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Intro -- Acknowledgements -- Contents -- Editor and Contributors -- 1 Introduction -- 1.1 Entering the 'Third World': A Glance Backwards -- 1.1.1 The Limits of Qualitative Analysis -- 1.1.2 The Subject of Semiotic Analysis -- 1.2 Quantitative Research Strategies in Semiotic Analysis -- 1.2.1 Empirical Validation of Qualitative Analyses -- 1.2.2 Mathematical and Experimental Validation of Models and Hypotheses -- 1.2.2.1 Jean Petitot and the Naturalization of Meaning -- 1.2.2.2 Alessandro Sarti and the Neurological Validity of Semiotic Models -- 1.2.3 Instrumented Analysis of Texts and Corpora -- 1.2.3.1 FranÃois Rastier's Definition of an Autonomous Empirical Dimension Proper to Semiotics -- 1.2.3.2 Franco Moretti's Distant Reading -- 1.2.3.3 Lev Manovich's Cultural Analytics -- 1.3 Quantifying Meaningfulness -- 1.3.1 Beyond Immanence: Mathematical and Experimental Validation -- 1.3.2 Within Immanence: Instrumented Analysis of Texts and Corpora -- References -- Beyond Immanence—Mathematical and Experimental Research -- 2 The Formalization of Elementary Semiotic Structures -- 2.1 Introduction -- 2.2 Elementary Structures as 'Intelligent Equations' -- 2.2.1 Conflicts and Bifurcations -- 2.2.2 Elements and Interactions -- 2.2.3 Universal Unfoldings: Towards a Solution to the Problem of Solutions -- 2.3 Elementary Structures as Schemas -- 2.3.1 Semiotic Square and the Hysteresis Cycle -- 2.3.2 The Paths in the External Space as 'Solutions' -- 2.3.3 Making the Schema More Complex -- 2.3.4 Variants and Homotopy Classes -- 2.4 Semantic Paradigms and Actantial Syntax -- 2.4.1 Externalization and Internalization: Slow and Fast Systems -- 2.4.2 The Integrated Semantic–Syntactic Schemas and the Problem of Conversion -- 2.5 Claude Lévi-Strauss' Canonical Formula of Myth -- 2.6 Further Developments and Openings -- Bibliography

National audience ; Qualitative methods allow us to work on stories, while quantitative approaches often cannot. But can we work on a story with quali-quantitative instruments? Can we find a story in a corpus just by the use of diagrams, without reading any of its individual documents? We will analyze one series of events that occurred in France in 2012: the accident that took place at the Fessenheim reactor and the subsequent political decision to close the plant. The data have been gathered from the OT-Media database. We will make use of textual statistics and narrative models to understand the constitution and evolution of this series of events. Our aim is to show how the use of textual statistics permits to realize narrative analyses on multimedia corpora.

National audience ; Qualitative methods allow us to work on stories, while quantitative approaches often cannot. But can we work on a story with quali-quantitative instruments? Can we find a story in a corpus just by the use of diagrams, without reading any of its individual documents? We will analyze one series of events that occurred in France in 2012: the accident that took place at the Fessenheim reactor and the subsequent political decision to close the plant. The data have been gathered from the OT-Media database. We will make use of textual statistics and narrative models to understand the constitution and evolution of this series of events. Our aim is to show how the use of textual statistics permits to realize narrative analyses on multimedia corpora.

International audience ; In semiotics, the concept of model reader is used to describe the felicity conditions of a text, that is, the information and pragmatic competence needed to interpret the text with reference to a hypothesis on its producer's intention. The model reader permits to formulate inferences about the implicit content of sentences and of the entire text. In this paper we propose to formalize the model reader as a function that takes as inputs a text and a larger context and produces as output what is needed to complete the text's implicit information, filling up its "blank spaces". One possible technique to implement this function is word embedding. We performed an experiment in this sense, using the data collected and analyzed by the Tracking Exposed group (TREX) during the Italian 2018 elections. For their study, six blank Facebook profiles were created, each characterized by a political orientation: all profiles followed the same common set of 30 pages, representative of the entire Italian political spectrum at the time, but each profile interacted only with content linked to its distinctive political orientation. TREX's study of the profiles' newsfeeds demonstrated that each profile was prompted with an uneven distribution of information sources, biased by its political orientation. For our study, we created six different word spaces, one for each profile. Then we identified a certain number of politically neutral terms and observed the semantic associations of these terms in each word space. To identify the terms, we performed a classification of the entire corpus with the software Iramuteq and selected the most significant terms associated with each cluster. Finally, by performing some operations within each word space, we observed some differences in semantic associations that are coherent with the political orientation of the corresponding profile. These results appear to show that word embedding is a valuable approach for computational text pragmatics, as they can help to model the inferences performed by a certain reader. Also, these results suggest the pertinence of such analyses for the study of filter bubbles resulting from algorithmic personalization.

International audience ; In semiotics, the concept of model reader is used to describe the felicity conditions of a text, that is, the information and pragmatic competence needed to interpret the text with reference to a hypothesis on its producer's intention. The model reader permits to formulate inferences about the implicit content of sentences and of the entire text. In this paper we propose to formalize the model reader as a function that takes as inputs a text and a larger context and produces as output what is needed to complete the text's implicit information, filling up its "blank spaces". One possible technique to implement this function is word embedding. We performed an experiment in this sense, using the data collected and analyzed by the Tracking Exposed group (TREX) during the Italian 2018 elections. For their study, six blank Facebook profiles were created, each characterized by a political orientation: all profiles followed the same common set of 30 pages, representative of the entire Italian political spectrum at the time, but each profile interacted only with content linked to its distinctive political orientation. TREX's study of the profiles' newsfeeds demonstrated that each profile was prompted with an uneven distribution of information sources, biased by its political orientation. For our study, we created six different word spaces, one for each profile. Then we identified a certain number of politically neutral terms and observed the semantic associations of these terms in each word space. To identify the terms, we performed a classification of the entire corpus with the software Iramuteq and selected the most significant terms associated with each cluster. Finally, by performing some operations within each word space, we observed some differences in semantic associations that are coherent with the political orientation of the corresponding profile. These results appear to show that word embedding is a valuable approach for computational text pragmatics, as they can help to model the ...

International audience ; In semiotics, the concept of model reader is used to describe the felicity conditions of a text, that is, the information and pragmatic competence needed to interpret the text with reference to a hypothesis on its producer's intention. The model reader permits to formulate inferences about the implicit content of sentences and of the entire text. In this paper we propose to formalize the model reader as a function that takes as inputs a text and a larger context and produces as output what is needed to complete the text's implicit information, filling up its "blank spaces". One possible technique to implement this function is word embedding. We performed an experiment in this sense, using the data collected and analyzed by the Tracking Exposed group (TREX) during the Italian 2018 elections. For their study, six blank Facebook profiles were created, each characterized by a political orientation: all profiles followed the same common set of 30 pages, representative of the entire Italian political spectrum at the time, but each profile interacted only with content linked to its distinctive political orientation. TREX's study of the profiles' newsfeeds demonstrated that each profile was prompted with an uneven distribution of information sources, biased by its political orientation. For our study, we created six different word spaces, one for each profile. Then we identified a certain number of politically neutral terms and observed the semantic associations of these terms in each word space. To identify the terms, we performed a classification of the entire corpus with the software Iramuteq and selected the most significant terms associated with each cluster. Finally, by performing some operations within each word space, we observed some differences in semantic associations that are coherent with the political orientation of the corresponding profile. These results appear to show that word embedding is a valuable approach for computational text pragmatics, as they can help to model the ...

National audience ; Despite the large literature that psychology has provided during the last 30 years about fiscal evasion, so far scarce attention has been given to social communication campaigns on tax compliance. The present study aims to provide a first exploration on the subject, with the objective of better understanding how social communication campaigns might deal with the topic of tax compliance, using as a theoretical background the Slippery Slope Framework. The exploration relies on qualitative methods, and it is based on a semiotic approach in two steps: first, an analysis of the strategies realized in two tv-ads promoted by the Italian Government in 2011 to contrast tax evasion; second, an analysis of the reactions to the tv-ads as expressed by citizens in social media (YouTube). Results discuss two main communicative strategies (ethical and punitive) that can be adopted to approach tax evasion, coherently with the dimensions of the Slippery Slope Framework. ; Riassunto. Nonostante l'ampia letteratura psicologica prodotta negli ultimi 30 anni sull'evasione fiscale, ad oggi il tema delle campagne di comunicazione sulla tax compliance ha ricevuto scarsa attenzione. Questo studio mira a compiere una prima esplorazione sul tema, per meglio comprendere come la comunicazione sociale possa affrontare il tema dell'ottemperanza fiscale, all'interno della cornice teorica offerta dallo slippery slope framework. La ricerca si basa su un approccio semiotico in due fasi: a) un'analisi di due spot televisivi contro l'evasione fiscale realizzate dal Governo italiano nel 2011; b) l'analisi delle reazioni dei cittadini ai due spot riscontrabili su uno dei principali social media (YouTube). I risultati consentono la discussione di due strategie comunicative (etica e punitiva) che, in coerenza con le dimensioni dello slippery slope framework, possono essere adottate nel caso dell'evasione fiscale.

National audience ; Despite the large literature that psychology has provided during the last 30 years about fiscal evasion, so far scarce attention has been given to social communication campaigns on tax compliance. The present study aims to provide a first exploration on the subject, with the objective of better understanding how social communication campaigns might deal with the topic of tax compliance, using as a theoretical background the Slippery Slope Framework. The exploration relies on qualitative methods, and it is based on a semiotic approach in two steps: first, an analysis of the strategies realized in two tv-ads promoted by the Italian Government in 2011 to contrast tax evasion; second, an analysis of the reactions to the tv-ads as expressed by citizens in social media (YouTube). Results discuss two main communicative strategies (ethical and punitive) that can be adopted to approach tax evasion, coherently with the dimensions of the Slippery Slope Framework. ; Riassunto. Nonostante l'ampia letteratura psicologica prodotta negli ultimi 30 anni sull'evasione fiscale, ad oggi il tema delle campagne di comunicazione sulla tax compliance ha ricevuto scarsa attenzione. Questo studio mira a compiere una prima esplorazione sul tema, per meglio comprendere come la comunicazione sociale possa affrontare il tema dell'ottemperanza fiscale, all'interno della cornice teorica offerta dallo slippery slope framework. La ricerca si basa su un approccio semiotico in due fasi: a) un'analisi di due spot televisivi contro l'evasione fiscale realizzate dal Governo italiano nel 2011; b) l'analisi delle reazioni dei cittadini ai due spot riscontrabili su uno dei principali social media (YouTube). I risultati consentono la discussione di due strategie comunicative (etica e punitiva) che, in coerenza con le dimensioni dello slippery slope framework, possono essere adottate nel caso dell'evasione fiscale.