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Social Networking: Visualizing Twitter
Social Networking: Visualizing Twitter
Social Networking: Visualizing Twitter
Social Networking: Visualizing Twitter
Social Networking: Visualizing Twitter
Social Networking: Visualizing Twitter
Social Networking: Visualizing Twitter
Social Networking: Visualizing Twitter
Social Networking: Visualizing Twitter
Social Networking: Visualizing Twitter
Social Networking: Visualizing Twitter
Social Networking: Visualizing Twitter
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Social Networking: Visualizing Twitter

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Slides by TEAM BIRCH from the SICSA Big Data InfoVis Summer School 2013 - …

Slides by TEAM BIRCH from the SICSA Big Data InfoVis Summer School 2013 -
Members:
Ruth Agbakoba
Anil Bandhakavi
Aminu Muhammad
Chris Hillman
Nut Limsopathan

Published in: Technology, Business
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Transcript

  • 1. Social Networking: Visualizing Twitter TEAM BIRCH: Chris, Ruth, Nut, Aminu and Anil
  • 2. Overview 1. Introduction 2. Background to Twitter and Boston Bombings 3. Big and Dirty Data Issues 4. Process: Capturing the integrated learning process 5. 5 W’ of Twitter Analytics 6. DEMO ‘Visualisation’ 7. Further Work 8. Learning Outcomes
  • 3. Who We Are • Aminu • Anil • Chris • Nut • Ruth
  • 4. Our Data • Twitter Data from 16:00 to 19:00 RE: Boston Marathon (Bombing) • Approx 550,000 tweets covering the 3 hour Period • Challenges – Data format – Lack of information – UserIDs vs. UserNames
  • 5. Big and Dirty Data Issues 1. Each tweet should have a record of its own! (Lines) 2. Formatting Issues 3. No standardisation (only ~10% tweets geo-location) 4. Only 5 fields > had to create three more 5. Different languages 6. Information overload – many different patterns identified therefore difficult to focus on a particular visualisation.
  • 6. Overview of Process Python Script Harvests Tweets using the Twitter API MapReduce code processes tweets Acquire Parse/Filter/Mine Create Visualisation in Tableau Public and Google Fusion Write out Text Files relevant to the analytics Display in Web Portal on Users Screen Represent Interact
  • 7. Map Reduce MapReduce code processes tweets • Parse • Added information where possible – retweet/hashtag/touser • Filter • Remove Records with invalid fields • Split into Geocoded, non- Geocoded • Mine • Word Counts • Hashtag Counts – all and split by location / original vs. retweet • Sentiment Extraction Acquire Parse/Filter/Mine Represent Interact
  • 8. Visualisation Tools Used Created a Real-time Twitter Analytics Portal with • Tableau Public • Google Fusion • Wix Web Portal • Purpose: – Insight – Exploratory – Confirmation
  • 9. Twitter Analytics • 5 W’s of Social Media! – Who – What – Where – When – Why
  • 10. DEMO
  • 11. Future Work • Gain an holistic view of the story over time – Bombing – 15th April – Shooting – 18th April – Fire fight & Manhunt – 19th April • Reflect the story as it evolved – Clustering – NLP (to move from basic to advanced analytics) – Explore more visualisation types
  • 12. Thank you for Listening! TEAM BIRCH: Chris, Ruth, Nut, Aminu and Anil

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