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.

In this talk, I provide an overview of my research on how to measure exposure to misinformation from political elites, understanding why people share misinformation, and how to combat spread of low-quality content online. First, I talk about a tool (https://misinfoexpose.com/) we develop for calculating Twitter users’ misinformation-exposure scores based on the falsity of statements of the public figures and organizations they choose to follow. I show that misinformation-exposure scores are negatively correlated with the quality of news users share themselves, and positively correlated with estimated conservative ideology. Next, I focus on why people fall for misinformation and describe a hybrid lab-field study in which Twitter users (N=1,901) complete a cognitive survey. I show that people who rely on intuitive gut responses over analytical thinking share lower quality content. I then build on this observation with a Twitter field experiment (N= 5,379) that uses a subtle intervention to nudge people to think about accuracy. I show the intervention significantly improve the quality of the news sources they shared subsequently. Finally, I will talk about a follow-up study where we directly correct Twitter users (N=2000) who shared misinformation by replying to their false tweets by including a link to the fact-checking website. We show that unlike the subtle accuracy nudge, the direct public correction results in users sharing lower quality content. Our experimental design translates directly into an intervention that social media companies could deploy to fight misinformation online.

In this talk, I will present an overview of my PhD thesis, which dealt with modelling and measuring differences in willingness of opinion expression in online public debate. I will elaborate on its empirical part in detail, which examined ideological differences in willingness to discuss with others on Twitter. There, a network-based method was developed with which these differences can be estimated; the method was applied in two case studies that concerned discussions about Saxon politics which attracted country-wide attention. For both events, users retweeting far-right parties and politicians were significantly more active. They also acted more confrontational in the sense that they replied mostly to users from different groups, while the contrary was not the case. Additionally, I will briefly present work in progress concerned with trending topics and news sharing.

Given the central importance of information seeking to human nature – epitomized by the view of humans as informavores – understanding the dynamics of how humans seek information and engage with knowledge is of key significance across disciplines, both in the basic and applied sciences. As one of the web’s primary multilingual knowledge sources with hundreds of millions of readers from across the globe, Wikipedia offers a unique opportunity to observe self-motivated information seeking beyond lab-based studies. In this talk, I will give an overview of our recent work aiming to systematically characterize the navigation of Wikipedia’s readers at scale. For example, I will show differences in the navigation strategies and how navigation is embedded in the larger online ecosystem. I will describe how the results not only empower us to better serve information needs of readers, but also how they provide us with crucial insights towards the strategic goal of knowledge equity in Wikimedia projects.

Recent research argues that digital platforms contribute to the long-lasting erosion of genre boundaries and established cultural classifications in the “heads and habits” of art consumers (DiMaggio 1987). This study draws on large volumes of YouTube data to illustrate how, on the contrary, cultural boundaries are techno-socially reproduced online, shedding light on the understudied role of algorithmic systems in the classification of cultural goods.

Through a ground-up investigation of how both automated recommendations and the taste patterns of 202,509 platform users segment 14,865 music videos, this work provides empirical evidence of the persistent relevance of categorical boundaries in platformized cultural reception. The recommender system used by YouTube largely relates music videos of the same genre, thus reinforcing pre-existing artistic classifications in the digital circulation of culture. The study also documents how most users do not cross categorical borders in their interactions with musical content, and that boundary strength varies across music genres. I discuss how the interplay between the social patterning of taste and the algorithmic filtering of cultural content generates techno-social feedback loops, and conclude by drawing broad implications for the sociological study of culture and consumption.

In this interdisciplinary seminar, I will revisit some of my previous works focusing on the historical analysis of various socio-technical and/or socio-cultural systems: the evolution of online diasporic communities, the phylogeny of research fields, the evolution of groups of scholars, the multi-scale and dynamic representation of networks of dead websites, the analysis of online debates on social media, etc. I will thus try to underline a general framework for using digital traces in a social sciences perspective by questioning the evidential power of such data. How can we get back to the root of the activities or events for which these traces provide evidence? What methods, tools and approaches should be used? This seminar is aimed at all researchers wishing to integrate digital traces into their research from a qualitative, quantitative and historical perspective.