An investigation of Newsweek, U.S. News and World Report, and Time's content emphasis in articles focusing on the 1980 presidential primary season was conducted in this study. The content emphasis was broken down into four categories: "horserace," "issues," "candidates' personal qualifications," and "other." The categories of "issues" and "candidates' personal qualifications" were combined to form the "substantive" category for the purpose of learning whether the content emphasis of the articles fell into either the "horserace" or "substantive" category.The unit of analysis for this study was the paragraph and a panel of coders was used to determine paragraph classifications. If a paragraph was classified "horserace" its emphasis was entertainment, portraying the campaign as a contest. If a paragraph was classified "substantive" its emphasis was information, concentrating on the issues of the campaign and the qualifications of the candidates.The researcher totaled the raw scores and the percentages of the categories to learn which type of content emphasis was being practiced by the magazines. To substantiate the level of significance in the differences of the raw scores the chi-square test was employed.Findings of the raw score totals in the four categories indicated that 61 percent of the 327 randomly selected paragraphs were classified as having "horserace" content emphasis, 10 percent were classified "issues," 19.3 percent were classified "candidates' personal qualifications," and 9.5 percent were classified "other." The "issues" and "candidates' personal qualifications" categories were combined and represented 29.3 percent.Chi-square tests showed that there were significantly less "substantive" paragraphs than "horserace" paragraphs overall, and Time magazine's coverage was the closest in balance between the two categories.The time period of this study was January 7, 1980 through June 16, 1980. This study also found that Newsweek had the largest amount of campaign coverage with 52 stories in 24 issues; next was Time with 41 stories in 19 issues; followed by U.S. News and World Report with 24 stories about the primary campaign and candidates in 18 issues.

Thesis (Ph.D.)--Ohio State University, 2005. / Title from first page of PDF file. Document formatted into pages; contains xvi, 163 p. Includes bibliographical references (p. 142-146). Available online via OhioLINK's ETD Center

Thesis (Ph. D.)--Ohio State University, 2004. / Title from first page of PDF file. Document formatted into pages; contains xi, 202 p. Includes bibliographical references (p. 190-202). Available online via OhioLINK's ETD Center

Project (M.S.)--Brigham Young University. Dept. of Statistics, 2006. / Includes bibliographical references (p. 66-67).

Thesis (Ph. D.)--University of Texas at Austin, 2004. / Supervisor: Daron Shaw. Vita. Includes bibliographical references. Also available from UMI.

Thesis (M.A.)--University of Missouri-Columbia, 2004. / Typescript. Includes bibliographical references (leaves 91-97). Also available on the Internet.

Thesis (M.A.)--University of Missouri-Columbia, 2004. / Typescript. Includes bibliographical references (leaves 91-97). Also available on the Internet.

In this dissertation project, I make three separate contributions on how we can improve aggregate election forecasting models with respect to modeling choices, interpretability, and performance. Two of the three papers are applications to particular cases, the U.S. and France specifically, while the third points to a cross-national pattern in polling errors. The first paper addresses how we can make more reasonable prior choices for key parameters – such as the variability of non-sampling error – by using past pre-election polls. I showcase this approach on U.S. presidential elections. The second paper shows how to create and aggregate predictions in a multi-party contest while keeping the individual forecasts intact. This is useful to see convergences or divergences in the forecasts which might affect our confidence in the aggregate prediction. I develop a new aggregate forecasting model for French presidential elections to demonstrate this idea. The last paper shows and investigates a pattern in polling errors. We see that across multiple countries and electoral systems, polling errors favor the lesser party in two-party contests, i.e. polling errors favor Democratic candidates in Republican states and vice versa. We demonstrate a simple adjustment procedure based on this pattern to reduce the mean absolute polling error. We achieve a 16% reduction in the 2016 U.S. presidential election.

Twitter has become a popular data source in the recent decade and garnered a significant amount of attention as a surrogate data source for many important forecasting problems. Strong correlations have been observed between Twitter indicators and real-world trends spanning elections, stock markets, book sales, and flu outbreaks. A key ingredient to all methods that use Twitter for forecasting is to agree on a domain-specific vocabulary to track the pertinent tweets, which is typically provided by subject matter experts (SMEs). The language used in Twitter drastically differs from other forms of online discourse, such as news articles and blogs. It constantly evolves over time as users adopt popular hashtags to express their opinions. Thus, the vocabulary used by forecasting algorithms needs to be dynamic in nature and should capture emerging trends over time. This thesis proposes a novel unsupervised learning algorithm that builds a dynamic vocabulary using Probabilistic Soft Logic (PSL), a framework for probabilistic reasoning over relational domains. Using eight presidential elections from Latin America, we show how our query expansion methodology improves the performance of traditional election forecasting algorithms. Through this approach we demonstrate how we can achieve close to a two-fold increase in the number of tweets retrieved for predictions and a 36.90% reduction in prediction error. / Master of Science

M.S.