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Stance Detection for Gauging Public Opinion: A Statistical Analysis of the Difference Between Tweet-Based and User-Based Stance in Twitter
Abstract
Public opinion provides policy makers with meaningful information on how the public feels towards a certain issue. Stance detection in social media is the problem of automatically determining the standpoint expressed in a specific tweet towards a target of interest such as a topic or an issue. One such application of stance detection is to gauge public opinion from Twitter data as an alternative to traditional methods such as surveys and polls. In this paper, we define user-based stance and claim that the aggregation of user-based stance is more aligned with the goal of gauging public opinion than the aggregation of tweet-based stance. Our analysis shows that tweet-based stance aggregation leads to significantly different results than user-based stance aggregation, and the effect size varies per stance class. This paper provides the basis and argument for user-based stance to measure public opinion from Twitter data.KeywordsData analysisPublic opinionStance detectionStance analysis
Today, administering COVID-19 vaccines at a societal scale has been deemed as the most appropriate way to defend against the COVID-19 pandemic. This global vaccination drive naturally fueled a possibility of pro-vax and anti-vax users strongly expressing their supports and concerns regarding the vaccines in online social media platforms. Understanding this online discourse is crucial for policy makers. This understanding is likely to impact the success of vaccination drives and might even impact the final outcome of our fight against the pandemic. The goal of this work is to improve this understanding using the lens of Twitter-discourse data. We first develop a classifier to categorize users according to their vaccine-related stance with high precision (97%). Using this method we detect and investigate specific user-groups who posted about vaccines in pre-COVID and COVID times. We specifically identify distinct topics that these users talk about, and investigate how vaccine-related discourse has changed between pre-COVID times and COVID times. Finally, for the first time, we investigate the change of vaccine-related stances in Twitter users and shed light on potential reasons for such changes in stance.
Wearing a mask by the general public has been a controversial issue from the beginning of the COVID-19 pandemic as the public authorities have had mixed messages, either advising people not to wear masks if uninfected, to wear as a protective measure, to wear them only when inside a building/room with insufficient air flow or to wear them in all the public places. To date, the governments have had different policies regarding mask-wearing by the general public depending on the COVID-19 pandemic evolution. In this context, the paper analyzes the general public's opinion regarding mask-wearing for the one-year period starting from 9 January 2020, when the first tweet regarding mask-wearing in the COVID-19 context has been posted. Classical machine learning and deep learning algorithms have been considered in analyzing the 8,795,633 tweets extracted. A random sample of 29,613 tweets has been extracted and annotated. The tweets containing news and information related to mask-wearing have been included in the neutral category, while the ones containing people's opinions (for or against) have been marked using a symmetrical approach into in favor and against categories. Based on the analysis, it has been determined that most of the mask tweets are in the area of in favor or neutral, while a smaller percentage of tweets and retweets are in the against category. The evolution of the opinions expressed through tweets can be further monitored for extracting the public perspective on mask-wearing in times of COVID-19.
Background
False claims about COVID-19 vaccines can undermine public trust in ongoing vaccination campaigns, posing a threat to global public health. Misinformation originating from various sources has been spreading on the web since the beginning of the COVID-19 pandemic. Antivaccine activists have also begun to use platforms such as Twitter to promote their views. To properly understand the phenomenon of vaccine hesitancy through the lens of social media, it is of great importance to gather the relevant data.
Objective
In this paper, we describe a data set of Twitter posts and Twitter accounts that publicly exhibit a strong antivaccine stance. The data set is made available to the research community via our AvaxTweets data set GitHub repository. We characterize the collected accounts in terms of prominent hashtags, shared news sources, and most likely political leaning.
Methods
We started the ongoing data collection on October 18, 2020, leveraging the Twitter streaming application programming interface (API) to follow a set of specific antivaccine-related keywords. Then, we collected the historical tweets of the set of accounts that engaged in spreading antivaccination narratives between October 2020 and December 2020, leveraging the Academic Track Twitter API. The political leaning of the accounts was estimated by measuring the political bias of the media outlets they shared.
Results
We gathered two curated Twitter data collections and made them publicly available: (1) a streaming keyword–centered data collection with more than 1.8 million tweets, and (2) a historical account–level data collection with more than 135 million tweets. The accounts engaged in the antivaccination narratives lean to the right (conservative) direction of the political spectrum. The vaccine hesitancy is fueled by misinformation originating from websites with already questionable credibility.
Conclusions
The vaccine-related misinformation on social media may exacerbate the levels of vaccine hesitancy, hampering progress toward vaccine-induced herd immunity, and could potentially increase the number of infections related to new COVID-19 variants. For these reasons, understanding vaccine hesitancy through the lens of social media is of paramount importance. Because data access is the first obstacle to attain this goal, we published a data set that can be used in studying antivaccine misinformation on social media and enable a better understanding of vaccine hesitancy.
To control the use of e-cigarette, Indonesia plan to establish a regulation that embodies all the concerns, sentiments, and opinions of public This study aims to identify public opinions in social media Twitter by classifying tweets into group in favor or against e-cigarette and explore dominant topics of each group. This research obtained 15,373 tweets between June 2019 – May 2020 that is classified into 4 labels: Against, Favor, Neutral, and Irrelevant. The best model was
selected with specification: 3 features (Count, Unigram, and Bigram), Logistic Regression algorithm, and three-stage classification pipeline (F1-score = 0.807). As for topic modelling, corpus Against and Favor are used to retrieve dominant topics. We chose Non-negative Matrix Factorization algorithm with k=6 and achieve high coherence scores, which are 0.962004 for corpus Against and 0.999736 for corpus Favor.
Society and individuals are negatively influenced both politically and socially by the widespread increase of fake news either way generated by humans or machines. In the era of social networks, the quick rotation of news makes it challenging to evaluate its reliability promptly. Therefore, automated fake news detection tools have become a crucial requirement. To address the aforementioned issue, a hybrid Neural Network architecture, that combines the capabilities of CNN and LSTM, is used with two different dimensionality reduction approaches, Principle Component Analysis (PCA) and Chi-Square. This work proposed to employ the dimensionality reduction techniques to reduce the dimensionality of the feature vectors before passing them to the classifier. To develop the reasoning, this work acquired a dataset from the Fake News Challenges (FNC) website which has four types of stances: agree, disagree, discuss, and unrelated. The nonlinear features are fed to PCA and chi-square which provides more contextual features for fake news detection. The motivation of this research is to determine the relative stance of a news article towards its headline. The proposed model improves results by ~4% and ~20% in terms of Accuracy and F1-score. The experimental results show that PCA outperforms than Chi-square and state-of-the-art methods with 97.8% accuracy.
Social media have become a common way for people to express their personal viewpoints, including sentiments about health topics. We present the results of an opinion mining analysis on vaccination performed on Twitter from September 2016 to August 2017 in Italy. Vaccine-related tweets were automatically classified as against, in favor or neutral in respect of the vaccination topic by means of supervised machine-learning techniques. During this period, we found an increasing trend in the number of tweets on this topic. According to the overall analysis by category, 60% of tweets were classified as neutral, 23% against vaccination, and 17% in favor of vaccination. Vaccine-related events appeared able to influence the number and the opinion polarity of tweets. In particular, the approval of the decree introducing mandatory immunization for selected childhood diseases produced a prominent effect in the social discussion in terms of number of tweets. Opinion mining analysis based on Twitter showed to be a potentially useful and timely sentinel system to assess the orientation of public opinion toward vaccination and, in future, it may effectively contribute to the development of appropriate communication and information strategies.
A reasonable approach for fact checking a claim involves retrieving potentially relevant documents from different sources (e.g., news websites, social media, etc.), determining the stance of each document with respect to the claim, and finally making a prediction about the claim's factuality by aggregating the strength of the stances, while taking the reliability of the source into account. Moreover, a fact checking system should be able to explain its decision by providing relevant extracts (ra-tionales) from the documents. Yet, this setup is not directly supported by existing datasets, which treat fact checking, document retrieval, source credibility, stance detection and rationale extraction as independent tasks. In this paper, we support the interdependencies between these tasks as annotations in the same corpus. We implement this setup on an Arabic fact checking corpus, the first of its kind.
Opinion polls have been the bridge between public opinion and politicians in elections. However, developing surveys to disclose people's feedback with respect to economic issues is limited, expensive, and time-consuming. In recent years, social media such as Twitter has enabled people to share their opinions regarding elections. Social media has provided a platform for collecting a large amount of social media data. This paper proposes a computational public opinion mining approach to explore the discussion of economic issues in social media during an election. Current related studies use text mining methods independently for election analysis and election prediction; this research combines two text mining methods: sentiment analysis and topic modeling. The proposed approach has effectively been deployed on millions of tweets to analyze economic concerns of people during the 2012 US presidential election.
Social media are an important source of information about the political issues, reflecting, as well as influencing, public mood. We present an analysis of Twitter data, collected over 6 weeks before the Brexit referendum, held in the UK in June 2016. We address two questions: what is the relation between the Twitter mood and the referendum outcome, and who were the most influential Twitter users in the pro- and contra-Brexit camps? First, we construct a stance classification model by machine learning methods, and are then able to predict the stance of about one million UK-based Twitter users. The demography of Twitter users is, however, very different from the demography of the voters. By applying a simple age-adjusted mapping to the overall Twitter stance, the results show the prevalence of the pro-Brexit voters, something unexpected by most of the opinion polls. Second, we apply the Hirsch index to estimate the influence, and rank the Twitter users from both camps. We find that the most productive Twitter users are not the most influential, that the pro-Brexit camp was four times more influential, and had considerably larger impact on the campaign than the opponents. Third, we find that the top pro-Brexit communities are considerably more polarized than the contra-Brexit camp. These results show that social media provide a rich resource of data to be exploited, but accumulated knowledge and lessons learned from the opinion polls have to be adapted to the new data sources.
Identifying public misinformation is a complicated and challenging task. Stance detection, i.e. determining the relative perspective a news source takes towards a specific claim, is an important part of evaluating the veracity of the assertion. Automating the process of stance detection would arguably benefit human fact checkers. In this paper, we present our stance detection model which claimed third place in the first stage of the Fake News Challenge. Despite our straightforward approach, our model performs at a competitive level with the complex ensembles of the top two winning teams. We therefore propose our model as the 'simple but tough-to-beat baseline' for the Fake News Challenge stance detection task.
Measuring and forecasting opinion trends from real-time social media is a long-standing goal of big-data analytics. Despite its importance, there has been no conclusive scientific evidence so far that social media activity can capture the opinion of the general population. Here we develop a method to infer the opinion of Twitter users regarding the candidates of the 2016 US Presidential Election by using a combination of statistical physics of complex networks and machine learning based on hashtags co-occurrence to develop an in-domain training set approaching 1 million tweets. We investigate the social networks formed by the interactions among millions of Twitter users and infer the support of each user to the presidential candidates. The resulting Twitter trends follow the New York Times National Polling Average, which represents an aggregate of hundreds of independent traditional polls, with remarkable accuracy. Moreover, the Twitter opinion trend precedes the aggregated NYT polls by 10 days, showing that Twitter can be an early signal of global opinion trends. Our analytics unleash the power of Twitter to uncover social trends from elections, brands to political movements, and at a fraction of the cost of national polls.
Sentiment analysis assigns a positive, negative or neutral polarity to an item or entity, extracting and aggregating individual opinions from their textual expressions by means of natural language processing tools. In this paper we observe that current sentiment analysis techniques are satisfactory in case there is a single entity under consideration, but can lead to inaccurate or wrong results when dealing with a set of multiple items. We argue in favor of importing techniques from voting theory and preference aggregation to provide a more accurate definition of the collective sentiment over a set of multiple items. We propose a notion of Borda count which combines individuals’ sentiment with comparative preference information, we show that this class of rules satisfies a number of properties which have a natural interpretation in the sentiment analysis domain, and we evaluate its behavior when faced with highly incomplete domains.
The consequences of anthropogenic climate change are extensively debated
through scientific papers, newspaper articles, and blogs. Newspaper articles
may lack accuracy, while the severity of findings in scientific papers may be
too opaque for the public to understand. Social media, however, is a forum
where individuals of diverse backgrounds can share their thoughts and opinions.
As consumption shifts from old media to new, Twitter has become a valuable
resource for analyzing current events and headline news. In this research, we
analyze tweets containing the word "climate" collected between September 2008
and July 2014. We determine how collective sentiment varies in response to
climate change news, events, and natural disasters. Words uncovered by our
analysis suggest that responses to climate change news are predominately from
climate change activists rather than climate change deniers, indicating that
Twitter is a valuable resource for the spread of climate change awareness.
Twitter is a microblogging website where users read and write millions of short messages on a variety of topics every day. This study uses the context of the German federal election to investigate whether Twitter is used as a forum for political deliberation and whether online messages on Twitter validly mirror offline political sentiment. Using LIWC text analysis software, we conducted a content-analysis of over 100,000 messages containing a reference to either a political party or a politician. Our results show that Twitter is indeed used extensively for political deliberation. We find that the mere number of messages mentioning a party reflects the election result. Moreover, joint mentions of two parties are in line with real world political ties and coalitions. An analysis of the tweets’ political sentiment demonstrates close correspondence to the parties' and politicians’ political positions indicating that the content of Twitter messages plausibly reflects the offline political landscape. We discuss the use of microblogging message content as a valid indicator of political sentiment and derive suggestions for further research.
Research examining the predictive power of social media (especially Twitter) displays conflicting results, particularly in the domain of political elections. This paper applies methods used in studies that have shown a direct correlation between volume/sentiment of Twitter chatter and future electoral results in a new dataset about political elections. We show that these methods display a series of shortcomings that make them inadequate for determining whether social media messages can predict the outcome of elections.
People turn to social media to express their opinion towards different topics and issues. This makes social media a valuable resource for mining public opinion. Stance detection is an approach to analyzing social media users’ content to determine public opinion. In this paper, we present a replicable methodology for coding tweets for stance detection towards the COVID-19 vaccination. The methodology includes a codebook for coding the stance towards COVID-19 vaccination and 2 approaches to sampling Twitter data for manual coding: keywords and hashtags. The codebook provides a template for other researchers to code social media data for stance towards vaccination. Our analysis of the results from 2 sampling approaches shows that sampling with hashtags leads to high inter-coder agreement. We analyze the stance and compare it with the results from sentiment analysis on the same dataset to highlight the distinction of our methodology for stance analysis when compared to sentiment analysis towards vaccination. The major contributions of this paper are: a replicable methodology for annotating stance towards COVID-19 vaccination with a codebook and dataset, and a comparison of sampling Twitter data with keywords and hashtags on the inter-coder agreement along with the resulting distribution of stance.KeywordsCOVID-19VaccinationPublic opinionStance
On April 13, 2021, U.S. authorities announced an investigation into potential adverse events associated with the Johnson & Johnson (Janssen, J&J) COVID-19 vaccine and recommended “a pause in the use of this vaccine out of an abundance of caution.” We examined whether public attitudes toward COVID-19 vaccination shifted after this recommended suspension using an interrupted time series with data from the Census Bureau’s Household Pulse Survey, which was fielded bi-weekly between January 6 and April 26, 2021. We found no significant changes in trends of the proportion of the U.S. adult population hesitant about getting a COVID-19 vaccine, but a significant increase in concerns about safety and efficacy of COVID-19 vaccines among the already hesitant population.
A central question in political representation is whether government responds to the people. To understand that, we need to know what the government is doing, and what the people think of it. We seek to understand a key question necessary to answer those bigger questions: How does American public opinion move over time? We posit three patterns of change over time in public opinion, depending on the type of issue. Issues on which the two parties regularly disagree provide clear partisan cues to the public. For these party-cue issues we present a slight variation on the thermostatic theory from (Soroka and Wlezien (2010); Wlezien (1995)); our “implied thermostatic model.” A smaller number of issues divide the public along lines unrelated to partisanship, and so partisan control of government provides no relevant clue. Finally, we note a small but important class of issues which capture response to cultural shifts.
Stance detection on social media is an emerging opinion mining paradigm for various social and political applications in which sentiment analysis may be sub-optimal. There has been a growing research interest for developing effective methods for stance detection methods varying among multiple communities including natural language processing, web science, and social computing, where each modeled stance detection in different ways. In this paper, we survey the work on stance detection across those communities and present an exhaustive review of stance detection techniques on social media, including the task definition, different types of targets in stance detection, features set used, and various machine learning approaches applied. Our survey reports state-of-the-art results on the existing benchmark datasets on stance detection, and discusses the most effective approaches. In addition, we explore the emerging trends and different applications of stance detection on social media, including opinion mining and prediction and recently using it for fake news detection. The study concludes by discussing the gaps in the current existing research and highlights the possible future directions for stance detection on social media.
People's opinion around social and political issues is currently witnessed by messages ordinarily posted on social media. With reference to a specific case study, namely the vaccination topic in Italy, this work discusses a crucial aspect in structuring the data processing pipeline in intelligent systems aimed at monitoring the public opinion through Twitter messages: a plain analysis of tweet contents is not sufficient to grasp the diversity of behavior across users. To get a sharper picture of the public opinion as expressed on social media, user-related information must be incorporated in the analysis. Relying on a dataset of tweets about vaccination and on an established text classification system, we present the results of a stance monitoring campaign with advanced analysis on temporal and spatial scales. The overall methodological workflow provides a sound solution for public opinion assessment from Twitter data.
Automatic elicitation of semantic information from natural language texts is an important research problem with many practical application areas. Especially after the recent proliferation of online content through channels such as social media sites, news portals, and forums; solutions to problems such as sentiment analysis, sarcasm/controversy/veracity/rumour/fake news detection, and argument mining gained increasing impact and significance, revealed with large volumes of related scientific publications. In this article, we tackle an important problem from the same family and present a survey of stance detection in social media posts and (online) regular texts. Although stance detection is defined in different ways in different application settings, the most common definition is “automatic classification of the stance of the producer of a piece of text, towards a target, into one of these three classes: {Favor, Against, Neither}.” Our survey includes definitions of related problems and concepts, classifications of the proposed approaches so far, descriptions of the relevant datasets and tools, and related outstanding issues. Stance detection is a recent natural language processing topic with diverse application areas, and our survey article on this newly emerging topic will act as a significant resource for interested researchers and practitioners.
To what extent user's stance towards a given topic could be inferred? Most of the studies on stance detection have focused on analysing user's posts on a given topic to predict the stance. However, the stance in social media can be inferred from a mixture of signals that might reflect user's beliefs including posts and online interactions. This paper examines various online features of users to detect their stance towards different topics. We compare multiple set of features, including on-topic content, network interactions, user's preferences, and online network connections. Our objective is to understand the online signals that can reveal the users' stance. Experimentation is applied on tweets dataset from the SemEval stance detection task, which covers five topics. Results show that stance of a user can be detected with multiple signals of user's online activity, including their posts on the topic, the network they interact with or follow, the websites they visit, and the content they like. The performance of the stance modelling using different network features are comparable with the state-of-the-art reported model that used textual content only. In addition, combining network and content features leads to the highest reported performance to date on the SemEval dataset with F-measure of 72.49%. We further present an extensive analysis to show how these different set of features can reveal stance. Our findings have distinct privacy implications, where they highlight that stance is strongly embedded in user's online social network that, in principle, individuals can be profiled from their interactions and connections even when they do not post about the topic.
Despite vaccination's role in preventing communicable diseases, misinformation threatens uptake. Social media may disseminate such anti-vaccination messages. We characterized trends in pro- and anti-vaccination discourse on Twitter. All tweets between 2010 and 2019 containing vaccine-related hashtags were identified. Pro- and anti-vaccine tweets and users per quarter (3-months) were tabulated; discussion subcommunities were identified with network analysis. 1,637,712 vaccine-related tweets were identified from 154 pro-vaccine and 125 anti-vaccine hashtags, with 86% of users posting exclusively pro-vaccine and 12% posting exclusively anti-vaccine hashtags. Pro-vaccine tweet volumes are larger than anti-vaccine tweets and consistently increase over time. In contrast, anti-vaccine tweet volumes have decreased since 2014, despite an increasing anti-vaccine user-base. Users infrequently responded across pro/anti-vaccine alignment (0.2%). Despite greater volumes of pro-vaccination discourse in recent years, and the anti-vaccination content userbase being smaller, the anti-vaccine community continues to grow in size. This finding coupled with the minimal inter-communication between communities suggests possible ideological isolation.
Textual information in the world can be broadly categorized into two main types: facts and opinions. Facts are objective expressions about entities, events, and their properties. Opinions are usually subjective expressions that describe people’s sentiments, appraisals, or feelings toward entities, events, and their properties. The concept of opinion is very broad. In this chapter, we only focus on opinion expressions that convey people’s positive or negative sentiments. Much of the existing research on textual information processing has been focused on themining and retrieval of factual information, e.g., information retrieval (IR), Web search, text classification, text clustering, and many other text mining and natural language processing tasks. Littleworkhadbeendone on the processing of opinions until only recently. Yet, opinions are so important that whenever we need to make a decision we want to hear others’ opinions. This is not only true for individuals but also true for organizations.
We use 23M Tweets related to the EU referendum in the UK to predict the Brexit vote. In particular, we use user-generated labels known as hashtags to build training sets related to the Leave/Remain campaign. Next, we train SVMs in order to classify Tweets. Finally, we compare our results to Internet and telephone polls. This approach not only allows to reduce the time of hand-coding data to create a training set, but also achieves high level of correlations with Internet polls. Our results suggest that Twitter data may be a suitable substitute for Internet polls and may be a useful complement for telephone polls. We also discuss the reach and limitations of this method.
Solicited public opinion surveys reach a limited subpopulation of willing participants and are expensive to conduct, leading to poor time resolution and a restricted pool of expert-chosen survey topics. In this study, we demonstrate that unsolicited public opinion polling through sentiment analysis applied to Twitter correlates well with a range of traditional measures, and has predictive power for issues of global importance. We also examine Twitter's potential to canvas topics seldom surveyed, including ideas, personal feelings, and perceptions of commercial enterprises. Two of our major observations are that appropriately filtered Twitter sentiment (1) predicts President Obama's job approval three months in advance, and (2) correlates well with surveyed consumer sentiment. To make possible a full examination of our work and to enable others' research, we make public over 10,000 data sets, each a seven-year series of daily word counts for tweets containing a frequently used search term.
This is a nonparametric test procedure for the analysis of matched-pair data, based on differences, or for a single sample. The null hypothesis is that the differences, or individual observations in the single-sample case, have a distribution centered about zero. The absolute values are ranked. The test statistic is the sum of the ranks for either the positive or the negative values. Examples and details, including large-sample properties, are given.
Keywords:
nonparametric;
distribution-free;
ranks;
absolute differences;
large samples;
ARE;
censored
Tracking trends in American public opinion, this study examines moods of public policy over time. James Stimson looks at shorter term movements as the public approves or disapproves politicians, trusts or distrusts government. His book is distinctive in that it focuses on determining the unobserved true opinion that lies beneath superficial polls. It argues that public opinion is decisive in American politics and identifies the citizens who produce influential change as a relatively small subset of the American electorate.
We propose a method for collective sentiment analysis for stock market prediction and analyse its ability to predict the change of a stock price for the next day. The proposed method is a two-stage process, based on the latest natural language processing and machine learning algorithms. Our evaluation shows best performance with the SVM approach in sentiment detection, with accuracy rates of 71.84/74.3% for positive and negative sentiment, respectively. The results of sentiment analysis are used in predicting stock price movement (up or down), and we found that users' activity on Stock Twits overnight positively correlates with stock trading on the next business day. The collective sentiments in after hours have powerful prediction on the change of stock price for the next day in 9 out of 15 stocks studied by using the Granger Causality test.
Co-creation communities allow companies to utilize consumers’ creative thinking in the innovation process. This paper seeks to understand the role of sentiment in user co-creation. The results suggest that management style can affect the success of co-creation communities. Specific employees’ communication styles, the sentiments embedded in the messages, task-oriented content, and proactiveness can all influence individual user sentiment. The aggregation of these individual user sentiments, resulting in collective sentiments, affects co-creation performance. Increasing negative collective sentiment results in decreased subsequent creativity and increased future participation. Conversely, growing positive collective sentiment leads to a lower level of participation.
More and more people express their opinions on social media such as Facebook and Twitter. Predictive analysis on social media time-series allows the stake-holders to leverage this immediate, accessible and vast reachable communication channel to react and proact against the public opinion. In particular, understanding and predicting the sentiment change of the public opinions will allow business and government agencies to react against negative sentiment and design strategies such as dispelling rumors and post balanced messages to revert the public opinion. In this paper, we present a strategy of building statistical models from the social media dynamics to predict collective sentiment dynamics. We model the collective sentiment change without delving into micro analysis of individual tweets or users and their corresponding low level network structures. Experiments on large-scale Twitter data show that the model can achieve above 85% accuracy on directional sentiment prediction.
Effect size helps readers understand the magnitude of differences found, whereas statistical significance examines whether the findings are likely to be due to chance. Both are essential for readers to understand the full impact of your work. Report both in the Abstract and Results sections.
The test is based on the maximum difference between an empirical and a hypothetical cumulative distribution. Percentage points are tabled, and a lower bound to the power function is charted. Confidence limits for a cumulative distribution are described. Examples are given. Indications that the test is superior to the chi-square test are cited.
This monumental study is a comprehensive critical survey of the policy preferences of the American public, and will be the definitive work on American public opinion for some time to come. Drawing on an enormous body of public opinion data, Benjamin I. Page and Robert Y. Shapiro provide the richest available portrait of the political views of Americans, from the 1930's to 1990. They not only cover all types of domestic and foreign policy issues, but also consider how opinions vary by age, gender, race, region, and the like. The authors unequivocally demonstrate that, notwithstanding fluctuations in the opinions of individuals, collective public opinion is remarkably coherent: it reflects a stable system of values shared by the majority of Americans and it responds sensitively to new events, arguments, and information reported in the mass media. While documenting some alarming case of manipulation, Page and Shapiro solidly establish the soundness and value of collective political opinion. The Rational Public provides a wealth of information about what we as a nation have wanted from government, how we have changed our minds over the years, and why. For anyone interested in the short- and long-term trends in Americans' policy preferences, or eager to learn what Americans have thought about issues ranging from racial equality to the MX missile, welfare to abortion, this book offers by far the most sophisticated and detailed treatment available.
“Where all people talk on the same subject, they should be agreed about the vocabulary with which they discuss it: or, at any rate, they should be aware that they are not agreed.”