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A universal formula is shown to predict the dynamics of public opinion including eventual sudden and unexpected outbreaks of minority opinions within a generic parameter space of five dimensions. The formula is obtained combining and extending several components of Galam model of opinion dynamics, otherwise treated separately, into one single update equation, which then deploys in a social space of five dimensions. Four dimensions account for a rich diversity of individual traits within a heterogeneous population, including differentiated stubbornness, contrarianism, and embedded prejudices. The fifth dimension is the size for the discussing update groups. Having one single formula allows exploring the complete geometry of the underlying landscape of opinion dynamics. Attractors and tipping points, which shape the topology of the different possible dynamics flows, are unveiled. Driven by repeated discussions among small groups of people during a social or political public campaign, the phenomenon of minority spreading and parallel majority collapse are thus revealed ahead of their occurrence. Accordingly, within the opinion landscape, unexpected and sudden events like Brexit and Trump victories become visible within a forecast time horizon making them predictable. Despite the accidental nature of the landscape, evaluating the parameter values for a specific case allows to single out which basin of attraction is going to drive the associate dynamics and thus a prediction of the outcome becomes feasible. The model may apply to a large spectrum of social situations including voting outcomes, market shares and societal trends, allowing to envision novel winning strategies in competing environments.

Social media house a trove of relevant information for the study of online opinion dynamics. However, harvesting and analyzing the sheer overload of data that is produced by these media poses immense challenges to journalists, researchers, activists, policy makers, and concerned citizens. To mitigate this situation, this article discusses the creation of (social) media observatories: platforms that enable users to capture the complexities of social behavior, in particular the alignment and misalignment of opinions, through computational analyses of digital media data. The article positions the concept of "observatories" for social media monitoring among ongoing methodological developments in the computational social sciences and humanities and proceeds to discuss the technological innovations and design choices behind social media observatories currently under development for the study of opinions related to cultural and societal issues in European spaces. Notable attention is devoted to the construction of Penelope: an open, web-services-based infrastructure that allows different user groups to consult and contribute digital tools and observatories that suit their analytical needs. The potential and the limitations of this approach are discussed on the basis of a climate change opinion observatory that implements text analysis tools to study opinion dynamics concerning themes such as global warming. Throughout, the article explicitly acknowledges and addresses potential risks of the machine-guided and human-incentivized study of opinion dynamics. Concluding remarks are devoted to a synthesis of the ethical and epistemological implications of the exercise of positioning observatories in contemporary information spaces and to an examination of future pathways for the development of social media observatories. ; The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This project has received funding from the European Union's Horizon 2020 research and innovation programme under grant agreement No. 732942.

5 pages, 6 figures. ; This letter focus on the effect of repulsive interactions on the adoption of an external message in an opinion model. With a simple change in the rules, we modify the Deffuant \emph{et al.} model to incorporate the presence of repulsive interactions. We will show that information receptiveness is optimal for an intermediate fraction of repulsive links. Using the master equation as well as Monte Carlo simulations of the message-free model, we identify the point where the system becomes optimally permeable to external influence with an order-disorder transition. ; We acknowledge financial support by the MEC (Spain) and FEDER (EU) through project FIS2007-60327. TVM acknowledges the support of FCT (Portugal) through Grant No. SFRH/BD/23709/2005, MP is supported by the Belgian Federal Government (IAP project "NOSY: Nonlinear systems, stochastic processes and statistical mechanics"). ; Peer reviewed

Since the last decade, the arrival of social media has established new practices which have changed the way humans interact. Nowadays, anyone can access personal information by simply following a subset of relevant users with a social media account. The availability of online data has led to a recent surge in trying to understand how social influence impact human behavior. This work investigates a set of relevant questions concerning the study of online opinions. How precisely can we predict the variations of judgments resulting from social influence? Are we able to predict the voting behavior of citizens from their online posts and comments? In this thesis, we aim at understanding how opinions are expressed on social media, and whether their evolution can be anticipated by taking the interaction between agents into account. In the first part of the thesis, we introduced the concept of intrinsic noise through a variable that captures the part in human revision judgment that is composed of unpredictable variations. To quantify opinion dynamics that are subject to social influence, we carried out online experiments in which the participants had to estimate some quantities while receiving information about the other participants' opinions. In the end, we discovered that about two thirds of the errors made by classical opinion dynamics models are due to these unpredictable variations. In the second part of the thesis, we focused on the measurement of people's opinion based on digital traces and we analyzed the communication patterns arising on real social media. In the context of political elections, our results showed that valid ideological positions can be deduced for political actors and engaged citizens based solely on network and textual data publicly available on social media platforms. ; (FSA - Sciences de l'ingénieur) -- UCL, 2017

Since the last decade, the arrival of social media has established new practices which have changed the way humans interact. Nowadays, anyone can access personal information by simply following a subset of relevant users with a social media account. The availability of online data has led to a recent surge in trying to understand how social influence impact human behavior. This work investigates a set of relevant questions concerning the study of online opinions. How precisely can we predict the variations of judgments resulting from social influence? Are we able to predict the voting behavior of citizens from their online posts and comments? In this thesis, we aim at understanding how opinions are expressed on social media, and whether their evolution can be anticipated by taking the interaction between agents into account. In the first part of the thesis, we introduced the concept of intrinsic noise through a variable that captures the part in human revision judgment that is composed of unpredictable variations. To quantify opinion dynamics that are subject to social influence, we carried out online experiments in which the participants had to estimate some quantities while receiving information about the other participants' opinions. In the end, we discovered that about two thirds of the errors made by classical opinion dynamics models are due to these unpredictable variations. In the second part of the thesis, we focused on the measurement of people's opinion based on digital traces and we analyzed the communication patterns arising on real social media. In the context of political elections, our results showed that valid ideological positions can be deduced for political actors and engaged citizens based solely on network and textual data publicly available on social media platforms. ; (FSA - Sciences de l'ingénieur) -- UCL, 2017

ObjectiveA classic statement about rights talks in American politics argues they are a divisive force, limiting discussion and creating zero‐sum questions. While we agree that rights talk has become ubiquitous, we disagree about its effects on the mass public. Rights frames are a way to provide publicly accessible reasons that should lead to perceptions of the source as less extreme, which enables discourse rather than cuts it off. We hypothesize that framing conservative issue positions in the language of "rights" (as opposed to morality) will lead to perceptions of the candidate as less conservative and less religious, enabling liberals to increase their support for the source.MethodsUsing a simple experimental design, we compare the effects of varying issue frames on beliefs about and attitudes toward a source across a wide variety of issues: abortion, the death penalty, gay rights, healthcare, and education.ResultsOur results support our hypothesis, though with some variation across issues that accords with the credibility of framing a conservative position in terms of rights.ConclusionContrary to prominent democratic theories, rights‐based frames promote discourse and perceptions of political moderation, particularly among younger Americans.

Intro -- Supervisor's Foreword -- Parts of this thesis have been published in the following articles -- Acknowledgements -- Contents -- 1 Introduction -- 1.1 Social Network Analysis -- 1.2 Opinion Dynamics and Influence Networks -- 1.2.1 The Fundamental French-Harary-DeGroot Model -- 1.2.2 Beyond Consensus and Towards Complex Social Phenomena -- 1.2.3 Social Phenomena of Relevance -- 1.2.4 Motivations and Key Concepts -- 1.2.5 Relation to Coordination of Multi-agent Systems -- 1.3 Thesis Outline and Statements of Collaborations -- References -- 2 Preliminaries -- 2.1 Notations and Definitions -- 2.2 Graph Theory -- 2.3 Basic Models of Opinion Dynamics -- 2.3.1 The DeGroot Model -- 2.3.2 The Friedkin-Johnsen Model -- 2.3.3 Comments on the Models -- References -- Part I How Differences in Private and Expressed Opinions Arise -- 3 A Novel Model for Opinion Dynamics Under Pressure to Conform -- 3.1 Introduction -- 3.1.1 Chapter Organization -- 3.2 A Model with Expressed and Private Opinions -- 3.2.1 The Opinion Dynamics Model -- 3.2.2 Local Public Opinion -- 3.2.3 Obtaining a Compact Form for the Influence Network -- 3.3 Convergence Properties of the Model -- 3.3.1 Causes of Persistent Disagreement and Differences in Opinions -- 3.3.2 Estimating Disagreement in the Private Opinions -- 3.3.3 An Individual's Resilience Affects Everyone -- 3.3.4 Local Public Opinions -- 3.3.5 Simulations -- 3.4 Conclusion -- 3.5 Appendix: Proofs and Simulations -- 3.5.1 Proof of Lemma 3.1 -- 3.5.2 Proof of Lemma 3.2 -- 3.5.3 Proof of Lemma 3.3 -- 3.5.4 Simulation Counter-Example -- 3.5.5 Proof of Corollary 3.2 -- References -- 4 The EPO Model's Connections with Social Psychology Concepts -- 4.1 Introduction -- 4.1.1 Introduction to Asch's Experiments -- 4.1.2 Pluralistic Ignorance in Social Networks -- 4.1.3 Chapter Organization.