Social Computing Topic

The Social Computing topic aims to gather research around computational models for the analysis of social interactions whether for web analysis or social robotics. The peculiarity of this theme is its multidisciplinarity: computational models are established in close collaboration with research fields such as psychology, sociology, and linguistics. They are based on methods from various fields in signal processing (eg speech signal processing for the recognition of emotions), in machine learning (eg use of Random Random Fields for the detection of opinions in texts ), in computer science (ex: the automatic processing of the natural language for the detection of opinions, theintegration of the socio-emotional component in the human-machine interactions).

The research carried out on this topic within the  IDS department is organized around the following three items:

  • social data analysis of the web
    • textual data: opinion mining, argument mining
    • relational data: analysis of community graphs, analysis of preference data, log analysis
  • social signal processing and emotion recognition (multimodal analysis – text, audio, video, social and emotional signals) in human-human and human-agent interactions.
    • Detection of user’s opinions and social-emotional behaviors
    • Recognition of emotions in handwriting
    • Generation of socio-emotional behaviors
  • socio-emotional strategies of interaction and human-machine dialogue

Applications for this theme include: e-reputation, referral systems, data mining, social robotics, embodied conversational agents and virtual companions (Cortana, Google now, Siri), multimedia indexing, medical applications (diagnostic aid).

Permanent researchers in Image Data and Signal Department : Chloé Clavel, Florence d’Alché Buc, Slim Essid, Laurence Likforman, Giovanna Varni, Anne Sabourin

Permanent Researcher in Network and Computer Science Department: Jean-Louis Dessalles

Postdocs: Atef Ben Youssef, Brian Ravenet, Varun Jain, Guillaume Dubuisson

PhD students: Caroline Langlet, Thomas Janssoone, Irina Poltavchenko, Valentin Barriere, Alexandre Garcia

Inter-disciplinary topic, related to the activities of the Economics and Social Sciences Department (SES):

Contact: Chloé Clavel


Next seminars: 

2 May 2018, 12h, B547

Title : State-dependent valuation and confirmatory biases in human reinforcement learning

Speaker : Stefan Palminteri, Human Reinforcement Learning team, Institut National de la Recherche Médical & Ecole Normale Supérieure

Abstract : Valence is a fundamental concept in the learning, decision-making and emotion literature. However, in the reinforcement learning context, different facets of this psychological concept are often confounded and/or ill-defined. In the present talk we theoretically propose, mathematically formalize and experimentally investigate two different facets of valence: ‘contextual’ and ‘informational’. Combining imaging and modeling techniques in a series of studies, we found that these different aspects have dissociable computational and neural correlates, and exert powerful effects on human learning behavior.

Main references :

Palminteri S, Justo D, Jauffret C, Pavlicek B, Dauta A, Delmaire C, Czernecki V, Karachi K, Capelle L, Durr A, Pessiglione M. Critical roles for anterior insula and dorsal striatum in punishment-based avoidance learning. Neuron (2012).

Palminteri S, Khamassi M, Joffily M, Coricelli G. Contextual modulation of value signals in reward and punishment learning. Nature Communications (2015).

Lefebvre G, Lebreton M, Meyniel F, Bourgeois-Gironde S, Palminteri S. Behavioural and neural characterization of optimistic reinforcement learning. Nature Human Behaviour (2017).

Past seminars :

21 March 12h B547

Title : Pauses in the perception and production of fluent speech: The case of nonnative listeners and speakers

Abstract: The production of silent and filled pauses in speech is known to vary by language. If production proceeds and motivates perception, then it is expected that listeners—including nonnative listeners—will show variation in how pauses influence their perception of speech. In this talk, I will share results from an experimental evaluation of this prediction with several different language groups.  Although still in progress, results support the hypothesis and show that nonnative listeners are sensitive to both silent and filled pauses, but in slightly different degrees.

Based on these results, I will discuss how nonnative speakers may become more aware of pauses in their own speech and how it relates to their second language fluency.  I will introduce and demonstrate Fluidity—an application designed to help learners increase their speed fluency. The application gives real-time feedback on such fluency-related measures as pauses via an expressive virtual agent. Some results from usability testing will be presented in order to show how the application is received by learners and their suggestions for future development.

Bio: Ralph Rose is a member of the Faculty of Science and Engineering at Waseda University in Tokyo, Japan where he and his colleagues manage the English language program. His research interests include filled pauses (uh/um), second language fluency, and educational technology—and particularly the intersection of these three.