Moral rhetoric presents a strategic dilemma for female politicians, who must navigate stereotypes while appealing to copartisan voters. In this article, we investigate how gender shapes elite moral rhetoric given the influence of partisanship, ideology, gender stereotypes, and moral psychology. Drawing on moral foundations theory, we examine how female and male representatives differ in their emphasis on the five foundations of care, fairness, authority, loyalty, and purity. Using the Moral Foundations Dictionary, we analyze a corpus of 2.23 million tweets by U.S. Congress members between 2013 and 2021. We find that female representatives are more likely to emphasize care and less likely to emphasize authority and loyalty than their male peers. However, when subsetting by party, we find that gender effects are most pronounced among Democrats and largely negligible among Republicans. These findings offer insight into the rhetorical dynamics of political leadership at the intersection of gender and partisan identities.

In this article, we present a machine learning-based solution for matching the performance of the gold standard of double-blind human coding when it comes to content analysis in comparative politics. We combine a quantitative text analysis approach with supervised learning and limited human resources in order to classify the front-page articles of a leading Hungarian daily newspaper based on their full text. Our goal was to assign items in our dataset to one of 21 policy topics based on the codebook of the Comparative Agendas Project. The classification of the imbalanced classes of topics was handled by a hybrid binary snowball workflow. This relies on limited human resources as well as supervised learning; it simplifies the multiclass problem to one of binary choice; and it is based on a snowball approach as we augment the training set with machine-classified observations after each successful round and also between corpora. Our results show that our approach provided better precision results (of over 80% for most topic codes) than what is customary for human coders and most computer-assisted coding projects. Nevertheless, this high precision came at the expense of a relatively low, below 60%, share of labeled articles.

Automated text analysis methods have made possible the classification of large corpora of text by measures such as topic and tone. Here, we provide a guide to help researchers navigate the consequential decisions they need to make before any measure can be produced from the text. We consider, both theoretically and empirically, the effects of such choices using as a running example efforts to measure the tone of New York Times coverage of the economy. We show that two reasonable approaches to corpus selection yield radically different corpora and we advocate for the use of keyword searches rather than predefined subject categories provided by news archives. We demonstrate the benefits of coding using article segments instead of sentences as units of analysis. We show that, given a fixed number of codings, it is better to increase the number of unique documents coded rather than the number of coders for each document. Finally, we find that supervised machine learning algorithms outperform dictionaries on a number of criteria. Overall, we intend this guide to serve as a reminder to analysts that thoughtfulness and human validation are key to text-as-data methods, particularly in an age when it is all too easy to computationally classify texts without attending to the methodological choices therein.

Political scientists often wish to classify documents based on their content to measure variables, such as the ideology of political speeches or whether documents describe a Militarized Interstate Dispute. Simple classifiers often serve well in these tasks. However, if words occurring early in a document alter the meaning of words occurring later in the document, using a more complicated model that can incorporate these time-dependent relationships can increase classification accuracy. Long short-term memory (LSTM) models are a type of neural network model designed to work with data that contains time dependencies. We investigate the conditions under which these models are useful for political science text classification tasks with applications to Chinese social media posts as well as US newspaper articles. We also provide guidance for the use of LSTM models.

Word vectorization is an emerging text-as-data method that shows great promise for automating the analysis of semantics—here, the cultural meanings of words—in large volumes of text. Yet successes with this method have largely been confined to massive corpora where the meanings of words are presumed to be fixed. In political science applications, however, many corpora are comparatively small and many interesting questions hinge on the recognition that meaning changes over time. Together, these two facts raise vexing methodological challenges. Can word vectors trace the changing cultural meanings of words in typical small corpora use cases? I test four time-sensitive implementations of word vectors (word2vec) against a gold standard developed from a modest data set of 161 years of newspaper coverage. I find that one implementation method clearly outperforms the others in matching human assessments of how public dialogues around equality in America have changed over time. In addition, I suggest best practices for using word2vec to study small corpora for time series questions, including bootstrap resampling of documents and pretraining of vectors. I close by showing that word2vec allows granular analysis of the changing meaning of words, an advance over other common text-as-data methods for semantic research questions.

When do parties respond to their political rivals and when do they ignore them? This article presents a new computational framework to detect, analyze and predict partisan responsiveness by showing when parties on opposite poles of the political spectrum react to each other’s agendas and thereby contribute to polarization. Once spikes in responsiveness are detected and categorized using latent Dirichlet allocation, we utilize the terms that comprise the topics, together with a gradient descent solver, to assess the classifier’s predictive accuracy. Using 10,597 documents from the official websites of radical right and ethnic political parties in Slovakia (2004–2014), the analysis predicts which political issues will elicit partisan reactions, and which will be ignored, with an accuracy of 83% (F-measure) and outperforms both Random Forest and Naive Bayes classifiers. Subject matter experts validate the approach and interpret the results.

Analyzing audiovisual communication is challenging because its content is highly symbolic and less rule-governed than verbal material. But audiovisual messages are important to understand: they amplify, enrich, and complicate the meaning of textual information. We describe a fully-reproducible approach to analyzing video content using minimally—but systematically—trained online workers. By aggregating the work of multiple coders, we achieve reliability, validity, and costs that equal those of traditional, intensively trained research assistants, with much greater speed, transparency, and replicability. We argue that measurement strategies relying on the “wisdom of the crowd” provide unique advantages for researchers analyzing complex and intricate audiovisual political content.

Automated text analysis allows researchers to analyze large quantities of text. Yet, comparative researchers are presented with a big challenge: across countries people speak different languages. To address this issue, some analysts have suggested using Google Translate to convert all texts into English before starting the analysis (Lucas et al. 2015). But in doing so, do we get lost in translation? This paper evaluates the usefulness of machine translation for bag-of-words models—such as topic models. We use the europarl dataset and compare term-document matrices (TDMs) as well as topic model results from gold standard translated text and machine-translated text. We evaluate results at both the document and the corpus level. We first find TDMs for both text corpora to be highly similar, with minor differences across languages. What is more, we find considerable overlap in the set of features generated from human-translated and machine-translated texts. With regard to LDA topic models, we find topical prevalence and topical content to be highly similar with again only small differences across languages. We conclude that Google Translate is a useful tool for comparative researchers when using bag-of-words text models.

Words matter in politics. The rhetoric that political elites employ structures civic discourse. The emergence of social media platforms as a medium of politics has enabled ordinary citizens to express their ideological inclinations by adopting the lexicon of political elites. This avails to researchers a rich new source of data in the study of political ideology. However, existing ideological text-scaling methods fail to produce meaningful inferences when applied to the short, informal style of textual content that is characteristic of social media platforms such as Twitter. This paper introduces the first viable approach to the estimation of individual-level ideological positions derived from social media content. This method allows us to position social media users—be they political elites, parties, or citizens—along a shared ideological dimension. We validate the proposed method by demonstrating correlation with existing measures of ideology across various political contexts and multiple languages. We further demonstrate the ability of ideological estimates to capture derivative signal by predicting out-of-sample, individual-level voting intentions. We posit that social media data can, when properly modeled, better capture derivative signal than discrete scales used in more traditional survey instruments.