Investigating Tweet Propagation via Dynamical Models and Influencer Analysis

Abstract: Social media consume an increasing portion of people’s daily lives and are important platforms in the realms of politics and marketing for reaching out to voters and consumers. Describing and predicting the behaviour of users on social media is thus of interest for companies and politicians, as well as researchers studying information diffusion and human behaviour. Twitter is a fast-paced microblog that is host to debates, conversations, and campaigns between users as well as organisations all over the world. As all interactions on Twitter are publicly available, the platform has been used as a data source for many studies. While previous works have mainly focused on interaction dynamics for specific user groups or topics, or on predicting virality, the perspective we take in this thesis is to focus on the level of the individual conversation and to use dynamical models to characterise user interactions. The most prominent characteristic of Twitter conversations is the clear presence of peaks in engagement. We introduce a classification scheme based on peak configurations to quantify the interaction patterns present on Twitter and find that around 70% of conversations exhibit a single peak in user engagement, usually followed by a slower decay. A second order linear model describes the dynamics of the single peak scenario well, indicating that most conversations have two phases - an initial phase of rapid rise and decline in interaction rate, followed by a phase of slowly decreasing interaction rate. We quantify the characteristic life span of Twitter conversations in terms of the second order system time constants. Furthermore, we investigate the impact that users with many followers, so called influencers, have on conversation dynamics, and in particular on the emergence of interaction peaks. The data suggests that influencers do have a noticeable, albeit limited effect on the spreading of conversations to other users.