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Designing a Model for Community Participation

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Abirami: Researcher

Avatar: Official proposal Official proposal
16/03/2021 12:35  

What if:

Scenario 4: What if as a (role) researcher I could (action) analyze data in the participatory platform with (Tools/Data/features from the inDICEs observatory) the inDICEs (webLyzard) dashboard so that (outcome/Impact) allow to analyze and compare data from the participatory platform and the external world.

Persona

  • Goals: finding specific metrics that demonstrate impact and allow him to quantify and compare good practices with ineffective in terms of UX interaction design in the Arts
  • Background: Researcher in Human Computer Interaction
  • Pronouns: He/They
  • Pain points: finding arts centered platforms that have easily accessible on their analytics and UX data, connecting with peers more oriented towards cultural heritage to collaborate or connect with
  • Needs: An online platform that serves as repository for contacts in the arts, data sets


User Scenario

Abirami is a researcher who is writing a report on the effectiveness of different forms of digital engagement and collaboration. His top goals are finding specific metrics that demonstrate impact and allow him to quantify and compare good practices with ineffective ones. He reviews metrics daily because since the pandemia there has been a dynamic online community and shift in terms of how CHI’s engage with their audience. He is often drawn to trending practices especially on social media or content that receives attention i.e. clicks over extended periods of time. He understands the virality or popularity and consistency of participation from users as a basic good. Using the InDICEs platform he often compares the analytics of the participation on the platform to what he sees on social media to understand if there’s a difference in consistency of use or involvement. He sees an opportunity with the InDICEs dashboard in terms of a specific tool that allows him to specify his geographic scope and see what related terms and articles appear daily. He is often sharing information he sees on the dashboard with his peers to see if there’s an opportunity to do a more qualitative dive into what specific conversations or collaborations have gained popularity or long time loyalty. To do this he participates in various discussions and most recently has created a survey that summarises his work and is testing it with the InDICEs community to see if there are variations, consistencies, or outliers. 



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List of Endorsements

Avatar: Fred Truyen Fred Truyen

AUTOMATED DATA CONTENT ANALYSIS

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Sources:
Date range:  
Color codes for sentiment analysis: Negative vs Positive
Endorsements count1
Abirami: Researcher Comments 0

Reference: IN-PROP-2021-03-101
Version number 1 (of 1) see other versions
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Value: ea79d2dc673bbedce2c8c2f136c1efc583ea307e386ed6227cddf1d5a103b26e

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This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 870792.
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