Abstract Recently we have witnessed a number of rapid shifts toward populism in the rhetoric and policies of major political parties, as exemplified in the 2016 Brexit Referendum, 2016 US Election, and 2017 UK General Election. Our perspective here is to focus on understanding the underlying societal processes behind these recent political shifts. We use novel methods to study social dynamics behind the 2016 Presidential election. This is done by using network science methods to identify key groups associated with the US right-wing during the election. We investigate how the groups grew on Twitter, and how their associated accounts changed their following behaviour over time. We find a new external faction of Trump supporters took a strong influence over the traditional Republican Party (GOP) base during the election campaign. The new group dominated the GOP group in terms of new members and endorsement via Twitter follows. Growth of new accounts for the GOP party all but collapsed during the campaign. While the Alt-right group was growing exponentially, it has remained relatively isolated. Counter to the mainstream view, we detected an unexpectedly low number of automated ‘bot’ accounts and accounts associated with foreign intervention in the Trump-supporting group. Our work demonstrates a powerful method for tracking the evolution of societal groups and reveals complex social processes behind political changes.

Citation: Bryden J, Silverman E (2019) Underlying socio-political processes behind the 2016 US election. PLoS ONE 14(4): e0214854. https://doi.org/10.1371/journal.pone.0214854 Editor: Haroldo V. Ribeiro, Universidade Estadual de Maringa, BRAZIL Received: September 17, 2018; Accepted: March 21, 2019; Published: April 9, 2019 Copyright: © 2019 Bryden, Silverman. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Data Availability: The data were downloaded according to Twitter’s Terms and Conditions (https://developer.twitter.com/en/developer-terms/agreement-and-policy) using the Twitter REST API. In accordance with Twitter’s Terms and Conditions, we have only released the Twitter IDs of the accounts we have sampled. These are available at http://dx.doi.org/10.17637/rh.7160027. Our study may be reconstructed by downloading additional data associated with these Twitter IDs using the Twitter REST API. Funding: JB was supported by Economic and Social Research Council (ES/L000113/1). ES is supported by the Medical Research Council (MC_UU_12017/14) and the Chief Scientist Office (SPHSU14). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. Competing interests: The authors have declared that no competing interests exist.

Introduction Donald Trump’s victory in the GOP primaries and the Presidential race surprised political analysts and confounded pollsters. Trump achieved this victory via a populist campaign which incorporated racially-charged and misogynistic language [1, 2]. This unusual campaign shifted the direction of the GOP and the US right-wing toward the far-right of the political spectrum [2]. An important factor behind this success was the campaign’s use of social media communication channels, especially Twitter [3]. Twitter, and social media in general, have become important tools for politicians and their followers to spread political messages [4–8]. The hierarchical structures commonly found in social media networks mean that well-connected politicians act as hub nodes, with information and influence spreading outward over the network. Political parties form clusters which reside at the centre of these networks [5, 8–10]. As intra-party discourse is increasingly taking place online, traditional boundaries between politicians, activists, party-members and members of the public have become increasingly blurred. The increased openness of parties moving their political discourse online has undoubted benefits for transparency and accountability. The concern is the opportunity this provides for an external group to target the online presence of a political party, and then start to dictate their political direction. The ability of a minority group to rapidly generate a new political faction and take control of a major political party in this way can cause problems for democracy [11, 12]. Such a novel process would differ radically from more traditional models of dynamics amongst political elites who occupy different internal factions of a party [11, 13], moving to a model whereby external factions play a stronger role [13–15]. In this work we look for evidence of an external group influencing the online presence of the GOP in the run up to the 2016 election and how this can explain the shift in the party’s direction. Recent changes in the rhetoric and policy platform of the GOP provide an example of the impact of external groups. During the 2016 election cycle, the Alt-right—an online community of activists who identify as white nationalists—emerged as a major influence on GOP political discourse. The Trump campaign and pro-Trump media outlets like Breitbart News co-opted Alt-right rhetoric, provoking a movement from implicit racial priming in campaigns to explicit racial messaging [2]. Trump and his supporters were able to use the shock value of explicit racial rhetoric to gain media attention for his platform, without destroying the GOP’s electoral calculus. The Trump campaign marked a large and sudden shift in rhetoric and target demographics in 2016, unprecedented in modern US political history [16]. Our work seeks to understand how communities of activists might provoke such a significant shift in the attitudes and rhetoric of a major political party. We looked for how political activists organised themselves and their political messaging, and how these aspects changed over time. We did this by examining Twitter data in the context of the 2016 election, given that Twitter has been shown to reflect US national polling aggregates accurately [17]. By studying these changes, we can analyse and document shifting allegiances during the election cycle, and the roles of different factors driving these dynamics.

Discussion The method presented here shows how it is possible to uncover underlying socio-political processes behind real political change. This is done by first identifying online groups, and then observing how they grow over time and how their members change the other accounts they follow. Political changes reflect these underlying processes so the ability to observe them is critical. Consequently, this approach is likely to be of great value in the social and political sciences. Twitter’s important role in party political communication and activism means that we can use this method to study shifting political discourses in a way we never could before, enabling us to develop more powerful theories of socio-political dynamics. To study social and socio-political processes, our method incorporates two key novel aspects. First, an innovative sampling procedure which allows us to target and download key groups of interest. Second, our generated data shows how groups evolve over time, including the shifting allegiances of group members. Since these data are dynamic networks, this approach can enhance our understanding of dynamic social processes. While much modelling work has been done [25–29], the next step is to bring these models to data such as that presented in this work [30–33]. Given the self-selecting nature of Twitter groups, we may be concerned that the groups we have found may not truly reflect the position of the party’s base. However, the high levels of intra-group connections are representative of an interconnected political party [7, 18]. The behaviour observed in this sample during the 2016 election cycle does match the behaviour seen during the rise of the Tea Party and victory of Trump in the primary elections. The sample here was targeted initially at the Alt-right, but we found connected accounts which were also associated with the right-wing of both the US and other countries, suggesting that the sample has captured a wide swathe of right-wing political communication and not just fringe groups or extremist individuals. The sample also went beyond the right-wing political sphere and sampled a loosely intra-connected group of general Twitter accounts (see group marked * in Fig 1), indicating that it had captured a comprehensive picture of those accounts associated with the US right-wing. Social media has continued to expand its influence over the political process [4–10, 17]. These technologies enable individuals to easily connect with one another, based on shared political opinions. It follows that they are likely to be playing a strong role in recent social-political movements by allowing politicians to rally disaffected individuals. At the very least, social media data allow us to observe the processes behind changing political factions. Understanding these processes, and how they happen is critically important to understanding modern democracy and voter behaviour, and our method marks a step change in how political factions can be identified, analysed, and tracked. The data we have presented show how, starting from June 2015, an external faction shifted the GOP away from its previous base. This shift could be explained by results from another study, which indicate that as support for an extreme position increases, support for a corresponding less extreme position will increase with a nonlinear relation [28]. Reasons for the emergence of the Trump group are less clear because it is relatively isolated from the more extreme alt-right group. There is, however, a potential role played by automated accounts where even a small number of bots were shown to have been able to shift opinion in Trump supporters [34]. That said, our data suggest that Trump’s followers consist largely of real, highly-engaged supporters. An alternative perspective looks at psychological reasons behind the shift to the right. Evidence shows that poor levels of well-being and low optimism for the future played a strong role in the shift toward Trump [35], with economic insecurity and a cultural backlash being key factors [2, 16, 36]. In this case, our results would indicate that disaffection for a major political party may have provided space in which a new political movement could grow and eventually take over that party.

Conclusion Donald Trump’s ascent to the Presidency has prompted a great deal of effort amongst pollsters, political scientists and social scientists to unearth the reasons for his unexpected success. Here we provide a method to follow the shifts in group membership and influence that can occur in political parties, and in so doing provide indicators of impending moves toward extremism within those parties. Our results fit into a picture where the Trump campaign’s mobilisation of a targeted group of supporters more than made up for Clinton’s funding advantage [29, 37]: A significant shift in the US political landscape. With that in mind, developing a more robust understanding of how political factions can be identified and analysed can give us a way to follow these fast-appearing and highly-motivated supporter groups, and their influence on politics.

Methods Ethics We were given ethical approval for the data collection and storage methods used in this research by the relevant Departmental Ethics Committee at Royal Holloway University of London. Even though all the information downloaded from Twitter is public, it was stipulated that we would only publish anonymous and/or agglomerate data. Consequently, we are only releasing the Twitter IDs which we sampled for the study and the agglomerate data published in the manuscript. Downloading a sample of accounts Our aim was to bias our sample to download accounts which had a right-wing political orientation, as defined in the American political context. To do this, we developed a novel weighted snowball-sampling technique in order to download a sample of twitter accounts which all share similar interests. Given our intent to focus on right-wing groups in the US, we began the sample with a prominent account followed by a large number of Alt-right and Trump supporters: the ‘_altright_’ Twitter account. Our technique biases the sample to accounts that are more likely to follow accounts which have already been sampled, collecting groups of close-knit accounts which are closely linked to those accounts already sampled. The process incrementally builds a dataset of accounts starting in a local community and then on to other closely linked communities. Other community-sampling techniques focus on a single group [38]. We downloaded sample accounts using the Twitter REST API. For each account sampled we recorded its creation date, biography, and the lists of which accounts they had followed and which accounts had followed them. People on Twitter tend to follow other accounts quite broadly but when two accounts follow one another that is a much better indication that they have something in common. Other approaches have used retweet or mention networks [7, 18], however we decided to use a follower network because these links are more permanent and less transient. For these reasons we used mutual following to build our network of accounts. To download our sample, we maintained a master list of accounts to be sampled, which each account being assigned a score determining which would be the next to be sampled. For any new account sampled, we generated a set of all accounts which both followed and were followed by this new account. This set of mutual-follower accounts was merged with the master list of accounts and their sample scores were updated. Score updates were done by dividing the score of the account which had been sampled equally amongst its mutual followers. The initial account was assigned a sample score of 1.0 and all other accounts were initiated with a sample score of 1.0. After the scores were updated, we then identified the highest-scoring account on our list which had not yet been sampled, and iterated the procedure with that account. Generating and characterising groups of accounts Our sample generated a network of accounts with accounts linked to one another based on whether they had mutually followed one another. We generated community structure for these accounts using the Louvain Method [19] and used groups at the lowest level of the hierarchy. The groups found were characterised by generating word frequencies for every word and pairs of words (unigrams and bigrams) used in the biographies of the Twitter accounts for each group. Words were converted to lower case and stripped of punctuation. In order to establish which words or word pairs characterise each group, we compared the fraction of users that use each word within a group with the fraction of users that used the word globally [18]. We then assessed how unlikely it was that the difference between these two fractions could have happened by random chance. This is given by the standardised Z-score which, for each word/word pair used in community c, is where μ c is the fraction of users in community c which have used the word, μ g is the fraction of all users that have used it, N c is the number of users in community c, and σ g is the standard deviation of usage of the word amongst all users where N is the global number of users. The words with the highest Z-Scores are those which are used more often by and are used to characterise that group. We ran an initial sample in November 2016, shortly after the Presidential election. To confirm the results we found had not been by chance, we reran the sample in July 2017 starting with the same initial ‘_altright_’ account. However, Twitter had suspended the account in the interim, so we used a downloaded copy of the account from November 2016 including its list of followers and used this to seed the sample. The rerun of the process yielded similar results to the first run. The sampler found a group with a large number of relatively neutral accounts which were also loosely interconnected (see starred group in Fig 1). Due to the nature of our sampler, one would expect to find that, after it had sampled the majority of interconnected accounts associated with the wider right-wing group, it would then start to sample accounts from the remainder of the Twitter web site. These accounts would not be closely interconnected but would be relatively isolated from the other right wing groups. The biographies of the group members were also consistent with this picture of the accounts not having strong political affiliations. Due to the fact that our sampler had sampled a large number of accounts from this looser-knit group and more-neutral group, we were confident that our sample had covered a broad sample of right-wing affiliated groups. Inferring account-following dates In order to track how accounts shifted their following behaviour over time, we needed to develop a method to infer the date of each following-event (i.e., when an account followed another account). Though our data contained a list of accounts followed, the order in which they were followed, and account creation dates, we did not have the specific dates of following-events. However, note that it is not possible to follow an account until it has been created. We used this information to infer that the following-event of an account must have happened after it was created, and consequently that all subsequent following events in the list must have happened after that date as well. In the list of accounts-followed we marked timestamp-accounts by going through the list in the order which they were followed (starting at the earliest account) and marking those accounts which had been created after all the previous accounts in the list. All accounts followed in the list after these timestamp-accounts must have been followed at some time after the timestamp-account was created. We inferred that the timestamp-accounts had been followed on their creation date. We then inferred the date-followed for accounts listed between the timestamp-accounts by spacing these dates evenly between these creation dates. Statistical analysis Our data show how the members of different groups change the way they follow accounts in the other groups over time. We wanted to statistically confirm that this following-pattern had not happened due to some random process in the following behaviour of a group’s members. Consequently we looked at the accounts followed by members of a group on a monthly basis. For the accounts followed by each member each month, we then calculated the proportions of these followed-accounts according to the group membership of the followed-accounts. We then calculated the mean and corresponding 95% confidence intervals for these per-group proportions. Because the 95% confidence intervals were relatively very small, we concluded that the patterns we found were highly statistically significant and did not need further testing to support our argument.

Supporting information S1 Fig. All groups in the sample. Plot showing the groups found by our sample and summarising the how the accounts follow one antoher at a group level. Groups shown (> 200 members) are sized by the number of members. Lines between groups are the same colour as the originating group. Links are the same colour as the group containing the following accounts and point to the group containing the followed accounts. The thickness of the line represents the average proportion of accounts, per individual in the originating group, which are followed in the linked group. https://doi.org/10.1371/journal.pone.0214854.s001 (PNG) S2 Fig. Botometer distributions. The distributions of scores assigned by Botometer to samples of 2,000 accounts taken from each of the three focal groups. https://doi.org/10.1371/journal.pone.0214854.s002 (PNG) S1 File. Group dynamics. Movie showing the dynamics of the three groups and their following behaviour over time. It shows how initial growth and internal following behaviour of the GOP group is superceded by growth and following of the Trump group. Areas of groups represent the size of the group at the time shown. For each month, link widths are scaled to represent the average proportion of accounts, per individual in the originating group, which were followed in the linked group. Links are the same colour as the group containing the following accounts and point to the group containing the followed accounts. https://doi.org/10.1371/journal.pone.0214854.s003 (MP4)

Acknowledgments Thanks to Johannes Müller for helpful comments. JB was supported by the Economic and Social Research Council (ES/L000113/1). ES is part of the Complexity in Health Improvement Programme, supported by the Medical Research Council (MC_UU_12017/14) and the Chief Scientist Office (SPHSU14).