DOCSLIB.ORG

Ericka Menchen-Trevino

Total Page：16

File Type：pdf, Size：1020Kb

Download full-text PDF Read full-text

Load more

Ericka Menchen-Trevino 7/24/2020 c COPYRIGHT by Kurt Wirth July 23, 2020 ALL RIGHTS RESERVED PREDICTING CHANGES IN PUBLIC OPINION WITH TWITTER: WHAT SOCIAL MEDIA DATA CAN AND CAN'T TELL US ABOUT OPINION FORMATION by Kurt Wirth ABSTRACT With the advent of social media data, some researchers have claimed they have the potential to revolutionize the measurement of public opinion. Others have pointed to non-generalizable methods and other concerns to suggest that the role of social media data in the field is limited. Likewise, researchers remain split as to whether automated social media accounts, or bots, have the ability to influence con- versations larger than those with their direct audiences. This dissertation examines the relationship between public opinion as measured by random sample surveys, Twitter sentiment, and Twitter bot activity. Analyzing Twitter data on two topics, the president and the economy, as well as daily public polling data, this dissertation offers evidence that changes in Twitter sentiment of the president predict changes in public approval of the president fourteen days later. Likewise, it shows that changes in Twit- ter bot sentiment of both the president and the economy predict changes in overall Twitter sentiment on those topics between one and two days later. The methods also reveal a previously undiscovered phenomenon by which Twitter sentiment on a topic moves counter to polling approval of the topic at a seven-day interval. This dissertation also discusses the theoretical implications of various methods of calculating social media sentiment. Most importantly, its methods were pre-registered so as to maximize the generalizability of its findings and avoid data cherry-picking or overfitting. ii ACKNOWLEDGEMENTS This dissertation is the culmination of years of mentorship by experts in their respective fields. Most importantly, I could not have reached this lifelong goal if not for good people - people who are generous with their time, attention, and advice. No journey is easy and while mine hit its obstacles, some were there to once again stand me on my feet. I'd first like to acknowledge Dr. Ericka Menchen-Trevino for her unsurpassed patience and gen- erosity. Her willingness to support me and provide advice throughout the writing of this dissertation, my time at American University, and in the job market afterward proves her capability as a professional and, even further, her quality as a human being. I will forever be grateful for Ericka's friendship, hard-learned lessons, and dedication. Likewise, Dr. W. Joseph Campbell spent a year truly investing in my work and encouraging me to push myself further. His willingness to treat me as a colleague helped me to believe in my research skills more strongly and, as a result, develop them further. Joe is generous, knowledgeable, and courteous and I consider myself lucky to have had him as a mentor. To round out my committee, Dr. Ryan T. Moore has again and again dropped everything to build my programming and analysis skills. If it weren't for his genius and unshakable calm, this dissertation and my skill set would be a far cry from where they are. Likewise, Dr. David Karpf was among the first of my mentors to take a vested interest in my development by giving a random student his time at a local coffee shop. Not only have I grown as a researcher from his advice, I've grown as a person. I'd also like to thank Beth Chunn of Rasmussen for so generously providing poll data for academic use. Her support of academic research should be a standard for all data collectors in the future. Ap- preciation to my American University cohort for their unfaltering emotional support, my parents and parents-in-law for their love, and the American University department staff for their quick and passionate work. Lastly and most importantly, thanks and love to my husband Daniel Wild for his toleration and patience through four strenuous years of my graduate school education. iii TABLE OF CONTENTS ABSTRACT . ii ACKNOWLEDGEMENTS . iii LIST OF TABLES . vii LIST OF FIGURES . viii CHAPTER 1. INTRODUCTION . .1 What Is Public Opinion and How Do We Measure It? . .5 The Origins of the Poll . .6 Random Sample Surveys in the Twentieth Century . .7 An Un-Narrowing Of Public Opinion Methods? . .8 Social Media as a Public Opinion Data Source . .9 The Strengths of Social Media Data . .9 The Challenges Of Using Social Media Data . 10 Predicting Elections, Mirroring, and Predicting Public Polls . 10 Inauthentic Communication and Public Opinion . 12 Summary . 13 2. LITERATURE REVIEW . 15 Random Sample Surveys As The Public Opinion Gold Standard . 15 Found Data vs. Created Data . 16 Topic Coverage vs. Population Coverage . 17 Social Media as Public Opinion Data . 18 Predicting Elections . 19 iv v Mirroring Public Polling . 22 Predicting Changes in Public Polling . 23 Power To The People? . 24 Bot Manipulation . 25 Methodology In Question . 27 Topics . 28 Measuring Twitter Sentiment . 28 Research Questions . 30 3. METHODS . 33 Data Collection . 33 Analysis . 37 Conversation-Level Measurement . 37 Individual-Level Measurement . 39 Measuring Bot Influence . 40 Final Results . 41 4. RESULTS AND DISCUSSION . 45 Measuring the Relationship Between Twitter Sentiment and Polling Data . 45 Per-Topic Results . 48 Effect Direction Over Time . 50 Time Lags . 51 Overview . 53 Measuring the Relationship Between Twitter Sentiment and Bot Sentiment . 54 Sentiment Measurement Techniques Compared . 56 Summary . 60 5. CONCLUSION . 61 Predicting Polls . 62 Bot Influence . 63 The Three Step Flow . 64 Future Research . 65 vi Contributions . 67 APPENDIX A. RQ1 RESULTS VIA INDIVIDUAL-BASED SENTIMENT MEASUREMENT . 78 B. RQ1 ROBUSTNESS CHECK . 79 C. RQ2 RESULTS VIA INDIVIDUAL-BASED SENTIMENT MEASUREMENT . 80 D. ABSOLUTE VALUES VS. CHANGE . 81 LIST OF TABLES Table Page 1. Corpus-based sentiment vs. poll regression coefficient estimate across lag times (standard error in parentheses) . 46 2. Verified user sentiment vs. poll regression coefficient estimate across lag times (standard error in parentheses) . 48 3. Corpus-based sentiment vs. bots regression coefficient estimate across lag times between bot sentiment and Twitter sentiment (standard error in parentheses) . 55 4. Residual standard error by sentiment calculation method, topic, and research question, with prediction improvement when calculating sentiment by corpus rather than individual 59 5. Residual standard error improvement when using corpus-based vs. individual-based cal- culation . 60 6. RQ1 results with individual data (standard error in parentheses) . 78 7. RQ1 results with individual but no verified data (standard error in parentheses) . 78 8. Individual-based sentiment vs. bots regression coefficient estimate across lag times between bot sentiment and Twitter sentiment (standard error in parentheses) . 80 9. Individual-based sentiment without verified users vs. bots regression coefficient estimate across lag times between bot sentiment and Twitter sentiment (standard error in parentheses) 80 vii LIST OF FIGURES Figure Page 1. Rune Karlsen's updated two-step flow featuring opinion leaders (OL) and passive individ- uals (P) (Karlsen, 2015). 30 2. Two-step flow including conversation among opinion leaders. 31 3. Data collection timeline . 34 4. Conversation-based measurement process . 38 5. Individual-based measurement process . 40 6. Economic confidence as measured by polls and economic sentiment as measured by Twitter sentiment over time. 47 7. Presidential approval as measured by polls and Twitter sentiment over time. 47 8. Three-day rolling averages with zero lag of poll data and Twitter sentiment over time for presidential approval. 52 9. Three-day rolling averages with zero lag of poll data and Twitter sentiment over time for economic confidence. 53 10. Three-day rolling averages of presidential polling changes and predicted changes in presi- dential polling approval using fourteen-day and seven-day lagged Twitter sentiment changes. 53 11. Three-day rolling averages with zero lag of overall Twitter sentiment change and Twitter bot sentiment change over time for the presidential approval topic. 56 12. Three-day rolling averages with zero lag of overall Twitter sentiment change and Twitter bot sentiment change over time for the economic confidence topic. 56 13. Three-day rolling averages of Twitter sentiment and predicted change in Twitter sentiment when using only two-day lagged bot sentiment as a predictor. 57 14. Three-day rolling averages of overall Twitter sentiment change and Twitter bot sentiment change over time. 57 15. Absolute presidential approval (left Y axis) compared to presidential approval change (right Y axis). ..
Recommended publications
  • 13 Collecting Statistical Data

    13 Collecting Statistical Data

    13 Collecting Statistical Data 13.1 The Population 13.2 Sampling 13.3 Random Sampling 1.1 - 1 • Polls, studies, surveys and other data collecting tools collect data from a small part of a larger group so that we can learn something about the larger group. • This is a common and important goal of statistics: Learn about a large group by examining data from some of its members. 1.1 - 2 Data collections of observations (such as measurements, genders, survey responses) 1.1 - 3 Statistics is the science of planning studies and experiments, obtaining data, and then organizing, summarizing, presenting, analyzing, interpreting, and drawing conclusions based on the data 1.1 - 4 Population the complete collection of all individuals (scores, people, measurements, and so on) to be studied; the collection is complete in the sense that it includes all of the individuals to be studied 1.1 - 5 Census Collection of data from every member of a population Sample Subcollection of members selected from a population 1.1 - 6 A Survey • The practical alternative to a census is to collect data only from some members of the population and use that data to draw conclusions and make inferences about the entire population. • Statisticians call this approach a survey (or a poll when the data collection is done by asking questions). • The subgroup chosen to provide the data is called the sample, and the act of selecting a sample is called sampling. 1.1 - 7 A Survey • The first important step in a survey is to distinguish the population for which the survey applies (the target population) and the actual subset of the population from which the sample will be drawn, called the sampling frame.
  • Preface Chapter 1

    Preface Chapter 1

    Notes Preface 1. Alfred Pearce Dennis, “Humanizing the Department of Commerce,” Saturday Evening Post, June 6, 1925, 8. 2. Herbert Hoover, Memoirs: The Cabinet and the Presidency, 1920–1930 (New York: Macmillan, 1952), 184. 3. Herbert Hoover, “The Larger Purposes of the Department of Commerce,” in “Republi- can National Committee, Brief Review of Activities and Policies of the Federal Executive Departments,” Bulletin No. 6, 1928, Herbert Hoover Papers, Campaign and Transition Period, Box 6, “Subject: Republican National Committee,” Hoover Presidential Library, West Branch, Iowa. 4. Herbert Hoover, “Responsibility of America for World Peace,” address before national con- vention of National League of Women Voters, Des Moines, Iowa, April 11, 1923, Bible no. 303, Hoover Presidential Library. 5. Bruce Bliven, “Hoover—And the Rest,” Independent, May 29, 1920, 275. Chapter 1 1. John W. Hallowell to Arthur (Hallowell?), November 21, 1918, Hoover Papers, Pre-Com- merce Period, Hoover Presidential Library, West Branch, Iowa, Box 6, “Hallowell, John W., 1917–1920”; Julius Barnes to Gertrude Barnes, November 27 and December 5, 1918, ibid., Box 2, “Barnes, Julius H., Nov. 27, 1918–Jan. 17, 1919”; Lewis Strauss, “Further Notes for Mr. Irwin,” ca. February 1928, Subject File, Lewis L. Strauss Papers, Hoover Presidential Library, West Branch, Iowa, Box 10, “Campaign of 1928: Campaign Literature, Speeches, etc., Press Releases, Speeches, etc., 1928 Feb.–Nov.”; Strauss, handwritten notes, December 1, 1918, ibid., Box 76, “Strauss, Lewis L., Diaries, 1917–19.” 2. The men who sailed with Hoover to Europe on the Olympic on November 18, 1918, were Julius Barnes, Frederick Chatfi eld, John Hallowell, Lewis Strauss, Robert Taft, and Alonzo Taylor.
  • George Gallup: Highlights of His Life and Work

    George Gallup: Highlights of His Life and Work

    George Gallup: Highlights of His Life and Work PRE-BIOGRAPHY AND PRE-HISTORY Understanding the character of creative people and the significance of their achievements requires, in the first place, a study of their pre-biography, together with the pre-history of that branch of science, art or culture where their endeavors have taken place. In other words, the person’s biography is a product of both the genealogy and the environment in which his or her professional activity has taken place. The life and work of George Gallup in this sense are exemplary. In the first place, he belonged to a large family, whose members had vigorously participated in the development of the United States, and whose accomplishments and merits are recorded in the annals of the country. Secondly, even though the modern stage of public opinion research began with the pioneering work of George Gallup in 1935 and 1936, the study of electoral attitudes in the US had a long history prior to that. Accordingly, after examining the pre-biography of George Gallup, we will also review the pre-history of public opinion research. Tenth-Generation American For generations, the large Kollop family resided in Lotharingia (Lorraine). During the Middle Ages some of its descendants moved over to England, retaining the Gollop name. It is believed to have been forged from the German words Gott and Lobe, meaning respectively “God” and “praise”. Over time various spellings of the family name emerged: Gallop, Galloup, Galloupe, Gallupe, and Gollop, with the version prevalent in America becoming Gal- lup. A historical record has been preserved in England concerning John Gollop (born about 1440), who came ‘out of the North in the fifth year of the reign of Edward IV’ (1465).
  • Has Polling Enhanced Representation? Unearthing Evidence from the Literary Digest Issue Polls

    Has Polling Enhanced Representation? Unearthing Evidence from the Literary Digest Issue Polls

    Studies in American Political Development, 21 (Spring 2007), 16–29. Has Polling Enhanced Representation? Unearthing Evidence from the Literary Digest Issue Polls David Karol, University of California, Berkeley How has representation changed over time in the Institutional reforms are not, however, the only United States? Has responsiveness to public opinion factors that can affect representation; technological waxed or waned among elected officials? What are change can also play a significant role. In fact, some the causes of such trends as we observe? Scholars scholars contend that the rise of scientific surveys have pursued these crucial questions in different since the 1930s has yielded more responsive govern- ways. Some explore earlier eras in search of the “elec- ment. According to this school of thought, polls toral connection”, i.e. the extent to which voters held provide recent cohorts of elected officials more accu- office-holders accountable for their actions and the rate assessments of public opinion than their prede- degree to which electoral concerns motivated poli- cessors enjoyed, which allows them to reflect their ticians’ behavior.1 Others explore the effects of insti- constituents’ views to a greater extent than the tutional changes such as the move to direct election politicians of yesteryear. Yet others doubt whether of senators or the “reapportionment revolution.”2 politicians were truly ignorant of public sentiment before the rise of the poll; nor is there much certainty regarding the level of current politicians’ understand- I thank Larry Bartels, Terri Bimes, Ben Bishin, Ben Fordham, ing of constituent opinion. Some also question John Geer, Brian Glenn, Susan Herbst, Mark Kayser, Brian Lawson, whether ignorance is at the root of elected officials’ Taeku Lee, Eileen McDonagh and Eric Plutzer for comments along frequent divergence from their constituents’ wishes.
  • The Signal and the Noise: Why So Many Predictions Fail--But Some Dont Pdf, Epub, Ebook

    The Signal and the Noise: Why So Many Predictions Fail--But Some Dont Pdf, Epub, Ebook

    THE SIGNAL AND THE NOISE: WHY SO MANY PREDICTIONS FAIL--BUT SOME DONT PDF, EPUB, EBOOK Nate Silver | 560 pages | 03 Feb 2015 | Penguin Books | 9780143125082 | English | United States The Signal and the Noise: Why So Many Predictions Fail—But Some Don't by Nate Silver Please note that the tricks or techniques listed in this pdf are either fictional or claimed to work by its creator. We do not guarantee that these techniques will work for you. Some of the techniques listed in The Signal and the Noise: Why So Many Predictions Fail - But Some Dont may require a sound knowledge of Hypnosis, users are advised to either leave those sections or must have a basic understanding of the subject before practicing them. DMCA and Copyright : The book is not hosted on our servers, to remove the file please contact the source url. If you see a Google Drive link instead of source url, means that the file witch you will get after approval is just a summary of original book or the file has been already removed. Loved each and every part of this book. I will definitely recommend this book to non fiction, science lovers. Your Rating:. Your Comment:. As a devotee of , website and especially podcasts, I was looking forward to this book. I wanted to love it. It was enjoyable, certainly, and I learned a few things. But after opening the door, it Nate Silver. He solidified his standing as the nation's foremost political forecaster with his near perfect prediction of the election. Drawing on his own groundbreaking work, Silver examines the world of prediction, investigating how we can distinguish a true signal from a universe of noisy data.
  • AP Statistics

    AP Statistics

    AP Statistics 1 SAMPLING C H A P 11 The idea that the examination of a relatively small number of randomly selected individuals can furnish dependable information about the characteristics of a vast unseen universe is an idea so powerful that only familiarity makes it cease to be exciting. Helen Mary Walker (1891 - 1983) What is Sampling? 2 We want to know something about a population (a mean or proportion, for example), but it isn’t possible to go out and obtain the information from every member of the population (called a census). So we take a “random sample” from the population, obtain information about the sample, and then use the sample information to estimate what we would get if we could reach the entire population. What is Sampling? 3 The information we want to know about the population is called a parameter. The information we get from the sample is called a statistic. We use the statistic to provide an estimate of the parameter. Sample Population Notation 4 Name Statistic Parameter Meany (mu) Standard Deviations (sigma) Correlationr (rho) Regression coefficientb (beta) Proportionppˆ (pi -- but we will use ) Surveys 5 A survey is a common way to get information from a population of people. Follow your favorite news source and you will hear nearly every day the phrase “In a recent survey …” A survey generally consists of questions asked of a selected group (the sample) in order to obtain an estimate of the opinions of the population (U.S. voters, for example). Sampling Variability 6 Different samples will produce different results.
  • Psephological Fallacies of Public Opinion Polling

    Psephological Fallacies of Public Opinion Polling

    SPECIAL ARTICLE Psephological Fallacies of Public Opinion Polling Praveen Rai Opinion polls in India capture electoral snapshots in time he terms “survey” and “opinion poll” in India would that divulge information on political participation, have remained a professional jargon of market research industry, had it not been used for predicting election ideological orientation of voters and belief in core T outcomes. The green shoots of opinion polls to study Indian democratic values. The survey data provides for crucial national elections emerged in the 1950s, but it caught the ima- social science insights, validation of theoretical research gination of the people and became clichéd in the closing dec- and academic knowledge production. Although the ade of the 20th century. The popularity of election surveys stems from the political socialisation and crystal ball gazing media’s obsession with political forecasting has shifted curiosity of Indians to foresee the outcomes of hustings before to electoral prophecy, psephology continues to provide the pronouncement of formal results. The electoral inquisitive- the best telescopic view of elections based on the ness of the stakeholders created a large canvas of opportunity feedback of citizens. The ascertainment of subaltern for opinion-polling industry and scope for scientifi c forecasting of Indian election competitions. The proliferation of electronic opinion by surveys not only broadens the contours of media and the rapid monetisation in the 1990s provided mo- understanding electoral democracy, but also provides mentum to polling agencies to venture into opinion polling on an empirical alternative to the elitist viewpoint of national electoral politics and state election contests. The opin- competitive politics in India.
  • Forecasting Elections: Voter Intentions Versus Expectations*

    Forecasting Elections: Voter Intentions Versus Expectations*

    Forecasting Elections: Voter Intentions versus Expectations* David Rothschild Justin Wolfers Microsoft Research Dept of Economics and Ford School of Public Policy, and Applied Statistics Center, Columbia University of Michigan Brookings, CEPR, CESifo, IZA and NBER [email protected] [email protected] www.ResearchDMR.com www.nber.org/~jwolfers Abstract Most pollsters base their election projections off questions of voter intentions, which ask “If the election were held today, who would you vote for?” By contrast, we probe the value of questions probing voters’ expectations, which typically ask: “Regardless of who you plan to vote for, who do you think will win the upcoming election?” We demonstrate that polls of voter expectations consistently yield more accurate forecasts than polls of voter intentions. A small-scale structural model reveals that this is because we are polling from a broader information set, and voters respond as if they had polled twenty of their friends. This model also provides a rational interpretation for why respondents’ forecasts are correlated with their expectations. We also show that we can use expectations polls to extract accurate election forecasts even from extremely skewed samples. This draft: November 1, 2012 Keywords: Polling, information aggregation, belief heterogeneity JEL codes: C53, D03, D8 * The authors would like to thank Stephen Coate, Alex Gelber, Andrew Gelman, Sunshine Hillygus, Mat McCubbins, Marc Meredith and Frank Newport, for useful discussions, and seminar audiences at AAPOR, Berkeley, Brookings, CalTech, Columbia, Cornell, the Conference on Empirical Legal Studies, the Congressional Budget Office, the Council of Economic Advisers, Harvard, Johns Hopkins, Maryland, Michigan, MIT, the NBER Summer Institute, University of Pennsylvania’s political science department, Princeton, UCLA, UCSD, USC, and Wharton for comments.
  • UC Santa Barbara UC Santa Barbara Electronic Theses and Dissertations

    UC Santa Barbara UC Santa Barbara Electronic Theses and Dissertations

    UC Santa Barbara UC Santa Barbara Electronic Theses and Dissertations Title Campaign spheres in Latin America: How institutions affect digital media in presidential elections Permalink https://escholarship.org/uc/item/0026d2q5 Author Brandao, Francisco Publication Date 2017 Peer reviewed|Thesis/dissertation eScholarship.org Powered by the California Digital Library University of California UNIVERSITY OF CALIFORNIA Santa Barbara Campaign spheres in Latin America: How institutions affect digital media in presidential elections A dissertation submitted in partial satisfaction of the requirements for the degree Doctor of Philosophy in Political Science by Francisco de Assis Fernandes Brandão Junior Committee in charge: Professor Bruce Bimber, Chair Professor Kathleen Bruhn Professor Matto Mildenberger Professor Michael Stohl January 2018 The dissertation of Francisco Brandão Jr. is approved. ___________________________________________ Kathleen Bruhn ___________________________________________ Matto Mildenberger ___________________________________________ Michael Stohl ___________________________________________ Bruce Bimber, Committee Chair December 2017 Campaign spheres in Latin America: How institutions affect digital media in presidential elections Copyright © 2018 by Francisco de Assis Fernandes Brandão Junior iii Dedication In memory of the students who lost their lives in Isla Vista, on May 23 2014, praying that God brings peace and heals their families, friends, this great community and country. Let there be light! To my mother, who always wondered what would be her life if she hadn`t dropped Graduate School to raise her children. To my wife and daughter, who were together with me in this journey. iv Acknowledgements Some might think it is a truism when graduate students write on their dissertations how much they owe to their graduate advisors for the satisfaction of getting the job done.
  • Citizen Forecasts of the 2008 U.S. Presidential Election

    Citizen Forecasts of the 2008 U.S. Presidential Election

    bs_bs_banner Citizen Forecasts of the 2008 U.S. Presidential Election MICHAEL K. MILLER Australian National University GUANCHUN WANG Lightspeed China Ventures SANJEEV R. KULKARNI Princeton University H. VINCENT POOR Princeton University DANIEL N. OSHERSON Princeton University We analyze individual probabilistic predictions of state outcomes in the 2008 U.S. presidential election. Employing an original survey of more than 19,000 respondents, we find that partisans gave higher probabilities to their favored candidates, but this bias was reduced by education, numerical sophistication, and the level of Obama support in their home states. In aggregate, we show that individual biases balance out, and the group’s predictions were highly accurate, outperforming both Intrade (a prediction market) and fivethirtyeight.com (a poll-based forecast). The implication is that electoral forecasters can often do better asking individuals who they think will win rather than who they want to win. Keywords: Citizen Forecasts, Individual Election Predictions, 2008 U.S. Presidential Election, Partisan Bias, Voter Information, Voter Preference, Wishful Thinking Bias in Elections. Related Articles: Dewitt, Jeff R., and Richard N. Engstrom. 2011. The Impact of Prolonged Nomination Contests on Presidential Candidate Evaluations and General Election Vote Choice: The Case of 2008.” Politics & Policy 39 (5): 741-759. http://onlinelibrary.wiley.com/doi/10.1111/j.1747-1346.2011.00311.x/abstract Knuckey, Jonathan. 2011. “Racial Resentment and Vote Choice in the 2008 U.S. Presidential Election.” Politics & Policy 39 (4): 559-582. http://onlinelibrary.wiley.com/doi/10.1111/j.1747-1346.2011.00304.x/abstract Politics & Policy, Volume 40, No. 6 (2012): 1019-1052.
  • Download (398Kb)

    Download (398Kb)

    US Presidential Elections: Why a Democrat is now favourite to win in 2020. The results of the US midterm elections are now largely in and they came as a shock to many seasoned forecasters. This wasn’t the kind of shock that occurred in 2016, when the EU referendum tipped to Brexit and the US presidential election to Donald Trump. Nor the type that followed the 2015 and 2017 UK general elections, which produced a widely unexpected Conservative majority and a hung parliament respectively. On those occasions, the polls, pundits and prediction markets got it, for the most part, very wrong, and confidence in political forecasting took a major hit. The shock on this occasion was of a different sort – surprise related to just how right most of the forecasts were. Take the FiveThirtyEight political forecasting methodology, most closely associated with Nate Silver, famed for the success of his 2008 and 2012 US presidential election forecasts. In 2016, even that trusted methodology failed to predict Trump’s narrow triumph in some of the key swing states. This was reflected widely across other forecasting methodologies, too, causing a crisis of confidence in political forecasting. And things only got worse when much academic modelling of the 2017 UK general election was even further off target than it had been in 2015. How did it go so right? So what happened in the 2018 US midterm elections? This time, the FiveThirtyEight “Lite” forecast, based solely on local and national polls weighted by past performance, predicted that the Democrats would pick up a net 38 seats in the House of Representatives.
  • A Rhetorical Study of the Public Speaking of Harold L. Ickes in the 1936 Presidential Campaign

    A Rhetorical Study of the Public Speaking of Harold L. Ickes in the 1936 Presidential Campaign

    Louisiana State University LSU Digital Commons LSU Historical Dissertations and Theses Graduate School 1955 A Rhetorical Study of the Public Speaking of Harold L. Ickes in the 1936 Presidential Campaign. William Scott oblesN Louisiana State University and Agricultural & Mechanical College Follow this and additional works at: https://digitalcommons.lsu.edu/gradschool_disstheses Recommended Citation Nobles, William Scott, "A Rhetorical Study of the Public Speaking of Harold L. Ickes in the 1936 Presidential Campaign." (1955). LSU Historical Dissertations and Theses. 127. https://digitalcommons.lsu.edu/gradschool_disstheses/127 This Dissertation is brought to you for free and open access by the Graduate School at LSU Digital Commons. It has been accepted for inclusion in LSU Historical Dissertations and Theses by an authorized administrator of LSU Digital Commons. For more information, please contact [email protected]. A RHETORICAL STUDY OF THE PUBLIC SPEAKING OF HAROLD L. ICKES IN THE 1936 PRESIDENTIAL CAMPAIGN A Dissertation Submitted to the Graduate Faculty of the Louisiana State University and Agricultural and Mechanloal College in partial fulfillment of the requirements for the degree of Doctor of Philosophy in The Department of Speech by William Scott Nobles A., Southeastern Oklahoma State College, 19^7 M. A . , WeBtern Reserve University, 19^8 August, 1955 ACKNOWLEDGMENT The w r i t e r acknowledges the assistance of all those who h a v e contributed to the preparation of this dissertation. Particularly, he wishes to acknowledge his Indebtedness to his wife, Elaine, for her en­ couragement a n d assistance throughout the research and the preparation of the manuscript; to Professor Waldo W.