By Newsgram Staff Writer
Researchers at Universidad Politecnica de Madrid in Spain have developed a model to detect the extent to which a conversation on Twitter – and thus the actual offline argument and political climate—is polarised.
The model revealed that a group is “perfectly polarised” on a given topic when it has been divided into two groups of the same size holding opposite opinions. A politically polarised society implies several risks, such as the appearance of radicalism or civil wars.
“We were interested to find out how can political polarisation be detected and, therefore, be fixed,” said Rosa Maria Benito, a professor at Universidad Politecnica de Madrid.
Case Study of Hugo Chavez
The researchers took the death of Venezuelan President Hugo Chavez in 2013 as the case study. Analysing 16 million tweets from more than three million users following Chavez’s death in Venezuela, Spanish researchers quantified the extent of polarization in Caracas. Benito and her colleagues downloaded over 16,383,490 messages written by 3,173,090 Twitter users from one month before and one month after Chavez’s death on March 5, 2013 – a total of 56 days.
They used these messages to create retweet networks, in which retweets could be considered a proxy for influence and adoption of ideas, and at last applied their model and polarisation index to the networks. Compilation of this data gave them a day-by-day breakdown of the extent of political polarisation in Venezuela over the course of 56 days.
It was surprising for the researchers to find that during the most critical days of the conversation – between Chavez’s death and state funeral- polarisation dropped to its lowest levels as foreign users had joined the conversation. This was the reason behind the disappearance of polarised structure of the network. Benito and her colleagues then plotted the geo-located tweets on a map of Caracas, the Venezuelan capital, and compared the polarity expressed – opposition— with the voting records and political affiliations of each municipality, finding a strong correlation between the two.