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Twitter can reveal political polarisation among users

Twitter can reveal political polarisation among users
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Researchers in Spain have developed a model to detect the extent to which a conversation on Twitter -- and thus the actual offline argument and...

London: Researchers 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. According to the researchers at Universidad Politecnica de Madrid, a group is "perfectly polarised" on a given topic when it has been divided into two groups of the same size holding opposite opinions.

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. As described in the journal Chaos, 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 applied their model and polarisation index to the networks. Once assembled, this confluence of data gave them a day-by-day breakdown of the extent of political polarisation in Venezuela over the course of 56 days.

The researchers found that during the most critical days of the conversation -- between Chavez's death and state funeral -- polarisation dropped to its lowest levels, due to the fact that foreign users joined the conversation. This temporarily caused the polarised structure of the network to disappear until the political electoral campaign began six days later.

Benito and her colleagues then plotted the geo-located tweets on a map of Caracas, the Venezuelan capital, and compared the polarity expressed -- officialism or opposition -- with the voting records and political affiliations of each municipality, finding a strong correlation between the two. The same approach could be applied to make political "polarisation maps" of other cities as well.

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