Big Data for Media Development

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  • In the past year Internews has been setting up several crowdsourcing mapping project and working in different countries trying to match information collected through traditional media channels and information collected from the so called crowd. Those projects has been implemented in different type of environments, at times in the humanitarian context, at times in election contexts, at times for transparency and accountability projects.
  • Today, we are seeing the merging of those verification technics with computing based systems, mechanical turks, automated systems, where the amount of time and human resources needed to implement verification protocols is becoming smaller and the verification itself is becoming cheaper. Those new systems are getting us closer to the truth.
  • Big Data for Media Development

    1. 1. Big Data and Data Verification What does big data mean to Internews?
    2. 2. What is Big Data? In information technology, big data is a collection of data sets so large and complex that it becomes difficult to process using “normal” database management tools or traditional data processing applications. The challenges of processing this data include how to capture, to curate, to storage, to search, to share, to analyze, and to visualize it. The appeal to use larger data sets is driven by the additional information available from analysis of a single large set of related data, as compared to separate smaller sets with the same total amount of data, allowing correlations to be found to spot trends, determine quality of research, run preventive analysis, spot correlations, model risks, and determine real-time impacts of events.
    3. 3. WHAT DOES BIG DATA HAS TO DO WITH INTERNEWS?
    4. 4. Data verification
    5. 5. ELECTUA.ORG
    6. 6. Falsifying/Verifying the context Falsifying/Verifying the content Falsifying/Verifying the source
    7. 7. Social Media ID ID of the Content on the Social media Crowdsourcing Direct engagement Media authentication Verification of the source Timing Influence Location ID of the Network ID of the trusting network
    8. 8. Language Crowdsourcing Content ID on Social Network Pictures/Video Verification of the content Time and place Triangulation Follow up with source
    9. 9. Factual context Crowdsourcing Verification the context Time and Location Global Context
    10. 10. Different technics to verify 1. 2. 3. 4. Separated Trusted and untrusted sources Trusted sources verify untrusted sources Verification protocols and technics Putting the burden of the verification process on the user
    11. 11. Falsification is always possible… ..but so is verification!
    12. 12. + = TRUTH….?
    13. 13. The question is not if we can verify crowdsourced information or use big data in our work, the question is how do we make the verified data for decision makers
    14. 14. THANK YOU ANAHI AYALA IACUCCI AAYALA@INTERNEWS.ORG @INFO_INNOVATION @ANAHI_AYALA

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