Online reactions to the 2017 ‘Unite the right’ rally in Charlottesville: measuring polarization in Twitter networks using media followership
Abstract Network analysis of social media provides an important new lens on politics, communication, and their interactions. This lens is particularly prominent in fast-moving events, such as conversations and action in political rallies and the use of social media by extremist groups to spread thei...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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SpringerOpen
2020-01-01
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| Series: | Applied Network Science |
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| Online Access: | https://doi.org/10.1007/s41109-019-0223-3 |
| _version_ | 1818930640696901632 |
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| author | Joseph H. Tien Marisa C. Eisenberg Sarah T. Cherng Mason A. Porter |
| author_facet | Joseph H. Tien Marisa C. Eisenberg Sarah T. Cherng Mason A. Porter |
| author_sort | Joseph H. Tien |
| collection | DOAJ |
| description | Abstract Network analysis of social media provides an important new lens on politics, communication, and their interactions. This lens is particularly prominent in fast-moving events, such as conversations and action in political rallies and the use of social media by extremist groups to spread their message. We study the Twitter conversation following the August 2017 ‘Unite the Right’ rally in Charlottesville, Virginia, USA using tools from network analysis and data science. We use media followership on Twitter and principal component analysis (PCA) to compute a ‘Left’/‘Right’ media score on a one-dimensional axis to characterize Twitter accounts. We then use these scores, in concert with retweet relationships, to examine the structure of a retweet network of approximately 300,000 accounts that communicated with the #Charlottesville hashtag. The retweet network is sharply polarized, with an assortativity coefficient of 0.8 with respect to the sign of the media PCA score. Community detection using two approaches, a Louvain method and InfoMap, yields communities that tend to be homogeneous in terms of Left/Right node composition. We also examine centrality measures and find that hyperlink-induced topic search (HITS) identifies many more hubs on the Left than on the Right. When comparing tweet content, we find that tweets about ‘Trump’ were widespread in both the Left and Right, although the accompanying language (i.e., critical on the Left, but supportive on the Right) was unsurprisingly different. Nodes with large degrees in communities on the Left include accounts that are associated with disparate areas, including activism, business, arts and entertainment, media, and politics. By contrast, support of Donald Trump was a common thread among the Right communities, connecting communities with accounts that reference white-supremacist hate symbols, communities with influential personalities in the alt-right, and the largest Right community (which includes the Twitter account FoxNews). |
| first_indexed | 2024-12-20T04:03:55Z |
| format | Article |
| id | doaj.art-ba40d63eebac4c9083a0614abe3be7f2 |
| institution | Directory Open Access Journal |
| issn | 2364-8228 |
| language | English |
| last_indexed | 2024-12-20T04:03:55Z |
| publishDate | 2020-01-01 |
| publisher | SpringerOpen |
| record_format | Article |
| series | Applied Network Science |
| spelling | doaj.art-ba40d63eebac4c9083a0614abe3be7f22022-12-21T19:54:06ZengSpringerOpenApplied Network Science2364-82282020-01-015112710.1007/s41109-019-0223-3Online reactions to the 2017 ‘Unite the right’ rally in Charlottesville: measuring polarization in Twitter networks using media followershipJoseph H. Tien0Marisa C. Eisenberg1Sarah T. Cherng2Mason A. Porter3Department of Mathematics and Mathematical Biosciences Institute; The Ohio State UniversityDepartment of Epidemiology, Center for the Study of Complex Systems, and Department of Mathematics; University of MichiganPrecision Health Enterprise and Institute for Next Generation Healthcare, Mount Sinai Health SystemDepartment of Mathematics; UCLAAbstract Network analysis of social media provides an important new lens on politics, communication, and their interactions. This lens is particularly prominent in fast-moving events, such as conversations and action in political rallies and the use of social media by extremist groups to spread their message. We study the Twitter conversation following the August 2017 ‘Unite the Right’ rally in Charlottesville, Virginia, USA using tools from network analysis and data science. We use media followership on Twitter and principal component analysis (PCA) to compute a ‘Left’/‘Right’ media score on a one-dimensional axis to characterize Twitter accounts. We then use these scores, in concert with retweet relationships, to examine the structure of a retweet network of approximately 300,000 accounts that communicated with the #Charlottesville hashtag. The retweet network is sharply polarized, with an assortativity coefficient of 0.8 with respect to the sign of the media PCA score. Community detection using two approaches, a Louvain method and InfoMap, yields communities that tend to be homogeneous in terms of Left/Right node composition. We also examine centrality measures and find that hyperlink-induced topic search (HITS) identifies many more hubs on the Left than on the Right. When comparing tweet content, we find that tweets about ‘Trump’ were widespread in both the Left and Right, although the accompanying language (i.e., critical on the Left, but supportive on the Right) was unsurprisingly different. Nodes with large degrees in communities on the Left include accounts that are associated with disparate areas, including activism, business, arts and entertainment, media, and politics. By contrast, support of Donald Trump was a common thread among the Right communities, connecting communities with accounts that reference white-supremacist hate symbols, communities with influential personalities in the alt-right, and the largest Right community (which includes the Twitter account FoxNews).https://doi.org/10.1007/s41109-019-0223-3United States politicsPolitical extremismMedia polarizationSocial mediaTwitterCommunity structure |
| spellingShingle | Joseph H. Tien Marisa C. Eisenberg Sarah T. Cherng Mason A. Porter Online reactions to the 2017 ‘Unite the right’ rally in Charlottesville: measuring polarization in Twitter networks using media followership Applied Network Science United States politics Political extremism Media polarization Social media Community structure |
| title | Online reactions to the 2017 ‘Unite the right’ rally in Charlottesville: measuring polarization in Twitter networks using media followership |
| title_full | Online reactions to the 2017 ‘Unite the right’ rally in Charlottesville: measuring polarization in Twitter networks using media followership |
| title_fullStr | Online reactions to the 2017 ‘Unite the right’ rally in Charlottesville: measuring polarization in Twitter networks using media followership |
| title_full_unstemmed | Online reactions to the 2017 ‘Unite the right’ rally in Charlottesville: measuring polarization in Twitter networks using media followership |
| title_short | Online reactions to the 2017 ‘Unite the right’ rally in Charlottesville: measuring polarization in Twitter networks using media followership |
| title_sort | online reactions to the 2017 unite the right rally in charlottesville measuring polarization in twitter networks using media followership |
| topic | United States politics Political extremism Media polarization Social media Community structure |
| url | https://doi.org/10.1007/s41109-019-0223-3 |
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