Social Data Analysis: Dynamics of real-time data

Bhulai, S. [Promotor]Koole, G.M. [Promotor

Quantifying societal emotional resilience to natural disasters from geo-located social media content

Natural disasters can have devastating and long-lasting effects on a community’s emotional well-being. These effects may be distributed unequally, affecting some communities more profoundly and possibly over longer time periods than others. Here, we analyze the effects of four major US hurricanes, namely, Irma, Harvey, Florence, and Dorian on the emotional well-being of the affected communities and regions. We show that a community’s emotional response to a hurricane event can be measured from the content of social media that its population posted before, during, and after the hurricane. For each hurricane making landfall in the US, we observe a significant decrease in sentiment in the affected areas before and during the hurricane followed by a rapid return to pre-hurricane baseline, often within 1-2 weeks. However, some communities exhibit markedly different rates of decline and return to previous equilibrium levels. This points towards the possibility of measuring the emotional resilience of communities from the dynamics of their online emotional response.Applied Probabilit

Circadian Patterns in Twitter

In this paper, we study activity on the microblogging platform Twitter. We analyse two separate aspects of activity on Twitter. First, we analyse the daily and weekly number of posts, through which we find clear circadian (daily) patterns emerging in the use of Twitter for multiple languages. We see that both the number of tweets and the daily and weekly activity patterns differ between languages. Second, we analyse the progression of individual tweets through retweets in the Twittersphere. We find that the size of these progressions follow a power-law distribution. Furthermore, we build an algorithm to analyse the actual structure of the progressions and use this algorithm on a limited set of tweets. We find that retweet trees show a star-like structure

Reply to Schmidt et al.: A robust surge of cognitive distortions in historical language

Applied ProbabilityQuantum Computin