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International audience ; Propaganda is a rhetorical technique to present opinions with the deliberate goal of influencing the opinions and the actions of other (groups of) individuals for predetermined misleading ends. The employment of such manipulation techniques in politics and news articles, as well as its subsequent spread on social networks, may lead to threatening consequences for the society and its more vulnerable members. In this paper, we present PROTECT (PROpaganda Text dEteCTion), a new system to automatically detect propagandist messages and classify them along with the propaganda techniques employed. PROTECT is designed as a full pipeline to firstly detect propaganda text snippets from the input text, and then classify the technique of propaganda, taking advantage of semantic and argumentation features. A video demo of the PROTECT system is also provided to show its main functionalities. ; La propaganda è una tecnica retorica per presentare determinate opinioni con l'obiettivo deliberato di influenzare le opinioni e le azioni di altri (gruppi di) individui per fini predeterminati e tendenzialmente fuorvianti. L'impiego di tale tecnica di manipolazione in politica e nella stampa, così come la sua diffusione sulle reti sociali, può portare a conseguenze disastrose per la società e per i suoi membri più vulnerabili. In questo articolo presentiamo PROTECT (PROpaganda Text dEteCTion), un nuovo sistema per identificare automaticamente i messaggi propagandistici e classificarli rispetto alle tecniche di propaganda utilizzate. PROTECT è un sistema progettato come una pipeline completa per rilevare in primo luogo i frammenti di testo propagandistici dato il testo proposto, e successivamente classificare tali frammenti secondo la tecnica di propaganda usata, sfruttando le caratteristiche semantiche e argomentative del testo. Questo articolo presenta anche un video dimostrativo del sistema PROTECT per mostrare le principali funzionalità fornite all'utente.

International audience ; Propaganda is a rhetorical technique to present opinions with the deliberate goal of influencing the opinions and the actions of other (groups of) individuals for predetermined misleading ends. The employment of such manipulation techniques in politics and news articles, as well as its subsequent spread on social networks, may lead to threatening consequences for the society and its more vulnerable members. In this paper, we present PROTECT (PROpaganda Text dEteCTion), a new system to automatically detect propagandist messages and classify them along with the propaganda techniques employed. PROTECT is designed as a full pipeline to firstly detect propaganda text snippets from the input text, and then classify the technique of propaganda, taking advantage of semantic and argumentation features. A video demo of the PROTECT system is also provided to show its main functionalities. ; La propaganda è una tecnica retorica per presentare determinate opinioni con l'obiettivo deliberato di influenzare le opinioni e le azioni di altri (gruppi di) individui per fini predeterminati e tendenzialmente fuorvianti. L'impiego di tale tecnica di manipolazione in politica e nella stampa, così come la sua diffusione sulle reti sociali, può portare a conseguenze disastrose per la società e per i suoi membri più vulnerabili. In questo articolo presentiamo PROTECT (PROpaganda Text dEteCTion), un nuovo sistema per identificare automaticamente i messaggi propagandistici e classificarli rispetto alle tecniche di propaganda utilizzate. PROTECT è un sistema progettato come una pipeline completa per rilevare in primo luogo i frammenti di testo propagandistici dato il testo proposto, e successivamente classificare tali frammenti secondo la tecnica di propaganda usata, sfruttando le caratteristiche semantiche e argomentative del testo. Questo articolo presenta anche un video dimostrativo del sistema PROTECT per mostrare le principali funzionalità fornite all'utente.

International audience ; Propaganda is a rhetorical technique to present opinions with the deliberate goal of influencing the opinions and the actions of other (groups of) individuals for predetermined misleading ends. The employment of such manipulation techniques in politics and news articles, as well as its subsequent spread on social networks, may lead to threatening consequences for the society and its more vulnerable members. In this paper, we present PROTECT (PROpaganda Text dEteCTion), a new system to automatically detect propagandist messages and classify them along with the propaganda techniques employed. PROTECT is designed as a full pipeline to firstly detect propaganda text snippets from the input text, and then classify the technique of propaganda, taking advantage of semantic and argumentation features. A video demo of the PROTECT system is also provided to show its main functionalities. ; La propaganda è una tecnica retorica per presentare determinate opinioni con l'obiettivo deliberato di influenzare le opinioni e le azioni di altri (gruppi di) individui per fini predeterminati e tendenzialmente fuorvianti. L'impiego di tale tecnica di manipolazione in politica e nella stampa, così come la sua diffusione sulle reti sociali, può portare a conseguenze disastrose per la società e per i suoi membri più vulnerabili. In questo articolo presentiamo PROTECT (PROpaganda Text dEteCTion), un nuovo sistema per identificare automaticamente i messaggi propagandistici e classificarli rispetto alle tecniche di propaganda utilizzate. PROTECT è un sistema progettato come una pipeline completa per rilevare in primo luogo i frammenti di testo propagandistici dato il testo proposto, e successivamente classificare tali frammenti secondo la tecnica di propaganda usata, sfruttando le caratteristiche semantiche e argomentative del testo. Questo articolo presenta anche un video dimostrativo del sistema PROTECT per mostrare le principali funzionalità fornite all'utente.

International audience ; Propaganda is a rhetorical technique to present opinions with the deliberate goal of influencing the opinions and the actions of other (groups of) individuals for predetermined misleading ends. The employment of such manipulation techniques in politics and news articles, as well as its subsequent spread on social networks, may lead to threatening consequences for the society and its more vulnerable members. In this paper, we present PROTECT (PROpaganda Text dEteCTion), a new system to automatically detect propagandist messages and classify them along with the propaganda techniques employed. PROTECT is designed as a full pipeline to firstly detect propaganda text snippets from the input text, and then classify the technique of propaganda, taking advantage of semantic and argumentation features. A video demo of the PROTECT system is also provided to show its main functionalities. ; La propaganda è una tecnica retorica per presentare determinate opinioni con l'obiettivo deliberato di influenzare le opinioni e le azioni di altri (gruppi di) individui per fini predeterminati e tendenzialmente fuorvianti. L'impiego di tale tecnica di manipolazione in politica e nella stampa, così come la sua diffusione sulle reti sociali, può portare a conseguenze disastrose per la società e per i suoi membri più vulnerabili. In questo articolo presentiamo PROTECT (PROpaganda Text dEteCTion), un nuovo sistema per identificare automaticamente i messaggi propagandistici e classificarli rispetto alle tecniche di propaganda utilizzate. PROTECT è un sistema progettato come una pipeline completa per rilevare in primo luogo i frammenti di testo propagandistici dato il testo proposto, e successivamente classificare tali frammenti secondo la tecnica di propaganda usata, sfruttando le caratteristiche semantiche e argomentative del testo. Questo articolo presenta anche un video dimostrativo del sistema PROTECT per mostrare le principali funzionalità fornite all'utente.

International audience ; Political debates offer a rare opportunity for citizens to compare the candidates' positions on the most controversial topics of the campaign. Thus they represent a natural application scenario for Argument Mining. As existing research lacks solid empirical investigation of the typology of argument components in political debates, we fill this gap by proposing an Argument Mining approach to political debates. We address this task in an empirical manner by annotating 39 political debates from the last 50 years of US presidential campaigns, creating a new corpus of 29k argument components, labeled as premises and claims. We then propose two tasks: (1) identifying the argumentative components in such debates, and (2) classifying them as premises and claims. We show that feature-rich SVM learners and Neural Network architectures outperform standard baselines in Argument Mining over such complex data. We release the new corpus USElecDeb60To16 and the accompanying software under free licenses to the research community.

International audience ; Political debates offer a rare opportunity for citizens to compare the candidates' positions on the most controversial topics of the campaign. Thus they represent a natural application scenario for Argument Mining. As existing research lacks solid empirical investigation of the typology of argument components in political debates, we fill this gap by proposing an Argument Mining approach to political debates. We address this task in an empirical manner by annotating 39 political debates from the last 50 years of US presidential campaigns, creating a new corpus of 29k argument components, labeled as premises and claims. We then propose two tasks: (1) identifying the argumentative components in such debates, and (2) classifying them as premises and claims. We show that feature-rich SVM learners and Neural Network architectures outperform standard baselines in Argument Mining over such complex data. We release the new corpus USElecDeb60To16 and the accompanying software under free licenses to the research community.

International audience ; Political debates are the means used by political candidates to put forward and justify their positions in front of the electors with respect to the issues at stake. Argument mining is a novel research area in Artificial Intelligence, aiming at analyzing discourse on the pragmatics level and applying a certain argumentation theory to model and automatically analyze textual data. In this paper, we present DISPUTool, a tool designed to ease the work of historians and social science scholars in analyzing the argumentative content of political speeches. More precisely, DISPUTool allows to explore and automatically identify argumentative components over the 39 political debates from the last 50 years of US presidential campaigns (1960-2016).

International audience ; Political debates offer a rare opportunity for citizens to compare the candidates' positions on the most controversial topics of the campaign. Thus they represent a natural application scenario for Argument Mining. As existing research lacks solid empirical investigation of the typology of argument components in political debates, we fill this gap by proposing an Argument Mining approach to political debates. We address this task in an empirical manner by annotating 39 political debates from the last 50 years of US presidential campaigns, creating a new corpus of 29k argument components, labeled as premises and claims. We then propose two tasks: (1) identifying the argumentative components in such debates, and (2) classifying them as premises and claims. We show that feature-rich SVM learners and Neural Network architectures outperform standard baselines in Argument Mining over such complex data. We release the new corpus USElecDeb60To16 and the accompanying software under free licenses to the research community.

Political debates offer a rare opportunity for citizens to compare the candidates' positions on the most controversial topics of the campaign. Thus they represent a natural application scenario for Argument Mining. As existing research lacks solid empirical investigation of the typology of argument components in political debates, we fill this gap by proposing an Argument Mining approach to political debates. We address this task in an empirical manner by annotating 39 political debates from the last 50 years of US presidential campaigns, creating a new corpus of 29k argument components, labeled as premises and claims. We then propose two tasks: (1) identifying the argumentative components in such debates, and (2) classifying them as premises and claims. We show that feature-rich SVM learners and Neural Network architectures outperform standard baselines in Argument Mining over such complex data. We release the new corpus USElecDeb60To16 and the accompanying software under free licenses to the research community.

International audience ; Political debates are the means used by political candidates to put forward and justify their positions in front of the electors with respect to the issues at stake. Argument mining is a novel research area in Artificial Intelligence, aiming at analyzing discourse on the pragmatics level and applying a certain argumentation theory to model and automatically analyze textual data. In this paper, we present DISPUTool, a tool designed to ease the work of historians and social science scholars in analyzing the argumentative content of political speeches. More precisely, DISPUTool allows to explore and automatically identify argumentative components over the 39 political debates from the last 50 years of US presidential campaigns (1960-2016).

Political debates are the means used by political candidates to put forward and justify their positions in front of the electors with respect to the issues at stake. Argument mining is a novel research area in Artificial Intelligence, aiming at analyzing dis-course on the pragmatics level and applying a certain argumentation theory to model and automatically analyze textual data. In this paper, we present DISPUTool, a tool designed to ease the work of historians and social science scholars in analyzing the argumentative content of political speeches. More precisely, DISPUTool allows to explore and automatically identify argumentative components over the 39 political debates from the last 50 years of US presidential campaigns (1960-2016).

n this paper, we present the annotation guidelines we defined for annotating arguments in political debates. In our guidelines, we consider each argument as being composed of a claim and one or more premises. The annotation process has started with defining the guidelines for three annotators containing examples from the data, and continued as cyclic process of evaluation and revision on the annotation to resolve the ambiguities in the guidelines. In this paper, we briefly discuss the resulting annotated dataset and give some examples of the annotation scheme. The quality of the annotated dataset is assessed by computing inter-annotator agreement using Krippendorf's α coefficient on a portion of the dataset.

This open access book addresses communicative aspects of the current COVID-19 pandemic as well as the epidemic of misinformation from the perspective of argumentation theory. Argumentation theory is uniquely placed to understand and account for the challenges of public reason as expressed through argumentative discourse. The book thus focuses on the extent to which the forms, norms and functions of public argumentation have changed in the face of the COVID-19 pandemic. This question is investigated along the three main research lines of the COST Action project CA 17132: European network for Argumentation and Public PoLicY analysis (APPLY): descriptive, normative, and prescriptive. The volume offers a broad range of contributions which treat argumentative phenomena that are directly related to the changes in public discourse in the wake of the outburst of COVID-19. The volume additionally places particular emphasis on expert argumentation, given (i) the importance expert discourse has had over the last two years, and (ii) the challenges that expert argumentation has faced in the public sphere as a result of scientific uncertainty and widespread misinformation. Contributions are divided into three groups, which (i) examine various features and aspects of public and institutional discourse about the COVID-19 pandemic, (ii) scrutinize the way health policies have been discussed, debated, attacked and defended in the public sphere, and (iii) consider a range of proposals meant to improve the quality of public discourse, and public deliberation in particular, in such a way that concrete proposals for argumentative literacy will be brought to light. Overall, this volume constitutes a timely inquiry into all things argumentative in pandemic discourse. This volume is of interest to a broad readership including philosophers, linguists, communication and legal scholars, and members of the wider public who seek to better understand the discourse surrounding communicative phenomena in times of crisis. COST (European Cooperation in Science and Technology) is a funding organisation for research and innovation networks. For more information: www.cost.eu

International audience ; Emotion analysis in polarized contexts represents a challenge for Natural Language Processing modeling. As a step in the aforementioned direction, we present a methodology to extend the task of Aspect-based Sentiment Analysis (ABSA) toward the affect and emotion representation in polarized settings. In particular, we adopt the three-dimensional model of affect based on Valence, Arousal, and Dominance (VAD). We then present a Brexit scenario that proves how affect varies toward the same aspect when politically polarized stances are presented. Our approach captures aspect-based polarization from newspapers regarding the Brexit scenario of 1.2m entities at sentence-level. We demonstrate how basic constituents of emotions can be mapped to the VAD model, along with their interactions respecting the polarized context in ABSA settings using biased key-concepts (e.g., "stop Brexit" vs. "support Brexit"). Quite intriguingly, the framework achieves to produce coherent aspect evidences of Brexit's stance from key-concepts, showing that VAD influence the support and opposition aspects.

International audience ; Emotion analysis in polarized contexts represents a challenge for Natural Language Processing modeling. As a step in the aforementioned direction, we present a methodology to extend the task of Aspect-based Sentiment Analysis (ABSA) toward the affect and emotion representation in polarized settings. In particular, we adopt the three-dimensional model of affect based on Valence, Arousal, and Dominance (VAD). We then present a Brexit scenario that proves how affect varies toward the same aspect when politically polarized stances are presented. Our approach captures aspect-based polarization from newspapers regarding the Brexit scenario of 1.2m entities at sentence-level. We demonstrate how basic constituents of emotions can be mapped to the VAD model, along with their interactions respecting the polarized context in ABSA settings using biased key-concepts (e.g., "stop Brexit" vs. "support Brexit"). Quite intriguingly, the framework achieves to produce coherent aspect evidences of Brexit's stance from key-concepts, showing that VAD influence the support and opposition aspects.