Retweet networks of the European Parliament: evaluation of the community structure

Cherepnalkoski, Darko; Mozetič, Igor

doi:10.1007/s41109-016-0001-4

Cited by 31 publications

(24 citation statements)

References 42 publications

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“…Another interesting result is an estimation of the Twitter user influence by the Hirsch index. We already showed that retweeting is a form of endorsement and can be used to identify real communities in retweet networks [ 40 ]. The Hirsch index combines productivity (tweet posting) with endorsement (retweeting) and yields a useful measure of influence on Twitter.…”

Section: Discussionmentioning

confidence: 99%

Stance and influence of Twitter users regarding the Brexit referendum

et al. 2017

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Social media are an important source of information about the political issues, reflecting, as well as influencing, public mood. We present an analysis of Twitter data, collected over 6 weeks before the Brexit referendum, held in the UK in June 2016. We address two questions: what is the relation between the Twitter mood and the referendum outcome, and who were the most influential Twitter users in the pro- and contra-Brexit camps? First, we construct a stance classification model by machine learning methods, and are then able to predict the stance of about one million UK-based Twitter users. The demography of Twitter users is, however, very different from the demography of the voters. By applying a simple age-adjusted mapping to the overall Twitter stance, the results show the prevalence of the pro-Brexit voters, something unexpected by most of the opinion polls. Second, we apply the Hirsch index to estimate the influence, and rank the Twitter users from both camps. We find that the most productive Twitter users are not the most influential, that the pro-Brexit camp was four times more influential, and had considerably larger impact on the campaign than the opponents. Third, we find that the top pro-Brexit communities are considerably more polarized than the contra-Brexit camp. These results show that social media provide a rich resource of data to be exploited, but accumulated knowledge and lessons learned from the opinion polls have to be adapted to the new data sources.

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Section: Discussionmentioning

confidence: 99%

Stance and influence of Twitter users regarding the Brexit referendum

et al. 2017

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“…We have already successfully applied the Louvain method for community detection to uncover influential communities in retweet networks in the context of climate and energy issues ( Sluban et al 2015 ). Our application and evaluation of the Louvain method on a retweet network of the European Parliament shows that there is a high degree of match between the detected retweet communities and the political group membership and nationalities of the members of the Parliament ( Cherepnalkoski and Mozetič 2016 ).…”

Section: Retweet Communitiesmentioning

confidence: 97%

Impact investing market on Twitter: influential users and communities

Novak

Amicis²,

Mozetič

2018

Appl Netw Sci

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The 2008 financial crisis unveiled the intrinsic failures of the financial system as we know it. As a consequence, impact investing started to receive increasing attention, as evidenced by the high market growth rates. The goal of impact investment is to generate social and environmental impact alongside a financial return. In this paper we identify the main players in the sector and how they interact and communicate with each other. We use Twitter as a proxy of the impact investing market, and analyze relevant tweets posted over a period of ten months. We apply network, contents and sentiment analysis on the acquired dataset.Our study shows that Twitter users exhibit favourable leaning (predominantly neutral or positive) towards impact investing. Retweet communities are decentralised and include users from a variety of sectors. Despite some basic common vocabulary used by all retweet communities identified, the vocabulary and the topics discussed by each community vary largely. We note that an additional effort should be made in raising awareness about the sector, especially by policymakers and media outlets. The role of investors and the academia is also discussed, as well as the emergence of hybrid business models within the sector and its connections to the tech industry. This paper extends our previous study, one of the first analyses of Twitter activities in the impact investing market.

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“…This was the case with Cherepnalkoski et al . [ 16 , 17 ], who used retweets to determine the political affiliation of the Members of the European Parliament (MEP). However, these authors note that it is difficult to generalize this method, because in addition to their political connections, MEPs also have national preferences.…”

Section: Related Workmentioning

confidence: 99%

Reconstruction of the socio-semantic dynamics of political activist Twitter networks—Method and application to the 2017 French presidential election

2018

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BackgroundDigital spaces, and in particular social networking sites, are becoming increasingly present and influential in the functioning of our democracies. In this paper, we propose an integrated methodology for the data collection, the reconstruction, the analysis and the visualization of the development of a country’s political landscape from Twitter data.MethodThe proposed method relies solely on the interactions between Twitter accounts and is independent of the characteristics of the shared contents such as the language of the tweets. We validate our methodology on a case study on the 2017 French presidential election (60 million Twitter exchanges between more than 2.4 million users) via two independent methods: the comparison between our automated political categorization and a human categorization based on the evaluation of a sample of 5000 profiles descriptions; the correspondence between the reconfigurations detected in the reconstructed political landscape and key political events reported in the media. This latter validation demonstrated the ability of our approach to accurately reflect the reconfigurations at play in the off-line political scene.ResultsWe built on this reconstruction to give insights into the opinion dynamics and the reconfigurations of political communities at play during a presidential election. First, we propose a quantitative description and analysis of the political engagement of members of political communities. Second, we analyze the impact of political communities on information diffusion and in particular on their role in the fake news phenomena. We measure a differential echo chamber effect on the different types of political news (fake news, debunks, standard news) caused by the community structure and emphasize the importance of addressing the meso-structures of political networks in understanding the fake news phenomena.ConclusionsGiving access to an intermediate level, between sociological surveys in the field and large statistical studies (such as those conducted by national or international organizations) we demonstrate that social networks data make it possible to qualify and quantify the activity of political communities in a multi-polar political environment; as well as their temporal evolution and reconfiguration, their structure, their alliance strategies and their semantic particularities during a presidential campaign through the analysis of their digital traces. We conclude this paper with a comment on the political and ethical implications of the use of social networks data in politics. We stress the importance of developing social macroscopes that will enable citizens to better understand how they collectively make society and propose as example the “Politoscope”, a macroscope that delivers some of our results in an interactive way.

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Retweet networks of the European Parliament: evaluation of the community structure

Cited by 31 publications

References 42 publications

Stance and influence of Twitter users regarding the Brexit referendum

Stance and influence of Twitter users regarding the Brexit referendum

Impact investing market on Twitter: influential users and communities

Reconstruction of the socio-semantic dynamics of political activist Twitter networks—Method and application to the 2017 French presidential election

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