Camille Roth: Semantic graphs and social networks

Affiliation: French National Centre for Scientific Research, Centre Marc Bloch

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The social distribution of information and the structure of social interactions are more and more frequently studied together, especially in fields related to computational social sciences. On the one hand, content analysis, variously called “text mining”, “automated text analysis” or “text-as-data methods”, relies on a wide range of techniques from simple numerical statistics (textual similarity, salient terms) to machine learning approaches applied at the level of sets of words or sentences, in particular to extract various types of semantic graphs – whether they are simple co-occurrence links between terms, “subject-predicate-object” triples, or more elaborate structures at the level of an entire sentence. These data and, sometimes, these semantic graphs, are also associated with actors whose various relations (interaction, collaboration, affiliation) are also frequently gathered in social graphs. This presentation aims at proposing an overview of approaches mixing contents and interactions, where digital public spaces and scientific communities represent frequent empirical grounds, being social systems where information and knowledge are produced and propagated in a decentralized way.


Henrik Müller: What’s in a story? How narratives structure the way we think about the economy

Affiliation: TU Dortmund University

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Telling narratives is the mode in which humans make sense of an otherwise incomprehensibly complex world. Societies run on a set of narratives that serve as short-hand descriptions of the state of the nation. These stories shape expectations and drive economic and policy decision. Journalism is a key player in shaping economic narratives. Furthermore, it adds an additional approach to economists’ reasoning: narratives can be valuable complements to the statistics-focused approach pursued by economists, particularly in times of substantial structural change, when high levels of uncertainty prevail. What’s more, modern text mining approaches lend themselves to detecting and quantifying the salience of narratives.


Nina Gierasimczuk: tba

Affiliation: Technical University of Denmark

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