admin

33 posts

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.

Centre Marc Bloch: open post-doctoral position! D/L May 15, 2020

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 on October 1st, 2020 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.

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).

Algorithmic guidance on cultural good consumption (online/offline)

Computer Science: One possible internship

Context

The effects of recommendation algorithms on the access to information and cultural goods is at the center of a growing debate, which aims at assessing whether they rather contribute to enlarge or to restrain the horizon of users with respect to their “organic” behavior, i.e. absent algorithms.

Goals

This internship topic focuses on the impact of algorithmic guidance on cultural good consumption, specifically musical goods. It aims at addressing the following question: to what extent could we say that traditional musical “recommendation” (radios, music libraries, record stores) is more or less diversified and/or serendipous than algorithmic recommendation (e.g., on YouTube or leading music streaming platforms)? In other words, we aim at appraising the discrepancy between online and offline guidance.

The intern would benefit from significant autonomy in the design and realization of the empirical measures, protocol, and result analysis. Besides, fully anonymized data coming from a leading music streaming platform would be readily available from the beginning of the internship.

Intended audience

We open one internship under the joint supervision of Camille Roth and Jérémie Poiroux. Applicants should ideally be achieving a master’s degree in computer science and related fields (e.g., applied mathematics); modeling and/or online data collection skills are desirable.

Practical Details

  • The internship could last between three and six months;
  • The internship is based in Berlin at the Centre Marc Bloch;
  • The intern should have working proficiency in either English, French or German;
  • The internship allowance is fixed by law at an amount of about 500 euros on a 38 hours basis.
  • Students registered at non-EU universities should also inquire first about the administrative issues related to the possibility of being hosted at the Centre, at a Germany-based institution.

Apply

To apply, please send an e-mail along with your resume to Camille Roth (roth[@]cmb.hu-berlin.de) and Jérémie Poiroux (poiroux[@]cmb.hu-berlin.de).

User confinement in online communities (SNA and info-viz)

Computer Science: Two possible internships

Context

User confinement (or containment) in online communities – variously denoted as, inter alia, balkanization, bubbles, echo chambers, fragmentation – is at the core of a growing number of studies. In the framework of Algodiv and Socsemics, the team contributes to advancing the formalization and the empirical appraisal of the informational and interactional confinement of individuals in web communities. This internship would aim at either appraising or visualizing user confinement on Twitter and its topical subnetworks, building upon exploratory work previously achieved within the team.

Goals

There are two main directions for further research at the moment in this context:

  • First, adopting a graph-theoretic and social network analysis perspective, in order to refine existing measures of structural confinement in topical Twitter subnetworks and then generalize some of the preliminary results, both in a methodological and in an empirical manner (principally by streamlining a robust empirical protocol to assert the distribution and magnitude of confinement);
  • Second, from an information visualization standpoint, by developing an interactive visualization interface that renders the structural-topical confinement of users both at the ego-centered level (local neighborhood of users) and the global level (a whole topical network).

Intended audience

We open up to two internships under the joint supervision of Camille Roth and Jérémie Poiroux.

For the first internship, applicants should ideally be achieving a master’s degree in computer science and related fields (e.g., applied mathematics); prior knowledge of network theory and/or online data collection is desirable.

The second internship targets students in information visualization and information design.

Practical Details

  • The internship could last between three and six months;
  • The internship is based in Berlin at the Centre Marc Bloch;
  • The intern should have working proficiency in either English, French or German;
  • The internship allowance is fixed by law at an amount of about 500 euros on a 38 hours basis.
  • Students registered at non-EU universities should also inquire first about the administrative issues related to the possibility of being hosted at the Centre, at a Germany-based institution.

Apply

To apply, please send an e-mail along with your resume to Camille Roth (roth[@]cmb.hu-berlin.de) and Jérémie Poiroux (poiroux[@]cmb.hu-berlin.de).

Automatic Hypothesis Generation for Network Growth Models

Computer Science: Two possible internships

Context

Networks have become a fundamental abstraction for modeling systems across many scientific fields. Plausible hypothesis describing their growth processes can help us understand a wide range of phenomena, but formulating such hypothesis is often challenging and requires insights that may be counter-intuitive. In the last years, we have developed an approach to automatically discover realistic network growth models from empirical data, employing a machine learning technique inspired by natural selection, and defining a unified formalism to describe such models as a mathematical function of arbitrary complexity [1]. As the proposed method is completely general and does not assume any pre-existing models, it can be applied “out of the box” to any given network. By automating hypothesis generation and validation, this research is aligned with the ambitious idea of creating Artificial Scientists. We have released an open source tool [2], recently ported to Python, to make this method easily accessible to the scientific community.

Goals

There are two main areas of improvement at the moment in this context:

  • The first one is related to the efficiency and speed of the search that this tool achieves, and which is probably one of the main barriers for a more general adoption of this scientific instrument, especially for larger networks. There are a number of opportunities for improving performance and scalability. In this respect the internship would focus on proposing algorithmic improvements targeting speed and scalability; performing rigorous tests of these proposals, both in terms of performance and correctness; applying viable improvements to the open source tool.
  • The other one corresponds to a categorization issue: similar or equivalent generators can be described by different mathematical functions, which are expressed as formal trees combining mathematical operators and constants. Automatically detecting groups of relatively similar functions is a highly desirable improvement to the tool which would demonstrate the existence of families of fundamental network generative processes. This issue is at the interface between computer science, network science and applied mathematics.

Both topics put much more importance on the improvement of the scientific concepts underlying each task rather than the more low-level issues regarding the pure optimization of the code itself.

Intended audience

We open up to two internships. Candidates should be achieving a Masters Degree in Computer Science and related fields. Beyond a sufficient knowledge in Computer Science, desirable skills include in particular : algorithmic complexity and graph theory, network science, machine learning (especially evolutionary computation / genetic programming), as well as python and its common scientific libraries. Ability to innovate autonomously is expected.

References

[1] Menezes, T. and Roth, C., 2014. Symbolic regression of generative network models. Scientific reports, 4, p.6284. https://www.nature.com/articles/srep06284

[2] https://github.com/telmomenezes/synthetic

Practical Details

  • The internship could last between three and six months;
  • The internship is based in Berlin at the Centre Marc Bloch;
  • The intern should have working proficiency in either English, French or German;
  • The internship allowance is fixed by law at an amount of about 500 euros on a 38 hours basis.
  • Students registered at non-EU universities should also inquire first about the administrative issues related to the possibility of being hosted at the Centre, at a Germany-based institution.

Apply

To apply, please send an e-mail along with your resume to Camille Roth (roth[@]cmb.hu-berlin.de) and Jérémie Poiroux (poiroux[@]cmb.hu-berlin.de).

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.