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I Read It on Reddit: Exploring the Role of Online Communities in the 2016 US Elections News Cycle

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Social Informatics (SocInfo 2017)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 10540))

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Abstract

Reddit has developed into a significant platform for political discussion among Millennials. In this exploratory study, we examine subscription trends on three political sub-forums on Reddit during the 2016 US presidential elections: /The_Donald, /SandersForPresident, and /HillaryClinton. As a theoretical framework, we draw from work on online communities’ group identity and cohesion. Concretely, we investigate how subscription dynamics relate to positive, negative and neutral news events occurring during the election cycle. We classify news events using a sentiment analysis of event-related news headlines. We observe that users who supported Sanders displayed no consolidation of support for Clinton after she won the Democratic Party’s presidential nomination. Secondly, we show that negative news events affected Sanders and Clintons subscription trends negatively, while showing no effect for Donald Trump. This gives empirical credence to Trump’s controversial claim that he could “stand in the middle of 5th Avenue and shoot somebody and not lose any voters”. We offer a number of explanations for the observed phenomena: the nature of the content of the three subreddits, their cultural dynamics, and changing dynamics of partisanship. We posit that the ‘death of expertise’ expresses itself on Reddit as a switch in persuasion tactics from a policy-based to an emotions-based approach, and that group members’ agreement on policy proved a weak marker for online communities’ group identity and cohesion. We also claim that strong partisanship coupled with weak party affiliation among Millennials contributed to the low levels of Democratic support consolidation after Clinton won the nomination.

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Author information

Authors and Affiliations

  1. Department of Slavonic Studies, Faculty of Modern and Medieval Languages, University of Cambridge, Cambridge, CB39DA, UK

    Jon Roozenbeek

  2. Department of Engineering, Institute for Manufacturing, University of Cambridge, Cambridge, CB30FS, UK

    Adrià Salvador Palau

Authors
  1. Jon Roozenbeek
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  2. Adrià Salvador Palau
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Corresponding author

Correspondence to Jon Roozenbeek .

Editor information

Editors and Affiliations

  1. Indiana University, Bloomington, Indiana, USA

    Giovanni Luca Ciampaglia

  2. University of Washington, Seattle, Washington, USA

    Afra Mashhadi

  3. University of Oxford, Oxford, United Kingdom

    Taha Yasseri

A Appendix

A Appendix

Table 2 contains the list of news events used in this paper and their classification. The relevant variables are set in columns, starting by labels which we describe as follows:

  • Year, Month, Day: The date of the news event.

  • Label: A label describing the news event(s) in the given day.

  • Party: A numerical label used to draw the vertical lines in the figures. If equal to 0, represents a new event mostly relevant for the Republican party, if equal to 1, it represents a news event relevant for the Democratic party. If equal to 2 it represents a news event equally relevant for both.

  • PositiveH: A numerical label used to classify news events positive to Hillary Clinton’s campaign as described in Sect. 2. If equal to 1, the event is considered as positive. If equal to 0 as neutral.

  • NegativeH: A numerical label used to classify news events negative to Hillary Clinton’s campaign as described in Sect. 2. If equal to 1, the event is considered as negative. If equal to 0 as neutral.

  • PositiveT: A numerical label used to classify news events positive to Donald Trump’s campaign as described in Sect. 2. If equal to 1, the event is considered as positive. If equal to 0 as neutral.

  • NegativeT: A numerical label used to classify news events negative to Donald Trump’s campaign as described in Sect. 2. If equal to 1, the event is considered as negative. If equal to 0 as neutral.

  • PositiveS: A numerical label used to classify news events positive to Bernie Sanders campaign as described in Sect. 2. If equal to 1, the event is considered as positive. If equal to 0 as neutral.

  • NegativeS: A numerical label used to classify news events negative to Bernie Sanders campaign as described in Sect. 2. If equal to 1, the event is considered as negative. If equal to 0 as neutral.

Table 2. News events considered for the analysis of subscriber and visitor trends.
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Fig. 4.
figure 4

Graphical representation of Reddit demographics according to different sources [23, 31, 52]

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Table 3. News event occurrence versus subscriber change, per candidate
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Roozenbeek, J., Salvador Palau, A. (2017). I Read It on Reddit: Exploring the Role of Online Communities in the 2016 US Elections News Cycle. In: Ciampaglia, G., Mashhadi, A., Yasseri, T. (eds) Social Informatics. SocInfo 2017. Lecture Notes in Computer Science(), vol 10540. Springer, Cham. https://doi.org/10.1007/978-3-319-67256-4_16

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  • DOI: https://doi.org/10.1007/978-3-319-67256-4_16

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