Policy Change and Public Opinion: Measuring Shifting Political Sentiment With Social Media Data

This article uses Twitter data and machine-learning methods to analyze the causal impact of the Supreme Court’s legalization of same-sex marriage at the federal level in the United States on political sentiment and discourse toward gay rights. In relying on social media text data, this project constructs a large data set of expressed political opinions in the short time frame before and after the Obergefell v. Hodges decision. Due to the variation in state laws regarding the legality of same-sex marriage prior to the Supreme Court’s decision, I use a difference-in-difference estimator to show that, in those states where the Court’s ruling produced a policy change, there was relatively more negative movement in public opinion toward same-sex marriage and gay rights issues as compared with other states. This confirms previous studies that show Supreme Court decisions polarize public opinion in the short term, extends previous results by demonstrating opinion becomes relatively more negative in states where policy is overturned, and demonstrates how to use social media data to engage in causal analyses

Policy Change and Public Opinion: Measuring Shifting Political Sentiment With Social Media Data

This article uses Twitter data and machine-learning methods to analyze the causal impact of the Supreme Court’s legalization of same-sex marriage at the federal level in the United States on political sentiment and discourse toward gay rights. In relying on social media text data, this project constructs a large data set of expressed political opinions in the short time frame before and after the Obergefell v. Hodges decision. Due to the variation in state laws regarding the legality of same-sex marriage prior to the Supreme Court’s decision, I use a difference-in-difference estimator to show that, in those states where the Court’s ruling produced a policy change, there was relatively more negative movement in public opinion toward same-sex marriage and gay rights issues as compared with other states. This confirms previous studies that show Supreme Court decisions polarize public opinion in the short term, extends previous results by demonstrating opinion becomes relatively more negative in states where policy is overturned, and demonstrates how to use social media data to engage in causal analyses

New Perspectives in Political Communication

This dissertation contains three chapters exploring the nature of political communication and public opinion formation by analyzing social media data. Each chapter uses original sets of Twitter data to examine the public’s response to major shifts in public policy (Chapter Two), the differences between partisan networks (Chapter Three), and how citizens engage with gun policy after mass shootings (Chapter Four). Chapter Two examines how public opinion towards gay marriage changed before and after the legalization of same-sex marriage as a result of the 2016 Obergefell v. Hodges Supreme Court decision. Exploiting the variation in state law prior to the Court’s decision, I use a difference-in-difference approach to find causal evidence that citizens residing in states where the Supreme Court overturns state laws are more likely to have a negative opinion of the federal decision. In Chapter Three, I collect an original dataset of Twitter conversations about the American political parties to develop a supervised learning algorithm that classifies users as liberal or conservative, using these labels to then map out separate ideological network structures. Analyzing these networks, I find significant differences in how conservative and liberal citizens form online networks, leading to important consequences for information diffusion and action coordination. In Chapter Four, I examine how messages from the political and media elite concerning gun control impact citizen engagement with gun policy issues in the wake of high-profile mass shootings. I analyze the impact of elite messaging with a panel data set of sixty thousand partisan Twitter users, data that includes each user’s full Twitter history as well as information on which accounts they follow. By building this Twitter panel, I am able to better determine which elite messages each user receives and whether the recipient chooses to engage with gun policy. I find that elite messages increase the likelihood a user will engage with gun policy issues, but further determine that we must broaden the notion of elite to include users only considered influential on the Twitter platform.</p

Finding Social Media Trolls: Dynamic Keyword Selection Methods for Rapidly-Evolving Online Debates

Online harassment is a significant social problem. Prevention of online harassment requires rapid detection of harassing, offensive, and negative social media posts. In this paper, we propose the use of word embedding models to identify offensive and harassing social media messages in two aspects: detecting fast-changing topics for more effective data collection and representing word semantics in different domains. We demonstrate with preliminary results that using the GloVe (Global Vectors for Word Representation) model facilitates the discovery of new and relevant keywords to use for data collection and trolling detection. Our paper concludes with a discussion of a research agenda to further develop and test word embedding models for identification of social media harassment and trolling.Comment: AI for Social Good workshop at NeurIPS (2019

Structure of the stationary phase survival protein YuiC from B.subtilis

- Background: Stationary phase survival proteins (Sps) were found in Firmicutes as having analogous domain compositions, and in some cases genome context, as the resuscitation promoting factors of Actinobacteria, but with a different putative peptidoglycan cleaving domain. - Results: The first structure of a Firmicute Sps protein YuiC from B. subtilis, is found to be a stripped down version of the cell-wall peptidoglycan hydrolase MltA. The YuiC structures are of a domain swapped dimer, although some monomer is also found in solution. The protein crystallised in the presence of pentasaccharide shows a 1,6-anhydrodisaccharide sugar product, indicating that YuiC cleaves the sugar backbone to form an anhydro product at least on lengthy incubation during crystallisation. - Conclusions: The structural simplification of MltA in Sps proteins is analogous to that of the resuscitation promoting factor domains of Actinobacteria, which are stripped down versions of lysozyme and soluble lytic transglycosylase proteins