Data science for modeling opinion dynamics on social media

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, 201