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Twitter-Based Social Accountability Processes: The Roles for Financial Inscriptions-Based and Values-Based Messaging

Abstract

Social media is changing social accountability practices. The release of the Panama Papers on April 3, 2016 by the International Consortium of Investigative Journalists (ICIJ) unleashed a tsunami of over 5 million tweets decrying corrupt politicians and tax-avoiding business elites, calling for policy change from governments, and demanding accountability from corporate and private tax avoiders. The current study uses 297,000+ original English-language geo-codable tweets with the hashtags #PanamaGate, #PanamaPapers, or #PanamaLeaks to examine the trajectory of Twitter-based social accountability conversations and the potential for the emergence of a longer-term social accountability user network. We propose that it is the combination of financial inscriptions and evaluative ethical utterances that incite and sustain social accountability conversations and social accountability networks. Financial inscriptions simultaneously remind audiences of both the information event that fomented the initial public reaction and the monetary magnitude of the event. Value-based ethical messaging, in turn, enunciates an ethical stance that simultaneously evaluates existing practices and emphasizes the need for accountability. It is the combining of these two types of messaging that helps to construct and sustain a normative narrative about social accountability. The results illustrate how the repetition and re-working of these two forms of messaging facilitated the construction of a normative narrative that coalesced into a social accountability network which persisted beyond the initial Panama Paper information event and which was re-activated in 2017 when the ICIJ published the Paradise Papers.

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Data Availability

Data are available from the public sources cited in the text.

Notes

  1. 1.

    The Panama Papers consisted of 11.5 million documents and 2.6 TB of data which contained details on the wealth accumulation and concealment activities of politicians, businesspeople and sports stars that were avoiding taxes by exploiting secretive offshore tax regimes. The Guardian, for example, notes that “twelve national leaders are among 143 politicians, their families and close associates from around the world known to have been using offshore tax havens” (Harding, 2016).

  2. 2.

    The Paradise Papers constitute 13.4 million confidential documents on tax haven activities that were made public by the ICIJ on November 5, 2017. The documents showed the “tax engineering activities of more than 100 multinational corporations, including Apple, Nike and the Botox-maker Allergan” as well as the “offshore interests and activities of more than 120 politicians and world leaders, including Queen Elizabeth II, and 13 advisers, major donors and members of U.S. President Donald J. Trump’s administration” (ICIJ, 2019).

  3. 3.

    For additional background on the Panama Papers, including insights into the key actors involved, the deals that were leaked, how the offshore tax evasion schemes worked, and consequences of the leak, see Trautman (2017) and Obermayer and Obermaier (2016). For an examination of speech genres and illocutionary force in Spanish-language #PanamaPapers tweets, see Neu et al. (2020). For an academic discussion of tax evasion and avoidance, please see the growing body of critical research on tax in interdisciplinary accounting (e.g., Anesa et al., 2019; Radcliffe et al., 2018).

  4. 4.

    There is a growing complementary literature on the role of Wikileaks in challenging existing forms of neoliberal governance (cf. Andrew & Baker, 2020; Munro, 2017). These studies offer a different, yet complementary, perspective on the role information leaks in inciting grassroots activism.

  5. 5.

    In related research, the linguistic aspects of communication have also been studied in the sustainability (e.g., Cho et al., 2010; Rodrigue et al., 2015) and business ethics literatures (e.g., Blanc et al., 2019; Lucas & Fyke, 2014; Neu et al., 2020; Winkler, 2011).

  6. 6.

    At the same time, broader “network” approaches—those that examine networks without explicitly using social network analysis tools—are also becoming increasingly popular (Mouritsen & Thrane, 2006).

  7. 7.

    Bruns and Stieglitz (2012) argue network participation can typically be understood according to a 1-9-90 breakdown, with “lead” users being those who are in the 99th percentile in terms of number of messages sent, “highly active users” are those who are in the next 9 percentiles (the 90th–98th percentiles), and “least active” users are those in the bottom 90th percentile.

  8. 8.

    In line with these ideas, the subsequent analyses are explicitly multi-method in nature, combining quantitative and qualitative analyses of the social media data as in Suddaby et al. (2015). Our first research question focuses on quantitative regression analyses, our second question on quantitative comparisons and qualitative and quantitative social network analysis (e.g., Richardson, 2009) measures, our third on qualitative and quantitative textual analyses (e.g., Moreno et al., 2019), and our fourth on quantitative comparisons. Also in line with our overall methodological approach, we approached our four research questions with a general analysis plan, but allowed the specific methods to evolve as inductive insights emerged from our initial analyses. Collectively, our multi-method analyses permit us to shed light on how accounting participates in the social accountability processes sparked by the release of the Panama Papers.

  9. 9.

    The coefficient on the interactive term Values-Based Words Financial Inscriptions in Model 4, meanwhile, is negative. This result is not surprising since we did not expect the efficacy of the two types of messaging to depend on the presence of both within the same tweet, but rather on the continuing presence of the two types of messaging within the conversation stream.

  10. 10.

    Within social media settings, conversations can drift and be disrupted as well as atrophy as participants turn their attention to other topics (Neu et al., 2020).

  11. 11.

    In Table 8 we show three measures of lexical diversity. Our first score, lexical diversity, is a basic measure reflecting the proportion of all words that are unique. The other two measures, MTLD (Measure of Textual Lexical Diversity) and HD-D (hypergeometric distribution diversity) are recommended by McCarthy and Jarvis (2010) insofar as they correct for the different lengths of the monthly samples of financial inscription tweets.

  12. 12.

    The application of textual analysis software such as LIWC (Linguistic Inquiry and Word Count) has become an increasingly common approach to uncovering hidden characteristics in text (Yekini et al., 2016). Our use of the LIWC dictionaries is similar to that employed in other recent accounting studies (Chen & Loftus, 2019; Hope & Wang, 2018; Moreno et al., 2019). For further details please refer to www.liwc.net.

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Funding

The funding provided by SSHRC is gratefully acknowledged as are the comments of our colleagues, participants at the 2017 Critical Perspectives on Accounting conference, and attendees at the Schulich School of Business research seminar.

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Correspondence to Gregory D. Saxton.

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Saxton, G.D., Neu, D. Twitter-Based Social Accountability Processes: The Roles for Financial Inscriptions-Based and Values-Based Messaging. J Bus Ethics (2021). https://doi.org/10.1007/s10551-021-04952-8

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Keywords

  • Big data
  • Ethical values
  • Financial inscriptions
  • Normative narratives
  • #PanamaPapers
  • Social accountability
  • Social media
  • Tax avoidance