The authors declare that they have no competing interests.
Conceived and designed the experiments: IM, DC Performed the experiments: DC Analyzed the data and results, and wrote the paper: DC, IM. Both authors read and approved the final manuscript.
Analyzing information from social media to uncover underlying real-world phenomena is becoming widespread. The goal of this paper is to evaluate the role of Twitter in identifying communities of influence when the ‘ground truth’ is known. We consider the European Parliament (EP) Twitter users during a period of one year, in which they posted over 560,000 tweets. We represent the influence on Twitter by the number of retweets users get. We construct two networks of influence: (i) core, where both users are the EP members, and (ii) extended, where one user can be outside the EP. We compare the detected communities in both networks to the ‘ground truth’: the political group, country, and language of the EP members. The results show that the core network closely matches the political groups, while the extended network best reflects the country of origin. This provides empirical evidence that the formation of retweet networks and community detection are appropriate tools to reveal real-world relationships, and can be used to uncover hidden properties when the ‘ground truth’ is not known.