CSS Seminar

Textual corpora are usually not static over time: as new documents get published (e.g., news, scientific articles, tweets), topics of interest may change. Describing their rise and fall over time has generated substantial research over the last decade. Better, it turns out that considering the temporal dimension of textual modeling improves the automated description of these corpora.
Over the years, researchers of the ERIC lab have developed several models to explore this paradigm. Early works simply run static models on different time slices. More elaborate approaches consider that models estimated on each time slice are not independent from each other. Even more elaborate approaches go further, get rid of time discretization and model time as a continuous variable along with textual content.
Our talk will present an overview of these approaches, also illustrating our lab’s recent progresses in this regard – especially in terms of tracking topics over time or for studying how pieces of information interact to trigger new information on social media.

To openly express one’s views is a fundamental right in any liberal democracy. However, in the age of social media, the questions of what is allowed to say and how public discourse should be regulated are ever more contested. We present a pre-registered study to analyze citizens’ preferences for online hate speech regulation. We devise synthetic hate speech posts mimicking actual cases of hate speech. Respondents are asked to judge the posts with regards to perceived offensiveness and hatefulness as well as to actions that should be taken by the platform providers and other consequences the sender of hate speech should face. The experiment is embedded in nationally representative online panels in the US and Germany, which allows us to analyze context- as well as individual-level determinants for online hate speech regulation preferences.

In 1751, Jean le Rond d’Alembert had a dream: “to make a genealogical or encyclopedic tree which will gather the various branches of knowledge together under a single point of view and will serve to indicate their origin and their relationships to one another”. Three hundreds years later, the digitalization of human activities and its outputs has made text-strings a universal medium for addressing knowledge production in domains as diverse as academia, biomedical research or online citizen debates. We will develop the notion of “branches of knowledge” envisionned by d’Alembert and propose a reconstruction method that makes it possible to reconstruct, from unstructured textual materials, a multi-level and multi-scale representation of these branches of knowledge. We will then investigate the new relationships that this type of reconstruction allows to establish with collective phenomena by using several applications of this method such as scientific publications, Twitter data or clinical trials.

In recent decades, the effect of social networks on culture has been both confirmed and questioned. Our paper argues this puzzling contradiction to result from theoretical and methodological gaps between different accounts of networks and culture—and offers a bridge across these gaps. On the theoretical side, we propose an integral model of symbolic construction that combines the previously separately studied ego and dyadic social network effects on culture across two major social network domains: instrumental and expressive. Namely, we argue that formation of group’s vocabulary of cultural elements (cultural consensus) is driven by personal instrumental social ties, while relations between the cultural elements constitutive of their meaning (cultural tightness) owe to dyadic expressive social ties. On the methodological side, we open up an opportunity to juxtapose and disentangle the different dimensions of social network effects on culture by introducing multilevel exponential random graph socio-semantic network modeling. This method is capable of estimating the relative contribution of the two social network dimensions to culture within the same model, thus controlling for their interplay. Applying our method to a unique longitudinal multi-source dataset obtained through five two-year-long ethnographic studies of European artistic collectives, we find support for our theoretical model across creative groups embedded in diverse social and cultural contexts. The approach and our findings open up new opportunities for the sociology of culture, social network analysis, symbolic interactionism, studies of creative collectives in arts, science, business, activism, and beyond.

Social connectivity structures and reinforces inequality in society but also provides a footprint of it. I will present two projects in which we analyze network structures to extract information about socioeconomic inequality. In the first project, we use correspondence analysis to infer Twitter users’ socioeconomic status from the accounts of commercial and entertainment brands in the US they follow. In the second project, we analyze SafeGraph data on physical store visits and co-visits in the US to investigate nuances of socioeconomic inequality in daily consumption. The projects demonstrate how we can both exploit and examine traces of economic and cultural consumption practices to understand an important manifestation of inequality in everyday life.

Multivariate networks consist of a vertex set with at least one type of edge between pairs of vertices and with numerical and/or qualitative attributes on the vertices and the edges. These networks provide a more accurate representation of social structure than univariate networks, but analysing them introduce technical and computational complexity. In this presentation, we consider exploratory and confirmatory analysis of multivariate networks represented as multigraphs. Multigraph data structure is described with examples of their natural appearance, together with a description of the possibility to obtain multigraphs using blocking, aggregation and scaling. Two random multigraph models are presented and several statistics under these models are derived. It is shown how these statistics can be used to analyse local and global network properties in order to convey important social phenomena and processes. Applications are used to illustrate the applicability of the presented approach, including when analysing the gendered inequalities in popular cinema by using dialogue networks. Moreover, some examples are provided on how to use the package ‘multigraphr’ in the statistical software R to perform the analysis.

In a rapidly changing world, facing an increasing number of socioeconomic, health and environmental crises, data-driven computational approaches can help us quantify vulnerabilities and monitor progress towards achieving the UN Sustainable Development Goals. In this talk, I will first provide a non-exhaustive overview of the main areas of applications where these approaches have shown their potential for social impact, and I will then deep-dive more specifically into my work on predicting food insecurity from conflict, weather, and economic data.

The use of digital traces can provide a wealth of information about human mobility, which can be used to understand patterns of movement in different contexts. Here I will present two studies on how socio-economic inequalities and gender shape mobility patterns by using mobile phone data. In the first one we will see how mobility is gendered in Santiago, Chile. In the second case, we will look at the socioeconomic drivers of mobility during the first wave of the COVID-19 pandemic in Italy. Finally I will present a study where we quantify the phenomenon of pandemic fatigue through the analysis of mobility indicators publicly available.

There is a public and scholarly debate about whether personalized services of social-media platforms contribute to the rise of bipolarization of political opinions. On the one hand, it is argued that personalized services of online social networks generate filter bubbles limiting contact between users who disagree. This reduces opportunities for assimilative social influence between users from different camps and prevents opinion convergence. On the other hand, empirical research also indicated that exposing users to content from the opposite political spectrum can activate the counter-part of assimilative influence, repulsive influence. Fostering contact that leads to opinion assimilation and limiting contacts likely to induce repulsive interactions, it has been concluded, may therefore prevent bipolarization. With an agent-based model, we demonstrate here that these conclusions fail to capture the complexity that assimilative and repulsive influence generate in social networks. Sometimes, more assimilative influence can actually lead to more and not less opinion bipolarization. Likewise, increasing the exposure of users to like-minded individuals sometimes intensifies opinion polarization. While emerging only in specific parts of the parameter space, these counter-intuitive dynamics are robust, as our simulation experiments demonstrate. We discuss implications for the debate about filter bubbles and approaches to improve the design of online social networks. While we applaud the growing empirical research on the micro-processes of assimilative and repulsive influence in online settings, we warn that drawing conclusions about resulting macro outcomes like opinion bipolarization requires a rigorous analysis capturing the complexity of online communication systems. Intuition alone is error-prone in this context. Accordingly, models capturing the complexity of social influence in networks should play a more important role in the design of communication systems.

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