The Partisan Next Door

In the United States, politics has become tribal and personalized. The influence of partisan divisions has extended beyond the political realm into everyday life, affecting relationships and workplaces as well as the ballot box. To help explain this trend, we examine the stereotypes Americans have of ordinary Democrats and Republicans. Using data from surveys, experiments, and Americans' own words, we explore the content of partisan stereotypes and find that they come in three main flavors—parties as their own tribes, coalitions of other tribes, or vehicles for political issues. These different stereotypes influence partisan conflict: people who hold trait-based stereotypes tend to display the highest levels of polarization, while holding issue-based stereotypes decreases polarization. This finding suggests that reducing partisan conflict does not require downplaying partisan divisions but shifting the focus to political priorities rather than identity—a turn to what we call responsible partisanship.

Political Violence

Political scientists, pundits, and citizens worry that America is entering a new period of violent partisan conflict. Provocative survey data show that up to 44% of the public support politically motivated violence in hypothetical scenarios. Yet, despite media attention, political violence is rare, amounting to a little more than 1% of violent hate crimes in the United States. We reconcile these seemingly conflicting facts with three large survey experiments (N=3,041), demonstrating that self-reported attitudes on political violence are biased upwards because of disengaged respondents, differing interpretations about questions relating to political violence, and personal dispositions towards violence that are unrelated to politics. Our estimates show that, depending on how the question is asked, existing estimates of support for partisan violence are 30-900% too large, and nearly all respondents support charging suspects who commit acts of political violence with a criminal offenses. These findings suggest that although recent acts of political violence dominate the news, they do not portend a new era of violent conflict.

A Deep Learning Based Approach for Fake News Detection

Owing to the rapid explosion of social media platforms in the past decade, we spread and consume information via the internet at an expeditious rate. It has caused an alarming proliferation of fake news on social networks. The global nature of social networks has facilitated international blowout of fake news. Fake news has proven to increase political polarization and partisan conflict. Fake news is also found to be more rampant on social media than mainstream media. The evil of fake news is garnering a lot of attention and research effort. In this work, we have tried to handle the spread of fake news via tweets. We have performed fake news classification by employing user characteristics as well as tweet text. Thus, trying to provide a holistic solution for fake news detection. For classifying user characteristics, we have used the XGBoost algorithm which is an ensemble of decision trees utilising the boosting method. Further to correctly classify the tweet text we used various natural language processing techniques to preprocess the tweets and then applied a sequential neural network and state-of-the-art BERT transformer to classify the tweets. The models have then been evaluated and compared with various baseline models to show that our approach effectively tackles this problemOwing to the rapid explosion of social media platforms in the past decade, we spread and consume information via the internet at an expeditious rate. It has caused an alarming proliferation of fake news on social networks. The global nature of social networks has facilitated international blowout of fake news. Fake news has proven to increase political polarization and partisan conflict. Fake news is also found to be more rampant on social media than mainstream media. The evil of fake news is garnering a lot of attention and research effort. In this work, we have tried to handle the spread of fake news via tweets. We have performed fake news classification by employing user characteristics as well as tweet text. Thus, trying to provide a holistic solution for fake news detection. For classifying user characteristics, we have used the XGBoost algorithm which is an ensemble of decision trees utilising the boosting method. Further to correctly classify the tweet text we used various natural language processing techniques to preprocess the tweets and then applied a sequential neural network and state-of-the-art BERT transformer to classify the tweets. The models have then been evaluated and compared with various baseline models to show that our approach effectively tackles this problem

Supreme Court Nominations at the Bar of Political Conflict: The Strange and Uncertain Career of the Liberal Consensus in Law

Nominations to the US Supreme Court have become increasingly important and contentious in America politics in recent decades. Reasons include the growing significance of constitutional law to the prospects of political power, accompanied by historical developments in the relative power of the competing party coalitions that have placed even more focus on the composition of the Court. Meanwhile, partisan conflict and stalemate have grown in the party systems and among We the People. In The Long Reach of the Sixties, Laura Kalman explores how the nomination struggles of Presidents Lyndon Johnson and Richard Nixon set the stage for the contemporary conflict besetting nominations and American politics more generally. Building on Kalman’s book, this review essay discusses the political and jurisprudential causes and implications of this conflict, with an eye toward what might lie ahead.

How the French Learned to Vote

Voting is a familiar civic activity today, yet few participants are probably aware of its long and controversial history, which was especially marked in the case of France, the country chosen for this study of how people learn to vote. Casting a ballot does not come naturally, and it also requires the technology to accomplish it, besides the legal framework to regulate it. Democratization and the development of citizenship are lengthy processes, like the achievement of free and fair elections involving a secret ballot for all adults. A great experiment with mass voting for men was initiated in France in 1789, only for recurrent upheaval to ensure that the question of who could vote, and how they did so, was frequently re-examined and revised. The entire electoral system was a constant source of partisan conflict, popular protest, and innovation, throwing the great issues around voting into particularly sharp focus. This is the first book to explore the contested and contingent practice of the vote in a comprehensive fashion, over a time span that begins before the French Revolution and concludes with the present, while according significant space to local as well as national elections. The thematic analysis will assist an understanding of those countries where democracy remains in its infancy, while also offering insight into widespread contemporary concerns about declining electoral turnout. In so far as the global adoption of voting is reflected in the context of a specific society, it will be of interest to political scientists as well as historians.

SHOULD BITCOIN BE HELD UNDER THE U.S. PARTISAN CONFLICT?

This paper probes the interrelationship between Bitcoin price (BP) and the U.S. partisan conflict (PC) by performing the bootstrap full- and sub-sample Granger causality tests. The positive influence from PC to BP reveals that Bitcoin can be considered as a tool to avoid the uncertainty caused by the rise in PC. However, this view cannot be supported by the negative impact, the major reason is that the burst of bubble undermines the hedging ability of Bitcoin. The above results are inconsistent with the intertemporal capital asset pricing model (ICAPM), underlining that high PC may drive BP to rise, in order to compensate for the losses and costs from factionalism. Conversely, BP has a negative impact on PC, suggesting that the U.S. political situation can be reflected by the Bitcoin market. Under the circumstance of the fiercer factionalism in the U.S., this investigation can benefit investors and related authorities.