THE DEMOCRACY OF POLITICAL CONTRIBUTIONS IN THE AMERICAN STATES

The intention of campaign finance regulations was to reduce the influence of special interest groups while increasing citizen contributions. Critics have suggested an unintentional consequence of this policy of increasing bias in campaign contributions in favor of incumbents. These claims of intentional and unintentional consequences, however, have rarely been tested. My dissertation examines the intentional and unintentional consequences of campaign finance regulations in the American states. This study adopts a theoretical framework emphasizing the different effects of regulations on two distinctive types of contributors. A particularistic contributor, whose motivation is influencing policy, is likely to be affected by contribution limits. A universalistic contributor, motivated by helping his or her favorite candidates, is not likely to respond to regulations. Furthermore, the disparity of contributions is not expected to be affected by contribution limits. Two specific hypotheses reflecting the theoretical consideration are tested: 1) Restrictive contribution limits reduce the number and amount of particularistic contributions and increase the disparity between the numbers as well as the amounts of contributions, and 2) Contribution limits do not affect the number, the amount, or the disparities of universalistic contributions. Individual contribution records on gubernatorial elections are collected from 1990 to 2000 in 42 states. After aggregating individual contribution records by state and candidate, two analyses are conducted at the state and candidate level. The results indicate that campaign finance regulations work without the unintentional consequence of providing a financial advantage to incumbents at both the state and candidate levels. Contribution limits increase the number of total contributors, reduce the number and amount of particularistic contributions, and increase the number of universalistic contributors. In addition, further analyses show a dynamic effect of contribution limits on corporations, labor unions, individuals, parties, and ideology PACs. Restrictive contribution limits reduce the number and amount of corporate contributions, but only reduce the amount of labor union contributions. On the other hand, strict contribution limits encourage individual contributions, but discourage party and ideological PAC contributions. The intentional consequence of campaign finance regulations does not result in the unintentional consequence of increasing bias in favor of incumbents. These findings suggest that current regulations that limit campaign contributions should remain in place

Measuring Online Public Opinion for Decision Making: Application of Deep Learning on Political Context

Thoughts travel faster and farther through cyberspace where people interact with one another regardless of limitations in language, space, and time. Is a poll sufficient to measure people’s opinions in this era of hyperconnectivity? This study introduces a deep learning method to measure online public opinion. By analyzing Korean texts from Twitter, this study generates time-series data on online sentiment toward the South Korean president, comparing it to traditional presidential approval to demonstrate the independence of the masses’ online discourse. The study tests different algorithms and deploys the model with high accuracy and advancement. The analysis suggests that online public opinion represents a unique population as opposed to offline surveys. The study model examines Korean texts generated by online users and automatically predicts their sentiments, which translate into group attitudes by aggregation. The research method can extend to other studies, including those on environmental and cultural issues, which have greater online presence. This provides opportunities to examine the influences of social phenomenon, benefiting individuals seeking to understand people in an online context. Moreover, it helps scholars in analyzing those public opinions—online or offline—that are more important in their decision making to assess the practicality of the methods