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Recovering from Scandals: Twitter Coverage of Oxfam and Save the Children Scandals

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Abstract

We examine Twitter data to assess the impact of media exposes on the reputations of two international nonprofits, Oxfam and Save the Children (STC). Using a random sample of 6794 Tweets, we study the daily gap between positive and negative sentiments expressed towards these organizations. The “unweighted gap” and the “weighted gap” (weighted by the number of followers) of the Twitter handle follow broadly the same trajectory with high fluctuation in response to new negative or positive media stories. Twitter handles with large audiences amplify variability in weighted gap. While Oxfam’s reputation did not fully recover to pre-Haiti levels even 6 months after the scandal, STC’s reputation returned to pre-scandal levels in 8 days, although it fluctuated in response to new revelations. Overall, reputation recovery for both organizations was aided when they received celebrity endorsements and focused public attention on their positive activities, especially by linking to visible global events.

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Notes

  1. https://www.theguardian.com/politics/2018/feb/18/brendan-cox-resigns-from-charities-amid-sexual-assault-claims.

  2. https://www.theguardian.com/society/2018/feb/22/unicef-deputy-justin-forsyth-quits-inappropriate-behaviour-claims.

  3. Guo and Saxton (2018) found that the attention an organization receives on Twitter is positively correlated with the number of followers it has and the volume of tweets it has sent. Thus, Twitter following is an important metric of an organization’s prominence in the public view.

  4. http://www.internetlivestats.com/twitter-statistics/.

  5. https://www.oxfamamerica.org/explore/about-oxfam/.

  6. https://www.bbc.com/news/uk-43031911.

  7. https://www.washingtonpost.com/news/worldviews/wp/2018/02/13/oxfam-prostitution-scandal-widens-to-at-least-three-countries/?noredirect=on&utm_term=.2518974dd10a.

  8. https://twitter.com/driverminnie/status/963532987886424064?lang=en.

  9. https://www.theguardian.com/global-development/2018/feb/15/desmond-tutu-resigns-oxfam-ambassador-immorality-claims.

  10. https://twitter.com/Mark_Goldring1/statuses/962693314603573249.

  11. https://twitter.com/griffinlawuk/statuses/964426805548478465.

  12. https://twitter.com/CholericCleric/statuses/964810445491142656.

  13. https://twitter.com/FinancialTimes/statuses/964378103907631104.

  14. https://twitter.com/itvnews/status/964249082737852417.

  15. https://twitter.com/JohnSimpsonNews/statuses/965978802106101761.

  16. https://en.wikipedia.org/wiki/Reputation_management#Online_reputation_management.

  17. https://www.brandwatch.com/.

  18. https://www.reputation.com/?utm_source=digitalinsights.

  19. http://www.sentimentmetrics.com/?utm_source=digitalinsights.

  20. In the context of ORM, brand-related Tweets present a number of challenges to automated sentiment analysis. Many studies note that Tweet polarity, as assessed by machines, does not necessarily correlate with sentiment towards an organization. Tweets may mention a brand but express a sentiment towards a different object or entity (Jiang et al., 2011).

  21. This was the original Web site: https://sifter.texifter.com/; Sifter has been discontinued starting September 2018.

  22. For example, we excluded commercial Tweets (such as advertising products on sale at Oxfam stores).

  23. We realized that the bulk of the Tweets focused on Oxfam and STC; clearly, these scandals had not imposed reputational spillovers on other charities as reflected in the Tweets directed at them. Hence, we decided to focus on Oxfam and STC only and not examine the issue of reputational spillovers.

  24. https://www.oxfam.org/en/pressroom/pressreleases/2018-01-22/richest-1-percent-bagged-82-percent-wealth-created-last-year.

  25. We observed many posts with sarcastic comments. However, a simple retweet (without any added message) typically indicates agreement with or support of the retweeted content. If this content was sarcastic, we coded the Tweet as “negative” (indeed, sarcasm can often only be detected by manual coding). As we demonstrate, our results are robust to the exclusion of retweets, ensuring that our interpretation of these posts does not affect our findings.

  26. https://twitter.com/Cristiano?ref_src=twsrc%5Egoogle%7Ctwcamp%5Eserp%7Ctwgr%5Eauthor.

  27. https://twitter.com/Camila_Cabello?ref_src=twsrc%5Egoogle%7Ctwcamp%5Eserp%7Ctwgr%5Eauthor.

  28. For a detailed review of various measures of influence on Twitter, see Riquelme and Gonzalez-Cantergiani (2016).

  29. https://twitter.com/EconomicTimes/statuses/966824935883653120.

  30. https://twitter.com/winnie_byanyima/status/972183633950896130.

  31. https://twitter.com/cristiano/status/964093317997826048?lang=en.

  32. https://twitter.com/Cristiano/statuses/973517278879854592.

  33. Both of these messages are depicted in Fig. 7.

  34. There might be an alternative explanation about STC rebound: since neither Cox nor Forsyth was associated with STC at the time of the media expose, the scandal could not be blamed on STC, enabling the quick rebound. However, we would like to note that even in the context of Oxfam, Haiti’s country director, Roland van Hauwermeiren, had left the organization in 2011, well before the scandal broke out. Hence, the key actors accused of inappropriate behavior had left their respective organizations well before 2018. Arguably, the thrust of the scandal was about organizational failure and how internally recognized moral actors allowed their key managers to function in inappropriate ways, and even tried to cover up their behaviors.

References

  • Agarwal, A., Xie, B., Vovsha, I., Rambow, O., & Passonneau, R. (2011). Sentiment analysis of twitter data. In Proceedings of the workshop on languages in social media (pp. 30–38). Association for Computational Linguistics.

  • Alhidari, I. S., Veludo-de-Oliveira, T. M., Yousafzai, S. Y., & Yani-de-Soriano, M. (2018). Modeling the effect of multidimensional trust on individual monetary donations to charitable organizations. Nonprofit and Voluntary Sector Quarterly,47(3), 623–644.

    Article  Google Scholar 

  • Amigó, E., Corujo, A., Gonzalo, J., Meij, E., & de Rijke, M. (2012). Overview of RepLab 2012: Evaluating online reputation management systems. In CLEF (Online Working Notes/Labs/Workshop).

  • Amigó, E., De Albornoz, J. C., Chugur, I., Corujo, A., Gonzalo, J., Martín, T., Meij, E., de Rijke, M., & Spina, D. (2013). Overview of RepLab 2013: Evaluating online reputation monitoring systems. In International conference of the cross-language evaluation forum for european languages (pp. 333–352). Berlin: Springer.

  • Bao, Y., Quan, C., Wang, L., & Ren, F. (2014). The role of pre-processing in Twitter sentiment analysis. In International conference on intelligent computing (pp. 615–624). Cham: Springer.

  • Barbosa, L., & Feng, J. (2010, August). Robust sentiment detection on twitter from biased and noisy data. In Proceedings of the 23rd international conference on computational linguistics: posters (pp. 36–44). Association for Computational Linguistics.

  • Bekkers, R. (2003). Trust, accreditation, and philanthropy in the Netherlands. Nonprofit and Volun-tary Sector Quarterly,32, 596–615.

    Article  Google Scholar 

  • Bennett, R., & Gabriel, H. (2003). Image and reputational characteristics of UK charitable organizations: An empirical study. Corporate Reputation Review,6(3), 276–289.

    Article  Google Scholar 

  • Berghmans, M., Simons, M., & Vandenabeele, J. (2017). What is negotiated in negotiated accountability? The case of INGOs. VOLUNTAS: International Journal of Voluntary and Nonprofit Organizations,28(4), 1529–1561.

    Article  Google Scholar 

  • Bollen, J., Gonçalves, B., Ruan, G., & Mao, H. (2011). Happiness is assortative in online social networks. Artificial Life,17(3), 237–251.

    Article  Google Scholar 

  • Borge-Hoelthoefer, J., & Moreno, Y. (2012). Absence of influential spreaders in rumor dynamics. Physical Review E,85(026116), 1–5.

    Google Scholar 

  • Carpenter, D. P., & Krause, G. A. (2012). Reputation and public administration. Public Administration Review,72(1), 26–32.

    Article  Google Scholar 

  • Castillo, C., El-Haddad, M., Pfeffer, J., & Stempeck, M. (2014). Characterizing the life cycle of online news stories using social media reactions. In Proceedings of the 17th ACM conference on computer supported cooperative work & social computing (pp. 211–223). ACM.

  • Coombs, W. T. (2007). Protecting organization reputations during a crisis: The development and application of situational crisis communication theory. Corporate Reputation Review,10(3), 163–176.

    Article  Google Scholar 

  • Coombs, W. T., & Holladay, S. J. (2006). Unpacking the halo effect: Reputation and crisis management. Journal of Communication Management,10(2), 123–137.

    Article  Google Scholar 

  • Coombs, W. T., & Holladay, S. J. (2012). Amazon.com’s Orwellian nightmare: Exploring apology in an online environment. Journal of Communication Management,16(3), 280–295.

    Article  Google Scholar 

  • Cottle, S., & Nolan, D. (2007). Global Humanitarianism and the changing media aid filed. Journalism Studies,8(6), 862–878.

    Article  Google Scholar 

  • Downs, A. (1972). Up and down with ecology: The “issue-attention cycle”. Public Interest, 28, 38–50.

    Google Scholar 

  • Dupuy, K., & Prakash, A. (2018). Do donors reduce bilateral aid to countries with restrictive NGO laws? A panel study, 1993–2012. Nonprofit and Voluntary Sector Quarterly,47(1), 89–106.

    Article  Google Scholar 

  • Fombrun, C. J., & Van Riel, C. B. (1997). The reputational landscape. Corporate reputation eview,1(2), 5–13.

    Article  Google Scholar 

  • Gibelman, M., & Gelman, S. R. (2004). A loss of credibility: Patterns of wrongdoing among nongovernmental organizations. VOLUNTAS: International Journal of Voluntary and Nonprofit Organizations,15(4), 355–381.

    Article  Google Scholar 

  • Go, A., Bhayani, R., & Huang, L. (2009). Twitter sentiment classification using distant supervision. CS224 N Project Report, Stanford (Vol. 1 No. 12).

  • Gonçalves, P., Araújo, M., Benevenuto, F., & Cha, M. (2013). Comparing and combining sentiment analysis methods. In Proceedings of the first ACM conference on online social networks (pp. 27–38). ACM.

  • Green, D. P., Zelizer, A., & Kirby, D. (2018). Publicizing scandal: Results from five field experiments. Quarterly Journal of Political Science,13(3), 237–261.

    Article  Google Scholar 

  • Gunningham, N., Kagan, R. A., & Thornton, D. (2004). Social license and environmental protection: why businesses go beyond compliance. Law & Social Inquiry,29(2), 307–341.

    Article  Google Scholar 

  • Guo, C., & Saxton, G. D. (2014). Tweeting social change: How social media are changing nonprofit advocacy. Nonprofit and Voluntary Sector Quarterly,43(1), 57–79.

    Article  Google Scholar 

  • Guo, C., & Saxton, G. D. (2018). Speaking and being heard: How nonprofit advocacy organizations gain attention on social media. Nonprofit and Voluntary Sector Quarterly,47(1), 5–26.

    Article  Google Scholar 

  • Hansmann, H. (1980). The role of nonprofit enterprise. Yale Law Review,89, 835–898.

    Article  Google Scholar 

  • Jansen, B. J., Zhang, M., Sobel, K., & Chowdury, A. (2009). Twitter power: Tweets as electronic word of mouth. Journal of the American Society for Information Science and Technology,60(11), 2169–2188.

    Article  Google Scholar 

  • Java, A., Song, X., Finin, T., & Tseng, B. (2007). Why we twitter: understanding microblogging usage and communities. In Proceedings of the 9th WebKDD and 1st SNA-KDD 2007 workshop on Web mining and social network analysis (pp. 56–65). ACM.

  • Jiang, L., Yu, M., Zhou, M., Liu, X., & Zhao, T. (2011). Target-dependent twitter sentiment classification. In Proceedings of the 49th annual meeting of the association for computational linguistics: Human language technologies (Vol. 1, pp. 151–160). Association for Computational Linguistics.

  • Jones, G. H., Jones, B. H., & Little, P. (2000). Reputation as reservoir: Buffering against loss in times of economic crisis. Corporate Reputation Review,3(1), 21.

    Article  Google Scholar 

  • Keck, M. E., & Sikkink, K. (1998). Activists beyond borders: Advocacy networks in international politics. Ithaca: Cornell University Press.

  • King, G., Schneer, B., & White, A. (2017). How the news media activate public expression and influence national agendas. Science,358(6364), 776–780.

    Article  Google Scholar 

  • Kouloumpis, E., Wilson, T., & Moore, J. D. (2011). Twitter sentiment analysis: The good the bad and the omg! Icwsm,11(538–541), 164.

    Google Scholar 

  • Kwak, H., Lee, C., Park, H., & Moon, S. (2010). What is Twitter, a social network or a news media?. In Proceedings of the 19th international conference on World wide web (pp. 591–600). ACM.

  • Lange, D., Lee, P. M., & Dai, Y. (2011). Organizational reputation: A review. Journal of Management,37(1), 153–184.

    Article  Google Scholar 

  • Light, P. C. (2008). How Americans view charities: A report on charitable confidence, 2008. Washington, D.C: Brookings Institution.

  • Lovejoy, K., & Saxton, G. D. (2012). Information, community, and action: How nonprofit organizations use social media. Journal of Computer-Mediated Communication,17(3), 337–353.

    Article  Google Scholar 

  • Mishina, Y., Block, E. S., & Mannor, M. J. (2012). The path dependence of organizational reputation: How social judgment influences assessments of capability and character. Strategic Management Journal,33(5), 459–477.

    Article  Google Scholar 

  • Mohammad, S. M., Kiritchenko, S., & Zhu, X. (2013). NRC-Canada: Building the state-of-the-art in sentiment analysis of tweets. arXiv preprint arXiv:1308.6242.

  • Pak, A., & Paroubek, P. (2010). Twitter as a corpus for sentiment analysis and opinion mining. In LREc (Vol. 10, No. 2010, pp. 1320–1326).

  • Papacharissi, Z., & de Fatima Oliveira, M. (2012). Affective news and networked publics: The rhythms of news storytelling on #Egypt. Journal of Communication,62(2), 266–282.

    Article  Google Scholar 

  • Prakash, A., & Gugerty, M. K. (2010). Trust but verify? Voluntary regulation programs in the nonprofit sector. Regulation Governance, 4(1), 22–47.

    Google Scholar 

  • Riquelme, F., & Gonzalez-Cantergiani, P. (2016). Measuring user influence on Twitter: A survey. Information Processing and Management,52, 949–975.

    Article  Google Scholar 

  • Rose-Ackerman, S. (1996). Altruism, nonprofits and economic theory. Journal of Economic Literature,34, 701–728.

    Google Scholar 

  • Savedoff, W., Glassman, A., & Madan, J. (2016). Global health, aid and corruption: Can we escape the scandal cycle? (Vol. 86). CGD Policy Paper.

  • Schultz, F., Utz, S., & Göritz, A. (2011). Is the medium the message? Perceptions of and reactions to crisis communication via twitter, blogs and traditional media. Public Relations Review,37(1), 20–27.

    Article  Google Scholar 

  • Sloan, M. (2009). The effects of nonprofit accountability ratings on donor behavior. Nonprofit and Voluntary Sector Quarterly,38, 220–236.

    Article  Google Scholar 

  • Smith, S. R., & Lipsky, M. (1993). Nonprofits for hire. Cambridge: Harvard University Press.

    Google Scholar 

  • Stavrakantonakis, I., Gagiu, A. E., Kasper, H., Toma, I., & Thalhammer, A. (2012). An approach for evaluation of social media monitoring tools. Common Value Management,52(1), 52–64.

    Google Scholar 

  • Stever, G. S., & Lawson, K. (2013). Twitter as a way for celebrities to communicate with fans: Implications for the study of parasocial interaction. North American journal of psychology,15(2), 339–354.

    Google Scholar 

  • Svensson, P. G., Mahoney, T. Q., & Hambrick, M. E. (2015). Twitter as a communication tool for nonprofits: A study of sport-for-development organizations. Nonprofit and Voluntary Sector Quarterly,44(6), 1086–1106.

    Article  Google Scholar 

  • Szper, R., & Prakash, A. (2011). Charity watchdogs and the limits of information-based regulation. VOLUNTAS: International Journal of Voluntary and Nonprofit Organizations,22(1), 112–141.

    Article  Google Scholar 

  • Thøgersen, J. (2006). Media attention and the market for ‘green’ consumer products. Business Strategy and the Environment,15(3), 145–156.

    Article  Google Scholar 

  • Torres-Moraga, E., Vásquez-Parraga, A., & Barra, C. (2010). Antecedents of donor trust in an emerging charity sector: the role of reputation, familiarity, opportunism and communication. Transylvanian Review of Administrative Sciences,6(29), 159–177.

    Google Scholar 

  • Vasterman, P. L. (2005). Media-hype: Self-reinforcing news waves, journalistic standards and the construction of social problems. European Journal of Communication,20(4), 508–530.

    Article  Google Scholar 

  • Weisbrod, B. (1988). The nonprofit economy. Cambridge, MA: Harvard University Press.

    Google Scholar 

  • Wien, C., & Elmelund-Præstekær, C. (2009). An anatomy of media hypes: Developing a model for the dynamics and structure of intense media coverage of single issues. European Journal of Communication,24(2), 183–201.

    Article  Google Scholar 

  • Yu, T., & Lester, R. H. (2008). Moving beyond firm boundaries: A social network perspective on reputation spillover. Corporate Reputation Review,11(1), 94–108.

    Article  Google Scholar 

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Acknowledgement

We gratefully acknowledge guidance and help from Chao Guo, Gregory Saxton, Andranik Tumasjan, Javier Borge Holthoefer, Jim Jansen, Kate Starbird, and Stuart Shulman.

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Authors and Affiliations

  1. University of Chicago, Chicago, USA

    Rebecca Scurlock

  2. University of Washington, Seattle, USA

    Nives Dolsak & Aseem Prakash

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  1. Rebecca Scurlock
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Correspondence to Aseem Prakash.

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Scurlock, R., Dolsak, N. & Prakash, A. Recovering from Scandals: Twitter Coverage of Oxfam and Save the Children Scandals. Voluntas 31, 94–110 (2020). https://doi.org/10.1007/s11266-019-00148-x

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