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Data Journalism

Database journalism

Computer-assisted journalism

it's not about how many developers you have, but what you do with them. – Adrian Holovaty

Data journalism outcomes can range from visualization to long form articles. It is about the process how to turn numbers in to a story, whether the story is comprised of words or graphics is irrelevant. By knowing the structure of your team and balancing resources, you can start to think how best to organize your group.

From Doing Journalism with Data: Frist Steps, Skills and Tolls: Lession: Login required

Key-roles, you will probably need to do at least 2 of these:

  1. Research (4:58)
    • Most important role
    • On basis of a journalistic hunch, or by digging around in data
    • Time consuming
  2. Writing (5:58)
    • Numbers without context are just numbers, words give context
    • Example: Yearly guide to public spending by each government department (Links to an external site.) (The Guardian)
  3. Development and coding
    • Coders (programmers), can help with the research and visualization
    • Coordinate who can write and code
    • Example: Connected China (Links to an external site.) (Reuters)
  4. Designing and visualizing
    • Designers can make visualizations happen
    • Example: 99% vs 1% (Links to an external site.) (The Guardian)

Teams

  1. Lone rangers
  2. Two-person teams
  3. Small scale teams
  4. Larger teams
    • Has a deliberate strategy to create a new kind of online journalism
    • They help with finding ways to tell the story better
    • Part of the newsdesk

Readings in the Guardian

Writing

The Inverted Pyramid Structure at Purdue University

For data journalism, the 5 W's have never been so important:

  1. Who? (1:30)
    • Where did the data come from?
    • The most important W
    • Transparency about the source is critical
    • Do not blindly trust accuracy of supplied data
  2. What? (3:05)
    • What are you trying to say?
    • What points are you trying to get across?
    • Your stories are aimed at the general public
    • Example: Art Market for Dummies (Askmedia.fr)
    • Your job is to bridge the gap between the data and the user
  3. When? (4:28)
  1. Where? (07:53)
  2. Why? (8:48)
    • Hardest question
    • Data journalism is less good at correlating that data to produce a cause and effect analysis.

데이터저널리즘

오픈데이터저널리즘
빅데이터저널리즘
데이터중심(기반)저널리즘

Computer assisted = fact gathering
여기에 더 나아가 인터액티브한 결과물을 도출하는 과정
따라서
데이터저널리즘 =

  • 데이터의 수집, 정리, 분석, 시각화, 스토리화 등의 과정을 통해 저널리즘을 실천하는 행위들

데이터 기반한 인포그래픽, 스토리텔링 → 데이터마이닝, 데이터 전문가의 역할이 중요해 짐

필요성

  • 정보불평등의 해결책
  • 저널리즘의 개관성과 신뢰도 향상
    • http://hot.coroke.net 충격, 경악, 황당 등 자극적이고 무의미하며 선정적인 단어로 구성된 헤드라인 사용
  • 깊이 있는 정보 (사실과 사실 밑에 있는 것들 (embedded))

역할

  • 저널리즘 객관성 강화
  • 데이터의 공공적인 성격을 부각시켜 공개화 추구 (open 데이터) → 정부, 기업의 투명성
    • 국가안보, 국익, 프라이버시, 국민의 알권리 등과 결합하여 충돌
    • 개방에 걸림돌이 되고 있음 (Lehtonen, 2011)

데이터저널리즘과 관련된 주제

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data_journalism.txt · Last modified: 2016/12/13 13:55 by hkimscil