Guillaume Deffuant, Irstea, France. Head of the LISC Laboratory (Laboratoire d’Ingénierie pour les Systèmes Complexes)
Title: Modelling the effects of gossips
Abstract: We rapidly review the main models of gossips which generally focus on their utility: detecting cheaters, providing information about potential partners, enforcing social norms. Then we consider a simple model
suggesting that gossips could also have an intrinsically negative impact on group cohesion. In this model, each agent holds an opinion about herself and the others. During random encounters by pairs, agents modify their opinions under the noisy influence of others. The influence is attractive and agents opinions are more strongly attracted by the opinions of whom they value higher than themselves and vice versa. We focus on two unexpected emerging patterns: Starting from zero, when agents talk only about themselves and about their direct interlocutor, agents opinions tend to grow and their average then stabilises at a significantly positive value. When introducing gossips, i.e. when the agents also talk about other agents that they know, this pattern tends to be inverted; the opinions tend to decrease and stabilise on average at a negative value. We show that these patterns are related to the competition between a positive bias for the self-opinion of agents and a negative bias for the opinion about others. The gossips increase the negative bias on the opinions about others and it can overcome the positive bias on self-opinions, which otherwise dominates.
Vittorio Loreto, La Sapienza Università di Roma, Director of the SONY Computer Science Lab in Paris
Title: On the stability of consensus in language and opinion dynamics
Abstract: Language and communication systems are beautiful examples of how a population of individuals can bootstrap almost “perfectly” shared conventions at different degrees of complexity (names, categories, syntax structures). On the other hand, opinion systems display many routes through which consensus can be broken leading instead to temporary or permanent cluster segregation, e.g., the emergence of echo-chambers. In this talk I’ll briefly review how the underlying dynamics of languages and opinions can be mathematically captured, focusing in particular on the resilience and stability of linguistic and opinions structures, trying to answer to questions like: why do linguistics structures emerge and survive on evolutionary time-scales? or ,is it possible to destroy echo-chambers to restore a better information ecology?
Yamir Moreno, Institut of Biocomputation and Physics of Complex Systems, Zaragoza University, Spain
Title: Emergence of Consensus and Topical alignment in online social systems
Abstract: Understanding the dynamics of social interactions is crucial to comprehend human behavior. The emergence of online social media has enabled access to data regarding people relationships on a large scale. Twitter, specifically, is an information-oriented network, with users sharing and consuming information. Here, we first show how the emergence of consensus can be characterized by a structural transition in the communication network, and secondly, we study whether users tend to be in contact with people interested in similar topics, i.e., if they are topically aligned. Specifically, we present a methodology that allows characterizing, using microblogging data, information-driven systems as mutualistic networks. Our results show that collective attention around a topic is reached when the user-meme network self-adapts from a modular to a nested structure, which ultimately allows minimizing competition and attaining consensus. We also propose an approach based on the use of hashtags to extract information topics from Twitter messages and model users’ interests. Our results show that, on average, users are connected with other users similar to them and stronger relationships are due to a higher topical similarity. Furthermore, we show that topical alignment provides interesting information that can eventually allow inferring users’ connectivity. Our work contributes to a better understanding of how complex social systems are structured.
Marton Karsai, Ecole Normale Supérieure de Lyon, Laboratoire de l’Informatique du Parallélisme, Computer Science Department;IXXI Rhône Alpes Complex Systems Institute, INRIA – DANTE team
Title: Socio-economic and network dependencies of linguistic patterns in Twitter
Abstract: Our usage of language is not solely reliant on cognition but is arguably determined by myriad external factors leading to a global variability of linguistic patterns. This issue, which lies at the core of sociolinguistics and is backed by many small-scale studies on face-to-face communication, is addressed here by constructing a dataset combining the largest French Twitter corpus to date with detailed socioeconomic maps obtained from national census in France. We show how key linguistic variables measured in individual Twitter streams depend on factors like socioeconomic status, location, time, and the social network of individuals. We found that (i) people of higher socioeconomic status, active to a greater degree during the daytime, use a more standard language; (ii) the southern part of the country is more prone to use more standard language than the northern one, while locally the used variety or dialect is determined by the spatial distribution of socioeconomic status; and (iii) individuals connected in the social network are closer linguistically than disconnected ones, even after the effects of status homophily have been removed. Our results inform sociolinguistic theory and may inspire novel learning methods for the inference of socioeconomic status of people from the way they tweet.
The satellite will consist of: keynote talks (40min) , invited talks (30 min), contributed talks (20 min) and a closing panel discussion.
Download the detailed opladyn_program
The abstracts below are indexed by the label indicated in the program above. K: Keynote talk, I: Invited talk, C: contributed talk
They can be downloaded by clicking on the presenter’s name