(For a more formal report on this, please refer to Section 6.2 of my Ph.D. Thesis.)
2011 was an interesting year for many and obvious reasons. Well, this is my opinion. However, a year is perceived very differently by individuals and therefore, the mean tendencies in the population are of interest.
In this direction ‒ extracting emotional tendencies from the general population ‒, I have analysed Twitter content geolocated in the UK and have extracted affective (mood) scores using an approach which is presented in my Ph.D. Thesis. Four emotions have been considered, namely anger, fear, sadness and joy. In the attached plot, the scores of anger, fear and sadness have been merged and joy's scores have been subtracted from them. Red line is the exact extracted signal, whereas the black line is its smoothed version (using a 7-point moving average to derive a weekly tendency). read more »
(For a more formal report on this, please refer to Section 7.1 of my Ph.D. Thesis.)
This is a network showing the average daily influence on Twitter for some urban centres in the UK. It is based on a set of 70 million tweets posted within a 6-month window and geolocated in the UK.
Starting from London and going clockwise, the importance or influence of a location (node) decreases. The network is directed; nodeA → nodeB means that nodeA influences the content of nodeB.
A quick and easy observation is that London, Manchester and Liverpool are UK's most influential cities in terms of Twitter content on a daily basis.