The Covid-19 Crisis: an NLP Exploration of the French Twitter Feed (February-May 2020)
Speaker: Christophe Benavent, Professor of Management, DRM, Université Paris Dauphine-PSL
Abstract
The Covid-19 pandemic offers a spectacular case of disaster management. In this literature, the paradigm of participation is fundamental: the mitigation of the impact of the disaster, the quality of the preparation and the resilience of the society, which facilitate the reconstruction, depend on the participation of the populations. Being able to observe and measure the state of mental health of the population (anxiety, confidence, expectations, ...) and to identify the points of controversy and the content of the discourse, are necessary to support measures designed to encourage this participation. Social media, and in particular Twitter, offer valuable resources for researching this discourse. The objective of this empirical study is to reconstruct a micro history of users' reactions to the pandemic as they share them on social networks. The general method used comes from new processing techniques derived from Natural Language Processing (NLP). Three analysis methods are used to process the corpus: analysis of the temporal evolution of term occurrences; creation of dynamic semantic maps to identify co-occurrences; analysis of topics using the SVM method. The main empirical result is that the mask emerges as a central figure of discourse, at least in the discourse produced by certain social media. The retrospective analysis of the phenomenon allows us to explain what made the mask a focal point not only in conversation, but also in behaviors. Its value resides less in its functional qualities than in its ability to fix attention and organize living conditions under the threat of pandemic.
Measuring Crime Reporting and Incidence: Method and Application to #MeToo
Speaker: Germain Gauthier, PhD candidate in Economics, CREST, Ecole Polytechnique
Abstract
This paper studies the Me Too movement's effects on sex criminality. As many victims do not report to the police, a long-standing empirical challenge with reported crime statistics is that they reflect variations in victim reporting and crime incidence. To separate both margins, I develop a duration model that studies the delay between the incident's occurrence and its report to the police. The model accounts for unobserved heterogeneity, never-reporters, and double-truncation in the data. I apply it to the police records of large US cities. Contrary to the widespread view that #MeToo was a watershed moment, I find that sex crime reporting had already been increasing for years before its sudden mediatization in October 2017. Nonetheless, the movement had a positive, persistent impact on victim reporting, particularly for juveniles, racial minorities, and victims of misdemeanors and old crime incidents. The increase in reporting translates into drastically higher probabilities of arrest for sex offenders. Using reported non-sexual crimes as a control group, difference-in-differences estimates suggest the movement also had a sizable deterrent effect.