Shift-share designs are gaining popularity in political science. This article introduces what shift-share designs are, reviews their application in the literature, synthesizes recent methodological developments, and discusses their potential utility in the field. Although shift-share designs have a long historical use in economics, their causal properties only recently began to be understood. Articles in political science tend to be aware of these developments, but do not fully discuss and test identifying assumptions and sometimes apply the methods incorrectly. Most articles rely on the share exogeneity framework, suggesting that the shifter exogeneity framework is underutilized despite its comparable prevalence in economics. I illustrate shifter exogeneity framework and develop auxiliary theoretical results that are potentially useful in applying the framework in political science settings.
We present the first large-scale computational study of political delegitimization discourse (PDD), defined as symbolic attacks on the normative validity of political entities. We curate and manually annotate a novel Hebrew-language corpus of 10,410 sentences drawn from Knesset speeches (1993-2023), Facebook posts (2018-2021), and leading news outlets, of which 1,812 instances (17.4\%) exhibit PDD and 642 carry additional annotations for intensity, incivility, target type, and affective framing. We introduce a two-stage classification pipeline combining finetuned encoder models and decoder LLMs. Our best model (DictaLM 2.0) attains an F$_1$ of 0.74 for binary PDD detection and a macro-F$_1$ of 0.67 for classification of delegitimization characteristics. Applying this classifier to longitudinal and cross-platform data, we see a marked rise in PDD over three decades, higher prevalence on social media versus parliamentary debate, greater use by male than female politicians, and stronger tendencies among right-leaning actors - with pronounced spikes during election campaigns and major political events. Our findings demonstrate the feasibility and value of automated PDD analysis for understanding democratic discourse.
Elections are not the only but arguably one of the most important pillars for the proper functioning of liberal democracies. Recent evidence across the globe shows that it is not straightforward to conduct them in a free and fair manner. One constant concern is the role of money in politics, more specifically, election campaign financing. Frequent scandals are proof of the difficulties encountered with current approaches to tackle the issue. Suggestions on how to overcome the problem exist but seem difficult to implement. With the help of blockchain technology we might be able to make a step forward. A separate crypto currency specifically designed to pay for costs of political campaigning and advertising could be introduced. Admittedly, at this stage, there are many open questions. However, under the assumption that blockchain technology is here to stay, it is an idea that deserves further exploration.
Building on recent work studying content in the online advertising ecosystem, including our own prior study of political ads on the web during the 2020 U.S. elections, we analyze political ad content appearing on websites leading up to and during the 2024 U.S. elections. Crawling a set of 745 news and media websites several times from three different U.S. locations (Atlanta, Seattle, and Los Angeles), we collect a dataset of over 15000 ads, including (at least) 315 political ads, and we analyze it quantitatively and qualitatively. Among our findings: a prevalence of clickbait political news ads, echoing prior work; a seemingly new emphasis (compared to 2020) on voting safety and eligibility ads, particularly in Atlanta; and non-election related political ads around the Israel-Palestine conflict, particularly in Seattle. We join prior work in calling for more oversight and transparency of political-related ads on the web. Our dataset is available at https://ad-archive.cs.washington.edu.
Examining the alignment of large language models (LLMs) has become increasingly important, e.g., when LLMs fail to operate as intended. This study examines the alignment of LLMs with human values for the domain of politics. Prior research has shown that LLM-generated outputs can include political leanings and mimic the stances of political parties on various issues. However, the extent and conditions under which LLMs deviate from empirical positions are insufficiently examined. To address this gap, we analyze the factors that contribute to LLMs' deviations from empirical positions on political issues, aiming to quantify these deviations and identify the conditions that cause them. Drawing on findings from cognitive science about representativeness heuristics, i.e., situations where humans lean on representative attributes of a target group in a way that leads to exaggerated beliefs, we scrutinize LLM responses through this heuristics' lens. We conduct experiments to determine how LLMs inflate predictions about political parties, which results in stereotyping. We find that while LLMs can mimic certain political parties' positions, they often exaggerate these positions more than human survey respondents do. Also, LLMs tend to overemphasize representativeness more than humans. This study highlights the susceptibility of LLMs to representativeness heuristics, suggesting a potential vulnerability of LLMs that facilitates political stereotyping. We also test prompt-based mitigation strategies, finding that strategies that can mitigate representative heuristics in humans are also effective in reducing the influence of representativeness on LLM-generated responses.
Jillian Fisher, Ruth E. Appel, Chan Young Park
et al.
AI systems often exhibit political bias, influencing users' opinions and decisions. While political neutrality-defined as the absence of bias-is often seen as an ideal solution for fairness and safety, this position paper argues that true political neutrality is neither feasible nor universally desirable due to its subjective nature and the biases inherent in AI training data, algorithms, and user interactions. However, inspired by Joseph Raz's philosophical insight that "neutrality [...] can be a matter of degree" (Raz, 1986), we argue that striving for some neutrality remains essential for promoting balanced AI interactions and mitigating user manipulation. Therefore, we use the term "approximation" of political neutrality to shift the focus from unattainable absolutes to achievable, practical proxies. We propose eight techniques for approximating neutrality across three levels of conceptualizing AI, examining their trade-offs and implementation strategies. In addition, we explore two concrete applications of these approximations to illustrate their practicality. Finally, we assess our framework on current large language models (LLMs) at the output level, providing a demonstration of how it can be evaluated. This work seeks to advance nuanced discussions of political neutrality in AI and promote the development of responsible, aligned language models.
Modeling political structures by simplicial complexes, we investigate whether introducing a mediator into a substructure increases or decreases the stability of the overall structure. We prove theorems that quantify the stability of a political structure when $n$ mediators are introduced, either one by one or simultaneously. We also examine how the stability is affected when a single agent is split into two. In addition, stability is expressed in terms of the $h$-vector, and special attention is given to a class of political structures modeled by shellable simplicial complexes. In the latter context, we analyze weighted political structures and examples of political structures modeled by independence complexes of graphs. This approach provides a rigorous, stepwise analysis of stability under different structural modifications, showing how the combinatorial and topological properties of the simplicial complex govern the structure's stability.
Analysis of parliamentary speeches and political-party manifestos has become an integral area of computational study of political texts. While speeches have been overwhelmingly analysed using unsupervised methods, a large corpus of manifestos with by-statement political-stance labels has been created by the participants of the MARPOR project. It has been recently shown that these labels can be predicted by a neural model; however, the current approach relies on provided statement boundaries, limiting out-of-domain applicability. In this work, we propose and test a range of unified split-and-label frameworks -- based on linear-chain CRFs, fine-tuned text-to-text models, and the combination of in-context learning with constrained decoding -- that can be used to jointly segment and classify statements from raw textual data. We show that our approaches achieve competitive accuracy when applied to raw text of political manifestos, and then demonstrate the research potential of our method by applying it to the records of the UK House of Commons and tracing the political trajectories of four major parties in the last three decades.
Achieving political consensus is crucial yet challenging for the effective functioning of social governance. However, although frontier AI systems represented by large language models (LLMs) have developed rapidly in recent years, their capabilities in this scope are still understudied. In this paper, we introduce PoliCon, a novel benchmark constructed from 2,225 high-quality deliberation records of the European Parliament over 13 years, ranging from 2009 to 2022, to evaluate the ability of LLMs to draft consensus resolutions based on divergent party positions under varying collective decision-making contexts and political requirements. Specifically, PoliCon incorporates four factors to build each task environment for finding different political consensus: specific political issues, political goals, participating parties, and power structures based on seat distribution. We also developed an evaluation framework based on social choice theory for PoliCon, which simulates the real voting outcomes of different political parties to assess whether LLM-generated resolutions meet the requirements of the predetermined political consensus. Our experimental results demonstrate that even state-of-the-art models remain undersatisfied with complex tasks like passing resolutions by a two-thirds majority and addressing security issues, while uncovering their inherent partisan biases and revealing some behaviors LLMs show to achieve the consensus, such as prioritizing the stance of the dominant party instead of uniting smaller parties, which highlights PoliCon's promise as an effective platform for studying LLMs' ability to promote political consensus. The code and dataset are released at https://zowiezhang.github.io/projects/PoliCon.
Human-computer interaction scholars are increasingly touching on topics related to politics or democracy. As these concepts are ambiguous, an examination of concepts' invoked meanings aids in the self-reflection of our research efforts. We conduct a thematic analysis of all papers with the word `politics' in abstract, title or keywords ($n$=378) and likewise 152 papers with the word `democracy.' We observe that these words are increasingly being used in human-computer interaction, both in absolute and relative terms. At the same time, we show that researchers invoke these words with diverse levels of analysis in mind: the early research focused on mezzo-level (i.e., small groups), but more recently the work has begun to include macro-level analysis (i.e., society and politics as played in the public sphere). After the increasing focus on the macro-level, we see a transition towards more normative and activist research, in some areas it replaces observational and empirical research. These differences indicate semantic differences, which -- in the worst case -- may limit scientific progress. We bring these differences visible to help in further exchanges of ideas and human-computer interaction community to explore how it orients itself to politics and democracy.
Gabriela Juncosa, Taha Yasseri, Julia Koltai
et al.
Quantifying how individuals react to social influence is crucial for tackling collective political behavior online. While many studies of opinion in public forums focus on social feedback, they often overlook the potential for human interactions to result in self-censorship. Here, we investigate political deliberation in online spaces by exploring the hypothesis that individuals may refrain from expressing minority opinions publicly due to being exposed to toxic behavior. Analyzing conversations under YouTube videos from six prominent US news outlets around the 2020 US presidential elections, we observe patterns of self-censorship signaling the influence of peer toxicity on users' behavior. Using hidden Markov models, we identify a latent state consistent with toxicity-driven silence. Such state is characterized by reduced user activity and a higher likelihood of posting toxic content, indicating an environment where extreme and antisocial behaviors thrive. Our findings offer insights into the intricacies of online political deliberation and emphasize the importance of considering self-censorship dynamics to properly characterize ideological polarization in digital spheres.
El artículo aborda la segunda estancia que, entre 1916 y 1918, Luis Emilio Recabarren realizó en la ciudad de Buenos Aires. Desde una perspectiva transnacional, analiza sus planteos sobre el sindicalismo socialista y, en particular, el rol que en el desarrollo de esas ideas tuvo la Argentina como espacio referencial.
1789-, Labor in politics. Political activity of the working class
AbstractThis paper explores how the years after World War II in Western Europe were an extreme case of working-class power. An initial theoretical discussion claims that while class can best be understood as a social category, at the same time politics in the broadest sense—and hence class-based movements—can shape social structure. This was the case in the in the post-World War II period when the working class dominated West European societies: especially perhaps in Britain, the “weight” of the working class shaped the nation. Trade union organization and state power ensured collective rights and were the basis for autonomous consumption; class identity was a source of pride. The end of this period saw trade union density continuing to increase. In the USA as well as in Western Europe, the power of management in the workplace was challenged. Especially in Western Europe, there was widespread radicalization of working-class youth. This was the last offensive of the working-class movement. However, power in the workplace remained essentially a veto-power. In the USA, oppositional politics became ethnic politics and even before de-industrialisation the white working class began to abandon its traditional politics. Nonetheless, in Western Europe the long shadow of the working-class movement ensured the partial survival of social rights long after the traditional social basis of the movement had withered away.
Alyvia Walters, Tawfiq Ammari, Kiran Garimella
et al.
Visual culture has long been deployed by actors across the political spectrum as tools of political mobilization, and have recently incorporated new communication tools, such as memes, GIFs, and emojis. In this study, we analyze the top-circulated Facebook memes relating to critical race theory (CRT) from May 2021 - May 2022 to investigate their visual and textual appeals. Using image clustering techniques and critical discourse analysis, we find that both pro- and anti-CRT memes deploy similar rhetorical tactics to make bifurcating arguments, most of which do not pertain to the academic formulations of CRT. Instead, these memes manipulate definitions of racism and antiracism to appeal to their respective audiences. We argue that labeling such discursive practices as simply a symptom of "post-truth" politics is a potentially unproductive stance. Instead, theorizing the knowledge-building practices of these memes through a lens of political epistemology allows us to understand how they produce meaning.
Ehsan-Ul Haq, Haris Bin Zia, Reza Hadi Mogavi
et al.
We share the largest dataset for the Pakistani Twittersphere consisting of over 49 million tweets, collected during one of the most politically active periods in the country. We collect the data after the deposition of the government by a No Confidence Vote in April 2022. This large-scale dataset can be used for several downstream tasks such as political bias, bots detection, trolling behavior, (dis)misinformation, and censorship related to Pakistani Twitter users. In addition, this dataset provides a large collection of tweets in Urdu and Roman Urdu that can be used for optimizing language processing tasks.
Valéria Oliveira de Vasconcelos, Tiago Zanquêta de Souza
Este artigo, de natureza teórica e ensaística, tem como objetivo investigar a produção científica dos últimos 21 anos nos Anais das Reuniões Nacionais da Associação Nacional de Pós-graduação e Pesquisa em Educação (ANPEd), especificamente do Grupo de Trabalho 06 – Educação Popular. Para tanto, garimpamos palavras geradoras presentes nessas publicações e buscamos apreender como essa práxis educativa vem alicerçando a cosmovisão do grupo por meio das pesquisas e práticas que a sustenta. A metodologia se assentou em estudo qualitativo, descritivo e analítico por meio do Estado da Arte. Para a coleta de dados utilizamos o aplicativo Voyant tools. O corpus teórico consiste em 48 resumos expandidos e 223 trabalhos completos, com a participação de 245 autoras e 93 autores. A análise dos dados foi realizada tomando as dez palavras geradoras mais frequentes nessas publicações e articulando-as com referencial específico. Os resultados indicam que a Educação Popular que emerge das pesquisas do GT06 é biófila e majoritariamente freiriana, feminina e feminista. Ademais, a EP foi para a escola! Os processos de pesquisa e ação sedimentam caminhos com os grupos sociais periféricos com quem são investigadas práticas na educação escolar/não escolar, na saúde e na cultura, mediatizadas pelo mundo.
Social Sciences, Labor in politics. Political activity of the working class
Predicting the political leaning of social media users is an increasingly popular task, given its usefulness for electoral forecasts, opinion dynamics models and for studying the political dimension of polarization and disinformation. Here, we propose a novel unsupervised technique for learning fine-grained political leaning from the textual content of social media posts. Our technique leverages a deep neural network for learning latent political ideologies in a representation learning task. Then, users are projected in a low-dimensional ideology space where they are subsequently clustered. The political leaning of a user is automatically derived from the cluster to which the user is assigned. We evaluated our technique in two challenging classification tasks and we compared it to baselines and other state-of-the-art approaches. Our technique obtains the best results among all unsupervised techniques, with micro F1 = 0.426 in the 8-class task and micro F1 = 0.772 in the 3-class task. Other than being interesting on their own, our results also pave the way for the development of new and better unsupervised approaches for the detection of fine-grained political leaning.
Entre los años 1926 y 1934, Elías Castelnuovo estrena seis obras de teatro vinculadas con distintos proyectos colectivos, que se configuran como los iniciadores del teatro “independiente” argentino: Teatro Libre (1927), Teatro Experimental del Arte (1928) y Teatro Proletario (1934). La hipótesis de que estas obras teatrales se proponen criticar la cultura y la sociedad dominantes, al igual que su producción narrativa anterior, a partir de la apelación a un tono tragicómico y de la apropiación de rasgos vanguardistas permite reconsiderar su recepción mayoritaria y advertir la singularidad del entramado entre estética y política de su apuesta literaria.
1789-, Labor in politics. Political activity of the working class
What would the inputs be to a machine whose output is the destabilization of a robust democracy, or whose emanations could disrupt the political power of nations? In the recent essay "The Coming AI Hackers," Schneier (2021) proposed a future application of artificial intelligences to discover, manipulate, and exploit vulnerabilities of social, economic, and political systems at speeds far greater than humans' ability to recognize and respond to such threats. This work advances the concept by applying to it theory from machine learning, hypothesizing some possible "featurization" (input specification and transformation) frameworks for AI hacking. Focusing on the political domain, we develop graph and sequence data representations that would enable the application of a range of deep learning models to predict attributes and outcomes of political, particularly legislative, systems. We explore possible data models, datasets, predictive tasks, and actionable applications associated with each framework. We speculate about the likely practical impact and feasibility of such models, and conclude by discussing their ethical implications.