CO
C. Olaru
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Polarisation in online social networks
Detecting polarisation using cross-community social ties
The last decade has seen an uptick in political polarisation on online social networks. This has led to increasing distrust and antagonism, sometimes resulting in conflict. As such, studying the emergence of political polarisation is increasingly important in
the current political landscape. The field of network science provides ways to model the underlying structure of social networks. This facilitates the measurement and prediction of polarisation. We propose a way of predicting the emergence of political polarisation by looking at the community structure of a network. Specifically, we examine the extent to which social ties between members of different communities influence how political polarisation takes form. We measure polarisation on
two levels: ideological and relational. We experiment on synthetic networks, generated using two different models. One of the models produces networks with homogeneous community structures. The other generates them according to power law distributions. Our findings show a negative correlation between the fraction of cross-community social ties and the level of polarisation in the network. ...
the current political landscape. The field of network science provides ways to model the underlying structure of social networks. This facilitates the measurement and prediction of polarisation. We propose a way of predicting the emergence of political polarisation by looking at the community structure of a network. Specifically, we examine the extent to which social ties between members of different communities influence how political polarisation takes form. We measure polarisation on
two levels: ideological and relational. We experiment on synthetic networks, generated using two different models. One of the models produces networks with homogeneous community structures. The other generates them according to power law distributions. Our findings show a negative correlation between the fraction of cross-community social ties and the level of polarisation in the network. ...
The last decade has seen an uptick in political polarisation on online social networks. This has led to increasing distrust and antagonism, sometimes resulting in conflict. As such, studying the emergence of political polarisation is increasingly important in
the current political landscape. The field of network science provides ways to model the underlying structure of social networks. This facilitates the measurement and prediction of polarisation. We propose a way of predicting the emergence of political polarisation by looking at the community structure of a network. Specifically, we examine the extent to which social ties between members of different communities influence how political polarisation takes form. We measure polarisation on
two levels: ideological and relational. We experiment on synthetic networks, generated using two different models. One of the models produces networks with homogeneous community structures. The other generates them according to power law distributions. Our findings show a negative correlation between the fraction of cross-community social ties and the level of polarisation in the network.
the current political landscape. The field of network science provides ways to model the underlying structure of social networks. This facilitates the measurement and prediction of polarisation. We propose a way of predicting the emergence of political polarisation by looking at the community structure of a network. Specifically, we examine the extent to which social ties between members of different communities influence how political polarisation takes form. We measure polarisation on
two levels: ideological and relational. We experiment on synthetic networks, generated using two different models. One of the models produces networks with homogeneous community structures. The other generates them according to power law distributions. Our findings show a negative correlation between the fraction of cross-community social ties and the level of polarisation in the network.