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Towards a critical data journalism practice

Towards a critical data journalism practice

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In this talk is offer three challenges for a critical data journalism practice drawing on the insights and examples from The Data Journalism Handbook: Towards a Critical Data Practice: https://www.aup.nl/en/book/9789462989511/the-data-journalism-handbook. The talk is a keynote given at the Digital Methods Initiative Summer School at the University of Amsterdam on 5 July 2021.

In this talk is offer three challenges for a critical data journalism practice drawing on the insights and examples from The Data Journalism Handbook: Towards a Critical Data Practice: https://www.aup.nl/en/book/9789462989511/the-data-journalism-handbook. The talk is a keynote given at the Digital Methods Initiative Summer School at the University of Amsterdam on 5 July 2021.

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Towards a critical data journalism practice

  1. 1. Towards a critical data journalism practice Liliana Bounegru @bb_liliana / lilianabounegru.org Lecturer in Digital Methods King’s College London
  2. 2. “Across the world journalists were discovering new ways to work by telling data-led stories in innovative ways. … This should not be seen as an isolated development within the field of journalism. These were just the effects of huge developments in international transparency beyond the setting up of open data portals. … They also included increased access to powerful free data visualization and cleaning tools, such as OpenRefine, Google Fusion Tables, Many Eyes, Datawrapper, Tableau Public and more. Those free tools combined with access to a lot of free public data facilitated the production of more and more public-facing visualizations and data projects.” – Rogers, Simon. 2021. “From The Guardian to Google News Lab: A Decade of Working in Data Journalism.” The Data Journalism Handbook: Towards A Critical Data Practice.
  3. 3. Three challenges for critical data journalism practice
  4. 4. 1. Make stories both with and about data 2. Align with marginalised issues and actors 3. Cultivate reflexive ways of telling
  5. 5. 1. Make stories both with and about data 2. Align with marginalised issues and actors 3. Cultivate reflexive ways of telling
  6. 6. “… the crucial role of data journalists as users and critics of data.” – Emmanuel Didier, Ecole normale supérieure; author of America by the Numbers: Quantification, Democracy, and the Birth of National Statistics
  7. 7. “Taking a feminist approach to data journalism means tuning in to the ways in which inequality enters databases and algorithms, as well as developing strategies to mitigate those biases.” –D’Ignazio, Catherine. 2021. “Data Journalism: What’s Feminism Got to Do With I.T.?” The Data Journalism Handbook: Towards A Critical Data Practice.
  8. 8. “… data journalism can serve not just to reinforce and reify dominant regimes of datafication – or ways of rendering life into data (van Dijck, 2014) – but also to interrogate them and make space for public involvement and intervention around data infrastructures.” – Gray, J., & Bounegru, L. (2019). What a difference a dataset makes? Data journalism and/as data activism. In J. Evans, S. Ruane, & H. Southall (Eds.), Data in Society: Challenging Statistics in an Age of Globalisation.
  9. 9. Algorithmic accountability reporting
  10. 10. “… a re-orientation of the traditional watchdog function of journalism towards the power wielded through algorithms (Diakopoulos, 2015).” –Diakopoulos, Nicholas. 2021. “The Algorithms Beat: Angles and Methods for Investigation.” The Data Journalism Handbook: Towards A Critical Data Practice.
  11. 11. “… at least four driving forces … appear to underlie many algorithmic accountability stories: (a) discrimination and unfairness, (b) errors or mistakes in predictions or classifications, (c) legal or social norm violations, and (d) misuse of algorithms by people either intentionally or inadvertently.” –Diakopoulos, Nicholas. 2021. “The Algorithms Beat: Angles and Methods for Investigation.” The Data Journalism Handbook: Towards A Critical Data Practice.
  12. 12. Reconfiguring infrastructures for making data journalism
  13. 13. “… There are consequences to having very few actors running such platforms and large numbers of journalists depending on them in the cross-border journalism realm. One of these could be understood as what in the landscape of ‘big tech’ has been called a 'hyper-modern form of feudalism’ based on data ownership (Morozov, 2016). This concept draws attention to how total control of users’ data and interactions is placed in the hands of a few companies who face no competition.” – Cândea, Ştefan. 2021 “Data Feudalism: How Platforms Shape Cross-Border Investigative Networks.” The Data Journalism Handbook: Towards A Critical Data Practice.
  14. 14. How can data journalism projects tell stories both with and about data including the various actors, processes, institutions, infrastructures and forms of knowledge through which data is made? How can data journalism projects account for the collective character of digital data, platforms, algorithms and online devices, including the interplay between digital technologies and digital cultures? How can data journalism projects cultivate their own ways of making things intelligible, meaningful and relatable through data, without simply uncritically advancing the ways of knowing “baked into” data from dominant institutions, infrastructures and practices? 1. Make stories both with and about data
  15. 15. 1. Make stories both with and about data 2. Align with marginalised issues and actors 3. Cultivate reflexive ways of telling
  16. 16. Data journalism by whom, for whom and in whose interests?
  17. 17. “… the role of journalism in maintaining social orders that support state aims and goals and structures and ideologies such as patriarchy, settler colonialism and White supremacy (Callison & Young, 2020).” – Young, Mary Lynn, and Candis Callison. 2021. “Data Journalism: In Whose Interests?” The Data Journalism Handbook: Towards A Critical Data Practice.
  18. 18. “Indigenous peoples have also been subject to contending with extensive anthropological and government archives and consistent media misrepresentations and stereotypes (Anderson & Robertson, 2011) in the service of varied forms and histories of settler colonialism (Tuck & Yang, 2012; Wolfe, 2006). Hence, the stakes for data journalism specifically as an extension of notions of machine-based objectivity are profound.” – Young, Mary Lynn, and Candis Callison. 2021. “Data Journalism: In Whose Interests?” The Data Journalism Handbook: Towards A Critical Data Practice.
  19. 19. “Walter (2016) has termed these data 5D data: Data that focus on Difference, Disparity, Disadvantage, Dysfunction and Deprivation.” – Kukutai, Tahu, and Maggie Walter. 2021. “Indigenous Data Sovereignty: Implications for Data Journalism.” The Data Journalism Handbook: Towards A Critical Data Practice.
  20. 20. “Indigenous data deserts” – Kukutai, Tahu, and Maggie Walter. 2021. “Indigenous Data Sovereignty: Implications for Data Journalism.” The Data Journalism Handbook: Towards A Critical Data Practice.
  21. 21. “Laguna Pueblo journalist Jenni Monet (2020) characterizes Indigenous communities in the United States as ‘Asterisk nations,’ which are those for whom no data exists.” – Young, Mary Lynn, and Candis Callison. 2021. “Data Journalism: In Whose Interests?” The Data Journalism Handbook: Towards A Critical Data Practice.
  22. 22. “… ID-SOV [Indigenous data sovereignty] as an emerging site of science and activism.” – Kukutai, Tahu, and Maggie Walter. 2021. “Indigenous Data Sovereignty: Implications for Data Journalism.” The Data Journalism Handbook: Towards A Critical Data Practice.
  23. 23. “ID-SOV research and networks … represent valuable sources of data and data expertise that can inform more equitable, critical and just approaches to journalism involving Indigenous peoples and issues.” – Kukutai, Tahu, and Maggie Walter. 2021. “Indigenous Data Sovereignty: Implications for Data Journalism.” The Data Journalism Handbook: Towards A Critical Data Practice.
  24. 24. – Ma, Yolanda Jinxin. 2021. “Alternative Data Practices in China.” The Data Journalism Handbook: Towards A Critical Data Practice.
  25. 25. How can data journalism projects make space for public participation and intervention in interrogating established data sources and re-imagining which issues are accounted for through data, and how? How can data journalism projects collaborate around transnational issues in ways which avoid the logic of the platform and the colony, and affirm innovations at the periphery? How can data journalism support marginalized communities to use data to tell their own stories on their own terms, rather than telling their stories for them? 2. Align with marginalised issues and actors
  26. 26. 1. Make stories both with and about data 2. Align with marginalised issues and actors 3. Cultivate reflexive ways of telling
  27. 27. “An empirically self-assured profession.” – Anderson, C. W. 2021. “Genealogies of Data Journalism.” The Data Journalism Handbook: Towards A Critical Data Practice.
  28. 28. “Epistemologically, there is an increasing belief amongst computational journalists that digital facts in some way ‘speak for themselves,’ or at least these facts will do so when they have been properly collected, sorted and cleaned.” – Anderson, C. W. 2021. “Genealogies of Data Journalism.” The Data Journalism Handbook: Towards A Critical Data Practice.
  29. 29. “ … [a genealogy of data journalism] would prompt a useful form of critical self-reflexivity, one that might help mitigate the (understandable and often well- deserved) self-confidence of working data journalists and reporters.” – Anderson, C. W. 2021. “Genealogies of Data Journalism.” The Data Journalism Handbook: Towards A Critical Data Practice.
  30. 30. Provisionality and uncertainty
  31. 31. “… journalism ought to rethink the means and mechanisms by which it conveys its own provisionality and uncertainty.” – Anderson, C. W. 2021. “Genealogies of Data Journalism.” The Data Journalism Handbook: Towards A Critical Data Practice.
  32. 32. “In particular, data journalists might think harder about how to creatively represent uncertainty in their empirical work.” – Anderson, C. W. 2021. “Genealogies of Data Journalism.” The Data Journalism Handbook: Towards A Critical Data Practice.
  33. 33. “As I started to do them [sketches with data] I had this realization that they could be quite an effective way to communicate the uncertainty of data projects. They could remind people that a human was responsible for making all of these design decisions.” – Chalabi, Mona, and Jonathan Gray. 2021. “Sketching With Data.” The Data Journalism Handbook: Towards A Critical Data Practice.
  34. 34. “It is not really perfect: To fit all of the rhinos in the scale is a little bit questionable I would say … But it makes you feel something about the numbers. And it is also transparent about its shortcomings. … When readers look at the illustrations of the endangered species they can look at the rhinos and think, ‘It is a little bit off but I get it.’ They have access to that critique in a way that they don’t with computer generated graphics.” – Chalabi, Mona, and Jonathan Gray. 2021. “Sketching With Data.” The Data Journalism Handbook: Towards A Critical Data Practice.
  35. 35. The role of emotions
  36. 36. “Part of the beauty of data visualization is that it can make things feel more visceral.” – Chalabi, Mona, and Jonathan Gray. 2021. “Sketching With Data.” The Data Journalism Handbook: Towards A Critical Data Practice.
  37. 37. “… a major finding from our research was the important role that emotions play in people’s engagements with data visualizations.” – Kennedy, Helen, et al. 2021. “Data Visualizations: Newsroom Trends and Everyday Engagements.” The Data Journalism Handbook: Towards A Critical Data Practice.
  38. 38. “First, analytics dashboards have important emotional dimensions that are too often overlooked. … The power and appeal of metrics are significantly grounded in the data’s ability to elicit particular feelings, such as excitement, disappointment, validation and reassurance.” – Petre, Caitlin. 2021. “Data-Driven Editorial?: Considerations for Working With Audience Metrics.” The Data Journalism Handbook: Towards A Critical Data Practice.
  39. 39. “Chartbeat knew that this emotional valence was a powerful part of the dashboard’s appeal, and the company included features to engender emotions in users. For instance, the dashboard was designed to communicate deference to journalistic judgement, cushion the blow of low traffic and provide opportunities for celebration in newsrooms.” – Petre, Caitlin. 2021. “Data-Driven Editorial?: Considerations for Working With Audience Metrics.” The Data Journalism Handbook: Towards A Critical Data Practice.
  40. 40. “As with every other communications medium, leveraging emotion in data comes with ethical responsibilities.” – D’Ignazio, Catherine. 2021. “Data Journalism: What’s Feminism Got to Do With I.T.?” The Data Journalism Handbook: Towards A Critical Data Practice.
  41. 41. Plurality and situatedness
  42. 42. “data journalists critical of digital universalist frameworks should aim … to consciously diversify data sources and decentre methods that would privilege ‘big data’ as the exclusive or most legitimate key to mapping empirical events and social realities.” – Chan, Anita Say. 2021. “Data Journalism, Digital Universalism and Innovation in the Periphery.” The Data Journalism Handbook: Towards A Critical Data Practice.
  43. 43. “Moves towards a ‘decolonization of knowledge’ underscore the significance of the diverse ways through which citizens and researchers in the Global South are engaging in bottom-up data practices. These practices leverage an emphasis on community practices and human-centred means of assessing and interpreting data—for social change, as well as speaking for the resistances to uses of big data that increase oppression, inequality and social harm.” – Chan, Anita Say. 2021. “Data Journalism, Digital Universalism and Innovation in the Periphery.” The Data Journalism Handbook: Towards A Critical Data Practice.
  44. 44. “Data journalists critical of digital universalism’s new extensions in data universalism should take heart to find allies and resonant concerns for developing accountable and responsible data practices with scholars in critical data studies, algorithm studies, software and platform studies, and postcolonial computing.” – Chan, Anita Say. 2021. “Data Journalism, Digital Universalism and Innovation in the Periphery.” The Data Journalism Handbook: Towards A Critical Data Practice.
  45. 45. – Muñoz, Eliana A. Vaca. 2021. “Organizing Data Projects With Women and Minorities in Latin America.” The Data Journalism Handbook: Towards A Critical Data Practice.
  46. 46. How might data journalists cultivate and consciously affirm their own styles of working with data, which may draw on, yet remain distinct from areas such as statistics, data science and social media analytics? How might data journalism develop a style of objectivity which affirms, rather than minimizes, its own role in intervening in the world and in shaping relations between different actors in collective life? How can data journalism projects tell stories about big issues at scale (e.g., climate change, inequality, multinational taxation, migration) while also affirming the provisionality and acknowledging the models, assumptions and uncertainty involved in the production of numbers? 3. Cultivate reflexive ways of telling
  47. 47. 1. Make stories both with and about data 2. Align with marginalised issues and actors 3. Cultivate reflexive ways of telling
  48. 48. Thank you! Liliana Bounegru @bb_liliana / lilianabounegru.org Lecturer in Digital Methods King’s College London

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