38 posts

Guiding code development: The case of recommender systems

This article by Camille Roth and Jérémie Poiroux has been published in the 11th issue of the Social science research on the Internet (RESET), “Writing code, making software”, in April 2022. Here is the abstract:

Several recent works on recommender algorithms have called for shifting the focus away from the study of their effects, such as the emergence of prediction biases or filter bubbles, to look at how they are designed. We propose here to answer this call thanks to a qualitative study based on interviews with about thirty developers. We show that the conditions of production of these algorithms are very closely linked to their use. Deployed on platforms with a large number of users, thus allowing a permanent observation of their functioning, algorithmic code evolves in a hybrid way that continuously depends on the work of developers and the actions of users. Simply put, the use of algorithmic guidance guides its own evolution – whether it is introducing new variables, new algorithmic processes and, above all, choosing between numerous variants through tests that quantify user reactions in real time in the light of essentially commercial objectives. From this point of view, code development is to a large extent a semi-autonomous evolutionary process in which user testing is the main arbiter: developers introduce mutations, users implicitly produce performance calculations, expressed in standard business terms (audience, sales). By emphasizing the crucial importance of the choice of these metrics, once the choices concerning the architecture of a given platform are made, we call on future research to frame the question of algorithmic policy primarily in terms of the definition of these two dimensions –performance and platform design– rather than opening up further the black box of code and its design.

The open-access online paper is available here (french only).

We launch our new Computational Social Science seminar series!

This monthly event will take place at Centre Marc Bloch in Stadtmitte and will be aimed at scholars in the Berlin/Brandenburg area interested in issues related to computational social science and is also thought as an opportunity to foster a local community in this regard.

For the first session, on Wednesday April 6th, from 5 to 6pm, to be followed by a buffet, we will receive Julien Velcin and Gaël Poux-Médard on “Different ways for modeling time with textual data” (see details below).
The program for this year will be finalized soon. If you are interested in hearing about upcoming sessions, you can subscribe to our mailing list by sending an email to You will receive a first email from the mailing list, which you will need to confirm to finalise the subscription process.

Seminar series in Computational Social Science – Centre Marc Bloch Berlin

Session 1: Wed April 6th, 5-6pm, Centre Marc Bloch, Friedrichstr 191 Berlin, (U Stadtmitte), Georg-Simmel Room, 3rd floor.
Different ways for modeling time with textual data
Julien Velcin and Gaël Poux-Médard (Lyon)

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.

About the speakers
Julien VELCIN (@jvelcin)
Julien Velcin is Professor of Computer Science at the University Lumière Lyon 2 (France). He works at the ERIC Lab in the Data Mining & Decision team on topics related to artificial intelligence, machine learning and natural language processing. More precisely, his research aims at designing new models and algorithms to deal with information networks. One of his favorite application field is the analysis of topics and opinion that flow through the social media.

Gaël Poux-Médard obtained a bachelor in physics at the University Lyon 1 (France), and two M.Sc degrees in “Physics of complex systems” and in “Digital Humanities” at the Ecole Normale Supérieure (ENS) of Lyon in 2019. He worked as intern at the Università Rovira i Virgilli (Tarragona, Spain) and at the CNR-ISC (Rome, Italy). Then, he started a PhD in Computer Science on “interactions in information spread” at the University Lyon 2.

Welcome to Victor Chareyron

Victor is student at Ecole Normale Supérieure Paris-Saclay. He started his academic curriculum studying economic and sociology, with such privileged topics as sociology of personal development, evolutions of the French university system, and sociology of YouTube’s creators. He is currently interested in the opportunities offered by computational methods and Machine Learning in sociology. He just started an internship within the RECORDS project with a focus on building visual representations to account for music consumption.

Victor Chareyron

Quoting is not Citing: Disentangling Affiliation and Interaction on Twitter

This article by Camille Roth, Jonathan Saint-Onge and Katrin Herms has been presented at Complex Networks 2021: the 10th International Conference on Complex Networks and their Applications in December 2021. Here is a brief summary:

On the whole, the paper significantly nuances the traditional “echo chamber” narrative by focusing on Twitter quote trees. By differentiating affiliation from interaction links, which are both concurrently observable, this contibution describes a variety of cross-cutting patterns and roles. The issue of online echo chambers is broadly related to socio-semantic assortativity (or fragmentation): social network clusters exhibit semantic similarity, and this homophily is also typically higher with affiliation than interaction links – both are old results. However, focusing on quote trees on Twitter makes it possible to contrast both link types (namely, quotes and retweets) on short-term and meso-scale events, rather than at the usual and often aggregate level of either links or clusters.

While the political valence (here Ideal Points) of retweeters generally reflects that of root tweet authors (i.e., a “baseline” audience), quotes attract a more central audience: reframing is also recentering, especially for large trees. The less politically central a root is, and the larger and non-central its audience (tree), the more quotes come from a diverse and, on average, central public. This back-and-forth movement persists in secondary quotes, albeit in an attenuated and non-monotonous manner. This finding is nuanced when focusing on user attitudes: while some users (especially non-central ones) quote root tweets of a distinct valence as the ones they normally retweet, some users do not, reminiscing a behavior more akin to echo chambers.

An open-access post-print is available here.

Tracing Affordance and Item Adoption on Music Streaming Platforms

This article by Dougal Shakespeare and Camille Roth has been presented at ISMIR’21: the 22nd International Society for Music Information Retrieval Conference in November 2021.

Following an active sample of Deezer users over a 2-year observation period, this study examines how music streaming platform users adopt on two fronts: (1) adopting affordances – we distinguish organic (O), algorithmic recommendation (A) and editorial curation (E); (2) items therein. By item adoption, we mean the transfer of items across affordances. For instance, a user who has been algorithmically recommended an item now listens to this item organically, that is, autonomously.
By assuming & confirming the diversity of user behaviour this work traces the interconnected, surprisingly sequential factors which drive affordance and item adoption. For one, the way users consume content during the day varies with respect to affordance adoption practices. It also find strong connections between affordance and item adoptions – for instance, users who favour an affordance display lower item adoption rates but on contrary, this makes a greater impact to their overall O catalogue.

The results paint a complex picture of user platform behaviour whereby time-of-day preference mediates low-level platform behaviour (activity levels) while affordance adoption preference mediates the ultimate higher-level organic user decision to adopt music into one’s organic catalogs. Coming full circle, the heterogeneity of item adoption and its impact brings into question the nature of what constitutes an organic stream – after taking into consideration the role of adoption, users are indeed found to be markedly less organic (and more algorithmic and editorial) than was initially thought. This in turn may redefine what adoption really is. This may be of significance to the emerging branch of literature seeking to appraise algorithmic impact relative to an organic reference.

An open-access post-print is available here.

Welcome to Myriam Boualami

Myriam Boualami just joined the team for three years under the joint supervision of Denis Eckert (within the PARIS team at Géographie-Cités) and Camille Roth (within the RECORDS project). A Master’s degree graduate in Geography – Spatial Analysis – Epistemology of Social Sciences, Myriam Boualami she will pursue her PhD research on the digitalization of the music industry, through a lens that focuses on the audiences’ behaviors. She works with geolocated digital footprints, and is interested in their uses and limits in the field of human geography.

Myriam Boualami

Postdoctoral Fellowship in Data Visualization for Computational Social Science

ERC Consolidator “Socsemics”, modeling of socio-semantic systems

We are opening one postdoctoral fellowship (2 years) in data visualization for computational social science in the context of the ERC Consolidator project “Socsemics”, led by Camille Roth. The appointee will further be associated with the UCLAB, a visualization research group at the University of Applied Sciences Potsdam. Co-directed by research professor Marian Dörk, the interdisciplinary team has backgrounds in interface design, computer science, and digital humanities. Applicants will be responsible for the design, implementation, and evaluation of novel data visualizations for scholarly use. The focus lies on the creation of new kinds of instruments for the study of socio-semantic dynamics in online communities. Further information may be found in this document.

Socsemics logo

Welcome to Lena Mangold

Lena Mangold just joined the team for three years under the supervision of Camille Roth and within the socsemics ERC project to pursue a PhD project in computational social sciences. Her work will focus on describing the nature of such clusters, the dynamics that lead to their emergence and stability, as well as their meta-level configurations.

Lena Mangold

Follow the guides: disentangling human and algorithmic curation in online music consumption

This article by Quentin Villermet, Jérémie Poiroux, Manuel Moussallam, Thomas Louail and Camille Roth has been selected at RecSys ’21: Fifteenth ACM Conference on Recommender Systems in September 2021 where it received the best paper runner-up award.

It focuses on user consumption diversity in online music streaming, showing that there is no blanket answer to the question of the effect of recommendation : it applies differently to different user types. The paper thus speaks of “filter niches” rather than “filter bubbles”: the influence of recommendation depends on users, their behavior and plausibly their expectations toward algorithmic guidance. This idea could shed light on recent findings and debates whereby algorithmic recommendation is sometimes found to be “bubbly”, sometimes not. Whether recommendation expands or not users’ horizon depends on their appetency for such or such affordance – on their filter niche. Also, algorithmic recommendation is generally compared against organic behavior. Yet human recommendation is another comparison point (e.g., platform playlists and radio programs) which this paper uses, thus proposing a trichotomy: organic, algorithmic and editorial access to content.

The publisher version is here, an open-access post-print is available here.

Welcome to Adèle Derosereuil

After two years of preparatory literary classes for the Grandes écoles (ENS), Adèle had obtained a master’s degree in sociology and political sciences from the University of Paris-Dauphine. She just joined as a trainee in both administration and research fields for six months at the Centre Marc Bloch. In the research part of her internship, she focuses on developing face to face interviews with Twitter users, thanks to the support of Katrin Herms and Camille Roth.

Adele Derosereuil

Appraising discrepancies and similarities in semantic networks using concept‑centered subnetworks

This article by Darkhan Medeuov, Camille Roth, Kseniia Puzyreva and Nikita Basov has been published in September 2021 in Applied Network Science – an open-access version may be found here.

It proposes an approach to compare semantic networks using concept-centered sub-networks. A concept-centered sub-network is defined as an induced network whose vertex set consists of the given concept (ego) and all its adjacent concepts (alters) and whose link set consists of all the links between the ego and alters (including alter-alter links). Vertex and link overlap indices of concept-centered networks make it possible to infer the similarity of semantic associations around a given concept for distinct actors. The results are further cross-evaluated by close reading textual contexts from which networks are derived, especially using written and interview texts from an ethnographic study of flood management practice in England.

Computational appraisal of gender representativeness in popular movies

This article by Antoine Mazieres, Telmo Menezes and Camille Roth has just been published in Humanities and Social Sciences Communications. It explores the possibility of using artificial intelligence and machine learning algorithms to assess gender representativeness in popular movies. It focuses on a very simple task: Counting faces of women and men appearing in more than 3500 popular movies spanning over 3 decades. On average, over the whole dataset, only 34.52% of faces displayed in a movie are detected as female. Also, we observed a significant increase of the number of women faces. From 1985 to 1998, this ratio is of 27% and reaches a point closer to a female-male parity in the most recent period, from 2014 to 2019, with a ratio of 44.9%. Also, the diversity of situations (formally, the variance of this ratio) increases. That means that films produced recently tend to delve into a more diverse range of on-screen women-men shares.

The open-access article may be found here, accompanied by a vulgarized version in several languages.

Bureaucratic Representation and the Rejection Hypothesis

The article “Bureaucratic Representation and the Rejection Hypothesis: A Longitudinal Study of the European Commission’s Staff Composition (1980–2013)” written by Magali Gravier and Camille Roth and published in January 2020 in the Journal of Public Administration Research and Theory has been selected as joint runner-up for the Riccucci-O’Leary Award 2021 by the Public management research association (PMRA).

The paper analyzes the evolution of the staff composition of the European Commission from 1980 to 2013 using the theory of representative bureaucracy. It first demonstrates how the Commission formulates guidelines which aim at offering fair levels of representation to each member state. However, comparing recruitment targets and actual staffing figures reveals very heterogeneous staff levels. Some member states enjoy unexpectedly high levels of representation whereas others present very low levels. The latter are particularly intriguing and open the door to the formulation of a “rejection hypothesis.” This hypothesis challenges one of the foundations of the theory of representative bureaucracy and leads us to suggest that the theory be enhanced in order to take into account its context of implementation in terms of consolidated or contested statehood, which in turn may explain the phenomena of rejected offers of bureaucratic representation.

The article may be found here.

Welcome to Govind Gandhi

Govind Gandhi just joined the team and will be working as an intern under the supervision of Camille Roth. With a background in physics, network theory and AI, Govind will focus his work on extending a framework to describe the evolution of socio-semantic networks over time, using local rules.

Govind Gandhi

Welcome to Titouan Morvan

Titouan is a mathematics student specializing in statistics and machine learning. As a research engineer, he joins the team for six months and will work under the supervision of Camille Roth within the socsemics ERC project on semantic hypergraphs with applications to debates on climate and energy policies.

Titouan Morvan

Welcome to Manuel Tonneau

Manuel Tonneau just joined the team for six months and will be working as a research engineer within the socsemics ERC project under the supervision of Camille Roth. With a background in economics and statistics, Manuel acquired a machine learning skillset in startup research teams (James, Creatext) and applied these techniques in a social science context at international research institutions (OECD, World Bank). At CMB, Manuel will combine stance detection methods and network analysis in an empirical study of echo chambers on Twitter.

Manuel Tonneau

One 3-year doctoral fellowship in computer science or related field

ERC Consolidator “Socsemics”, modeling of socio-semantic systems

In addition to one three-year post-doctoral fellowship, the team now opens one new three-year doctoral fellowship in computer science for the development of graph-theoretic and dynamic models of the emergence and stability of socio-semantic clusters in online communities, or on breakthroughs in automated content analysis by aiming at going beyond classical distributional approaches to render the linguistic complexity of utterances in web corpuses. Further information may be found in this document.

Socsemics logo

Centre Marc Bloch: open post-doctoral position! D/L February 28, 2021

Centre Marc Bloch e.V. is opening a position for a postdoctoral researcher who focus on economic economic and sociological impacts of ICT-related industries. The contract would ideally start between April 1st and October 1st, 2021, and may cover a maximal period of three years.

Individual research topics could cover the whole field, but a project in one of the four following research directions would be particularly welcome:

  • ICT-mediated job markets
  • Local markets
  • Work automation and algorithmic management
  • Emergence of consumers-producers

You may find the detailed Call for Application here.

Welcome to Romain Avouac

Romain Avouac just joined the team for a year under the supervision of Camille Roth and within the socsemics ERC project as a research assistant. He will focus on developing NLP approaches that go beyond classical distributional approaches in order to better assess the semantic similarity between actors’ online positions. Romain works as a trainee public statistician at the French statistical institute (INSEE).

Romain Avouac

Welcome to Dougal Shakespeare

Dougal Shakespeare just joined the team for three years under the supervision of Camille Roth and within the ANR RECORDS project to purse a PHD in computational social science. He will research the role of algorithmic guidance on music streaming platforms. His work focuses on exploring differentiations between algorithmic and organic music consumption behaviours, tracing the degree to which commonly deployed filtering algorithms may expand or rather, constrain the diversity of one’s music preference.

Dougal Shakespeare

Welcome to Jonathan St-Onge

A former philosopher of science and cognitive scientist, Jonathan St-Onge just joined the team for three years under the supervision of Camille Roth and within the socsemics ERC project to purse a PHD in computational social science. He mixes and matches probabilistic network models with different semantic representations to better understand how the nested hierarchy of both social and semantic structures come together in digital niches as socio-semantic bubbles. At a metalevel, he is greatly interested by how and why scientists study models of the things rather than the things themselves.

Jonathan St-Onge

Team presentations at Sunbelt 2020

The Sunbelt virtual conference, INSNA’s flagship international conference on social network analysis, took place online during July 13-17, 2020 and where the team presented two communications and one poster.

Telmo Menezes introduced his work with Camille Roth about a natural language representation model called “semantic hypergraphs” which enables the extraction of information from free text, including for instance identification of claims, conflicts, and beliefs of actors. Nikita Basov and Camille Roth presented their tribute to John Mohr based on their article published in Poetics as “The Socio-Semantic Space of John Mohr”, addressing the visualization of sizable hybrid socio-semantic networks of co-authors and concepts surrounding a given scholar. Finally, Jonas Stein, Jérémie Poiroux and Camille Roth presented a poster largely based on Jonas’s masters internship work about the intersection of user’s structural and semantic confinement on Twitter.

Welcome to Katrin Herms

Katrin Herms just joined the team for three and a half years under the supervision of Camille Roth and within the socsemics ERC project to pursue an interdisciplinary PhD project in sociology linking social network analysis of polarized internet communities with face-to-face Interviews. Linked with her practical background in journalism, her main interests are discourse analysis and social dynamics emerging around political issues in France and Germany.

Katrin Herms

Large-scale diversity estimation through surname origin inference

In 2018, Antoine Mazières and Camille Roth published in Bulletin of Sociological Methodology the article “Large-Scale Diversity Estimation Through Surname Origin Inference”. Recently, Antoine wrote an informal debriefing (in french) of the study, which gives us the chance to make a post on this site.

The abstract of the article is as follow:
The study of surnames as both linguistic and geographical markers of the past has proven valuable in several research fields spanning from biology and genetics to demography and social mobility. This article builds upon the existing literature to conceive and develop a surname origin classifier based on a data-driven typology. This enables us to explore a methodology to describe large-scale estimates of the relative diversity of social groups, especially when such data is scarcely available. We subsequently analyze the representativeness of surname origins for 15 socio-professional groups in France.

Welcome to Jonas Stein

Jonas Stein just joined the team for a four-month research internship on user confinement in Twitter networks under the supervision of Camille Roth and Jérémie Poiroux and within the project “SOCSEMICS”. Jonas has a background in agent-based simulation and social network analysis.
More information about him may be found on his LinkedIn profile.

Jonas Stein

Tubes and Bubbles – Topological confinement of recommendations on YouTube

The paper “Tubes and Bubbles – Topological confinement of recommendations on YouTube” by Camille Roth, Antoine Mazières and Telmo Menezes just got published in PLOS ONE.

Contrarily to popular belief about so-called “filter bubbles”, several recent studies show that recommendation algorithms generally do not contribute much, if at all, to user confinement; in some cases, they even seem to increase serendipity [see e.g., 1, 2, 3, 4, 5, 6].
Our study demonstrates however that this may not be the case on YouTube: be it in topological, topical or temporal terms, we show that the landscape defined by non-personalized YouTube recommendations is generally likely to confine users in homogeneous clusters of videos. Besides, content for which confinement appears to be most significant also happens to garner the highest audience and thus plausibly viewing time.

The paper is available as an open-access article. We also set up a small vulgarization website, and you may read the CNRS Press release.

Project RECORDS: open doctoral positions! D/L May 31, 2020

We are opening two doctoral fellowships from September 2020 in the framework of the ANR-funded project RECORDS that is focused on the understanding of practices and dynamics surrounding music streaming platforms.

One fellowship will be based at Géographie-cités in Paris, about the spatial dynamics underlying content consumption on streaming platforms, with music streaming as a primary case study.

The other fellowship will be based at Centre Marc Bloch in Berlin, and will address the large-scale and longitudinal study of algorithmic guidance in the context of music streaming platforms.

Please find the detailed call for application here.

New ANR-funded grant called “RECORDS”

The team hosts a new ANR-funded grant called “RECORDS” (2020-2023), focused on the understanding of practices surrounding online content platforms, and specifically in the context of musical streaming through a unique partnership with one of the major platforms in this area, Deezer.

The project generally aims at documenting the diversity of practices and behaviors on streaming platforms, understanding the effects of manual and algorithmic content recommendation, and describing the potential spatial diffusion of artists and works. RECORDS articulates quantitative and qualitative empirical protocols, by relying both on a unique source of usage data stemming directly from the platform (comprehensive listening histories on millions of users on several years) and on a large-scale survey (featuring tens of thousands of respondents) and associated interviews with a selection of consenting participants.

The project gathers about 25 researchers of diverse backgrounds including sociology, computer science and geography. It is being supervised byThomas Louail (Géographie Cités), Philippe Coulangeon (Observatoire Sociologique du Changement), Camille Roth (Centre Marc Bloch), Jean-Samuel Beuscart (Orange Labs SENSE) and Manuel Moussallam (Deezer R&D).

The kick-off meeting will take place on two days in June 2020 at the Centre de Colloques of Campus Condorcet in Aubervilliers.

Welcome to Quentin Villermet

Quentin Villermet just joined the team for a five-month MSc research internship on music recommendation algorithms and their impact on listening practices under the supervision of Camille Roth and Jérémie Poiroux and within the new ANR project “RECORDS”. Quentin has a background in artificial intelligence and his interests include bio-inspired AI, statistics and network infrastructure. More information about him may be found on his LinkedIn profile.

Sunbelt 2020 & NetGLoW 2020 : call for abstracts / socio-semantic session

Sunbelt is a the main venue for social network analysis and its 40th edition will take place in Paris in June 2020. As member of the scientific organizing committee, Camille Roth is pleased to announce the call for abstracts, oral presentations and posters. Proposals should be submitted by January 31, 2020 through this link.

Of particular interest to the team is the session “Advances in Socio-Semantic Network Analysis”, led by Iina Hellstein and co-organized by Nikita Basov, Johanne Saint-Charles, Adina Nerghes and Camille Roth. We particularly encourage submissions for this session, whose description follows.

A related session will also also take place during the conference Networks in the Global World (St. Petersburg, July 7-9, 2020): the team further encourages submissions to the “Semantic and Socio-Semantic Networks” session by February 10, 2020.

Sunbelt 2020 session on “Advances in Socio-Semantic Network Analysis”
Social actors (stakeholders, group members, organizations) are linked (or separated) both by their social ties, and the content (knowledge, beliefs, frames, claims) they share (or do not share) in their communication. This interplay between the social relationships and the content of communication is increasingly approached as a socio-semantic network intertwining social and cultural, or cognitive and relational realms, where meanings and interactions coevolve.

This organized session addresses the recent advances in socio-semantic network analysis, and invites theoretical, methodological and empirical papers contributing (but not limited) to the following themes: (1) Theorizing relationships between social structure and meaning structure; (2) Qualitative, quantitative or mixed methods to relate meaning and social relationships; (3) Multivariate socio-semantic networks; (4) Relations between semantic similarity and social ties; (5) Combining relations between stakeholders and their frames; (6) Connecting macro- and micro-level social and semantic network patterns.

Automatic Discovery of Families of Network Generative Processes

Telmo Menezes had the opportunity to present his last paper with Camille Roth entitled “Automatic Discovery of Families of Network Generative Processes” and published earlier this year [SpringerLink] [arXiv], during an oral session at the Complex Networks 2019 conference in Lisbon.

This work relies on a machine learning approach introduced by them some years ago for automatically discovering plausible and human-understandable generators that fit and help explain observable complex networks. Recently, they expanded this work to identify families of generators, and demonstrated its application in discovering a small number of such families within a large corpus of facebook ego networks. The abstract of the presentation in Lisbon offers a brief overview and can be found here (p. 225), the presentation is here.

Quali-Quantitative meeting – December 2019

The Computational Social Science Team organizes bimonthly internal meetings aimed at discussing “quali-quantitative” approaches. The point of these meetings is to present the work-in-progress carried out within the Pole’s framework and also to offer methodological workshops for training in digital approach (database generation, corpus construction, processing, and so on.). It is thus a forum for dialogue capable of generating new qualitative-quantitative research questions within the Centre Marc Bloch. Please note it will progressively transform into a computational social science seminar open to an outside audience.

In December 2019, we were pleased to listen to:

  • Mirjam Dageförde who presented her statistical work (with Emiliano Grossman) about “Selfish, not social! How voters derive their policy preferences”
  • Jérémie Poiroux about filter bubbles that Twitter users possibly contribute to build. This work was part of the Algodiv project and will be continued with Camille Roth. The presentation can be found here (in French).

Interactional and Informational Attention on Twitter

Our paper called “Interactional and Informational Attention on Twitter”, by Agathe Baltzer, Marton Karsai and Camille Roth, just got out in Information 10(8), and is featured on its cover page. This work appraises the distribution of attention at the collective and individual level on Twitter, and both from a social (users) and semantic (topics) viewpoint. We exhibit the existence of socio-semantic attentional constraints and focus effects.

Neurons spike back

This article by Dominique Cardon, Jean-Philippe Cointet and Antoine Mazières retraces the history of artificial intelligence through the lens of the tension between symbolic and connectionist approaches. From a social history of science and technology perspective, it seeks to highlight how researchers, relying on the availability of massive data and the multiplication of computing power have undertaken to reformulate the symbolic AI project by reviving the spirit of adaptive and inductive machines dating back from the era of cybernetics.
The full english version may be accessed here.

Open doctoral and post-doctoral positions ! D/L: Sept 30, 2019

The team is now opening several doctoral students and post-doctoral researchers to work under the ERC Consolidator grant Socsemics, focusing on internet echo chambers and polarization. These offers take place in an interdisciplinary context and touch a variety of domains: computational social science, political science, NLP, information visualization, sociology of the internet, social network analysis, complex network modeling, essentially.

Detailed job offers may be found here with a deadline for application set at September 30th, 2019.

Please check the team presentation video and the “Socsemics” ERC project website

Extensive information on the scientific content and context are available in the above-mentioned job offers – interested applicants may nonetheless feel free to contact Camille Roth (roth[@] to discuss this further.

Appraising algorithmic biases

“Algorithmic Distortion of Informational Landscapes”, by Camille Roth, has just been published in Intellectica 70(1):97-118 –
This review paper focuses on biases induced by recommendation algorithms. It explores the state of the art along a double dichotomy: first regarding the discrepancy between users’ intentions and actions (1) under some algorithmic influence and (2) without it; second, by distinguishes algorithmic biases on (1) prior information rearrangement and (2) posterior information arrangement.
An open-access pre-print may be downloaded here.