Automated content analysis increasingly supports communication research, yet scaling manual coding into computational pipelines raises concerns about measurement reliability and validity. We introduce a Hierarchical Error Correction (HEC) framework that treats model failures as layered measurement errors (knowledge gaps, reasoning limitations, and complexity constraints) and targets the layers that most affect inference. The framework implements a three-phase methodology: systematic error profiling across hierarchical layers, targeted intervention design matched to dominant error sources, and rigorous validation with statistical testing. Evaluating HEC across health communication (medical specialty classification) and political communication (bias detection), and legal tasks, we validate the approach with five diverse large language models. Results show average accuracy gains of 11.2 percentage points (p < .001, McNemar's test) and stable conclusions via reduced systematic misclassification. Cross-model validation demonstrates consistent improvements (range: +6.8 to +14.6pp), with effectiveness concentrated in moderate-to-high baseline tasks (50-85% accuracy). A boundary study reveals diminished returns in very high-baseline (>85%) or precision-matching tasks, establishing applicability limits. We map layered errors to threats to construct and criterion validity and provide a transparent, measurement-first blueprint for diagnosing error profiles, selecting targeted interventions, and reporting reliability/validity evidence alongside accuracy. This applies to automated coding across communication research and the broader social sciences.

This paper presents a novel dataset of public broadcast interviews featuring high-ranking German politicians. The interviews were sourced from YouTube, transcribed, processed for speaker identification, and stored in a tidy and open format. The dataset comprises 99 interviews with 33 different German politicians across five major interview formats, containing a total of 28,146 sentences. As the first of its kind, this dataset offers valuable opportunities for research on various aspects of political communication in the (German) political contexts, such as agenda-setting, interviewer dynamics, or politicians' self-presentation.

This paper aims to provide a comparison between texts produced by French and Italian politicians on polarizing issues, such as immigration and the European Union, and their chatbot counterparts created with ChatGPT 3.5. In this study, we focus on implicit communication, in particular on presuppositions and their functions in discourse, which have been considered in the literature as a potential linguistic feature of manipulation. This study also aims to contribute to the emerging literature on the pragmatic competences of Large Language Models. Our results show that, on average, ChatGPT-generated texts contain more questionable presuppositions than the politicians' texts. Furthermore, most presuppositions in the former texts show a different distribution and different discourse functions compared to the latter. This may be due to several factors inherent in the ChatGPT architecture, such as a tendency to be verbose and repetitive in longer texts, as exemplified by the occurrence of political slogans mainly formed by change-of-state verbs as presupposition triggers (e.g., dobbiamo costruire il nostro futuro, 'we must build our future').

Abstract: This study investigates sentiment patterns within Spanish political party communications on Twitter by employing BETO and RoBERTuito, two pre-trained language models optimized for Spanish text. With a dataset comprising tweets from major Spanish political parties--PSOE, PP, Vox, Podemos, and Ciudadanos--spanning 2019 to 2024, this research analyzes sentiment distributions and explores the relationship between sentiment and party ideology. Results reveal that both models consistently identify a predominant Neutral sentiment across parties, with significant variations in Negative and Positive sentiments that align with ideological distinctions. Vox exhibits higher levels of Negative sentiment, while PSOE demonstrates a relatively high Positive sentiment, supporting the hypothesis that emotional appeals in political messaging reflect ideological stances. This study highlights the utility of pre-trained models in analyzing non-English social media sentiment and underscores the implications of sentiment dynamics in shaping public discourse within a multi-party system. Keywords: Spanish political parties, sentiment analysis, Twitter, BETO, RoBERTuito, political communication, ideology, social media analysis 1. Introduction In the era of digital politics, social media has emerged as a potent platform where public opinion is actively shaped and reflected. For countries like Spain, where a spectrum of political ideologies coexists, understanding the sentiment behind political communications becomes crucial. Sentiment analysis, particularly on platforms like Twitter, serves as a powerful tool to decode public attitudes and the emotional undertones in political party communications (Cambria et al., 2013; Giachanou & Crestani, 2016). By leveraging sentiment analysis, researchers can quantify and interpret political sentiments, thereby offering insights into party strategies and public reactions. In Spain's unique political landscape, where new and traditional parties like Podemos, PSOE, PP, Ciudadanos, and Vox engage vigorously on social media, analyzing sentiment can reveal the underlying strategies each employs. Recent advancements in pre-trained models tailored for the Spanish language, such as BETO and RoBERTuito, offer refined accuracy in detecting nuanced sentiments within Spanish tweets (Pérez et al., 2021).

Most events in the world receive at most brief coverage by the news media. Occasionally, however, an event will trigger a media storm, with voluminous and widespread coverage lasting for weeks instead of days. In this work, we develop and apply a pairwise article similarity model, allowing us to identify story clusters in corpora covering local and national online news, and thereby create a comprehensive corpus of media storms over a nearly two year period. Using this corpus, we investigate media storms at a new level of granularity, allowing us to validate claims about storm evolution and topical distribution, and provide empirical support for previously hypothesized patterns of influence of storms on media coverage and intermedia agenda setting.

In today's digital age, images have emerged as powerful tools for politicians to engage with their voters on social media platforms. Visual content possesses a unique emotional appeal that often leads to increased user engagement. However, research on visual communication remains relatively limited, particularly in the Global South. This study aims to bridge this gap by employing a combination of computational methods and qualitative approach to investigate the visual communication strategies employed in a dataset of 11,263 Instagram posts by 19 Brazilian presidential candidates in 2018 and 2022 national elections. Through two studies, we observed consistent patterns across these candidates on their use of visual political communication. Notably, we identify a prevalence of celebratory and positively toned images. They also exhibit a strong sense of personalization, portraying candidates connected with their voters on a more emotional level. We note a substantial presence of screenshots from news websites and other social media platforms. Furthermore, text-edited images with portrayals emerge as a prominent feature. In light of these results, we engage in a discussion regarding the implications for the broader field of visual political communication. This article serves as a testament to the pivotal role that Instagram has played in shaping the narrative of two fiercely polarized Brazilian elections, casting a revealing light on the ever-evolving dynamics of visual political communication in the digital age. Finally, we propose avenues for future research in the realm of visual political communication. Introduction In the ever-evolving arena of election campaigns, candidates rely heavily on the media as their megaphone to amplify their messages to the masses. Over the years, the landscape of political communication has undergone a profound transformation. This transformation has been driven by the rise of online social media platforms, which have emerged as indispensable tools for candidates in their quest to gauge public sentiment and rally support from the electorate (Boulianne & Olof Larsson, 2023; Farkas & Bene, 2021). The significance of this transformation has been further accentuated by the global ascent of populist leaders, spanning diverse nations, who have wholeheartedly embraced social media as their primary mode of communication (Bernardi & Costa, 2020; Novoselova, 2020).

This study verified the effectiveness of Donald Trump's Twitter campaign in guiding agen-da-setting and deflecting political risk and examined Trump's Twitter communication strategy and explores the communication effects of his tweet content during Covid-19 pandemic. We collected all tweets posted by Trump on the Twitter platform from January 1, 2020 to December 31, 2020.We used Ordinary Least Squares (OLS) regression analysis with a fixed effects model to analyze the existence of the Twitter strategy. The correlation between the number of con-firmed daily Covid-19 diagnoses and the number of particular thematic tweets was investigated using time series analysis. Empirical analysis revealed Twitter's strategy is used to divert public attention from negative Covid-19 reports during the epidemic, and it posts a powerful political communication effect on Twitter. However, findings suggest that Trump did not use false claims to divert political risk and shape public opinion.

In this study we investigate how social media shape the networked public sphere and facilitate communication between communities with different political orientations. We examine two networks of political communication on Twitter, comprised of more than 250,000 tweets from the six weeks leading up to the 2010 U.S. congressional midterm elections. Using a combination of network clustering algorithms and manually-annotated data we demonstrate that the network of political retweets exhibits a highly segregated partisan structure, with extremely limited connectivity between left- and right-leaning users. Surprisingly this is not the case for the user-to-user mention network, which is dominated by a single politically heterogeneous cluster of users in which ideologically-opposed individuals interact at a much higher rate compared to the network of retweets. To explain the distinct topologies of the retweet and mention networks we conjecture that politically motivated individuals provoke interaction by injecting partisan content into information streams whose primary audience consists of ideologically-opposed users. We conclude with statistical evidence in support of this hypothesis.