitter data because Twitter is a website where people express their thoughts often. In conclusion, this proposal is for the well- being of one’s mental health. The results are evaluated using various metric at macro and micro level and indicate that the trained model detects the status of emotions bases on social interactions.

Integration of Refugees and Support for the Ethos of Conflict

2017 ◽  
Vol 62 (9) ◽  
pp. 2040-2067 ◽  
Author(s):  
Jonathan Hall
Keyword(s):  
Public Opinion ◽  
Social Identity ◽  
Large Scale ◽  
The State ◽  
Ethnic Cleansing ◽  
Public Opinion Research ◽  
Global Migration ◽  
Large Scale Survey ◽  
Social Identity Complexity ◽  
Migration Crisis

Following forced expulsion and campaigns of ethnic cleansing, substantial portions of national communities affected by conflict no longer live within the boundaries of the state. Nevertheless, existing wartime and postwar public opinion research is largely confined to countries directly affected by conflict. As a result, current research may overlook important war-affected populations and processes shaping their opinions. I address this problem by examining the question: does incorporation in settlement countries reduce support for conflict ideology? Examining this question requires new microdata. I examine the results of a large-scale survey of ex-Yugoslavs in Sweden. The findings suggest that incorporation undermines support for conflict ideology by increasing the socioeconomic security and social identity complexity of migrants. This has important implications for multiculturalism policies in the context of the current global migration crisis.

Public Opinion on Healthcare

2021 ◽  
pp. 49-68
Author(s):  
Diana Burlacu ◽  
Andra Roescu
Keyword(s):  
Public Opinion ◽  
Regional Variation ◽  
Large Scale ◽  
Healthcare Systems ◽  
The Political ◽  
Public Healthcare ◽  
Healthcare Provision ◽  
Health Policy Reform ◽  
Large Scale Survey ◽  
New Research

This chapter explores public opinion on healthcare from both theoretical and empirical perspectives. It critically reviews the literature surrounding public opinion on healthcare, teasing out the main concepts used—support for public healthcare provision/spending, overall evaluations of the healthcare systems, and the political salience of healthcare—while also discussing implications, gaps, and potential new research avenues. The chapter examines the operationalization of these key measures in large-scale survey items and compares trends in Europe over time. While Europeans mostly agree that it is the government’s responsibility to ensure adequate healthcare, there is much more regional variation when it comes to satisfaction with healthcare systems, and their political salience. The chapter concludes by arguing for the need to further examine the link between these key public opinion measures and their impact on health policy reform.

scholarly journals Exposure to Arguments and Evidence Changes Partisan Attitudes Even in the Face of Countervailing Leader Cues

2021 ◽  
Author(s):  
Ben M Tappin
Keyword(s):  
Public Opinion ◽  
Large Scale ◽  
Motivated Reasoning ◽  
Survey Experiment ◽  
Policy Positions ◽  
Policy Issues ◽  
Party Leaders ◽  
The Face ◽  
Large Scale Survey ◽  
Demographic Subgroups

Patterns of public opinion recently observed in American politics tempt the conclusion that substantive arguments and evidence are less effective, or ineffective, at changing partisan minds when they overtly contradict cues from in-party leaders. This conclusion follows naturally from theories of partisan motivated reasoning. However, observations of public opinion do not provide the counterfactual outcomes required to draw this conclusion. Here we report a large-scale survey experiment in which we randomized exposure to the policy positions of Donald Trump and Joe Biden, as well as information that overtly contradicts their positions. Our design incorporates 24 policy issues and 48 information treatments. We find that the information does persuade partisans on average, and, critically, fully retains its persuasive force even when paired with countervailing cues from in-party leaders. This result holds across policy issues, demographic subgroups, and one- and two-sided cue environments, and is puzzling for partisan motivated reasoning theory.

Survey Data as Evidence in Trademark Infringement Cases

1965 ◽  
Vol 2 (1) ◽  
pp. 64-73 ◽  
Author(s):  
Donald L. Thompson
Keyword(s):  
Public Opinion ◽  
Survey Research ◽  
Survey Data ◽  
Research Problem ◽  
Legal Evidence ◽  
Trademark Infringement ◽  
Public Opinion Surveys ◽  
Unfair Trade ◽  
The Status ◽  
Conceptual Problems

➤There are still many basic, unresolved issues with respect to the status of survey data as legal evidence. In cases involving unfair trade, particularly alleged trademark infringement, public opinion surveys exist as an attractive, logical basis for making a decision. Increasingly, litigants have attempted to introduce such data, often with quite unpredicted and unexpected results. Evaluation is made of major survey research problem areas in terms of their legal significance. The article is designed to acquaint the survey research specialist with some of the practical and conceptual problems encountered in preparing survey data for court.

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