Lecture9 - Social Media and GIS

Introduction

  • The lecture will focus on social media and GIS, addressing its continued relevance despite the changing social media landscape.
  • A recent paper by Phil Barty on the impact of social media around fracking in Lancashire will be discussed.
  • Definitions, characteristics, data types, geographic extraction (geotagging, geocoding, geoparsing), examples, challenges, and ethical considerations will be covered.

Social Media: Definitions and Characteristics

  • Social media involves informal and formal communication between individuals and organizations.
  • Platforms like Facebook, Twitter, and LinkedIn serve different purposes.
  • Mobile devices facilitate constant connectivity and data sharing.

Cynical View

  • Social media can create a filtered view of reality, reinforcing existing opinions.

Data and Usage

  • Most studies focus on Twitter, Flickr, and Webio due to their simple formats.
  • Global average social media usage is around 144 minutes a day, varying by age group.
  • Billions of active users on platforms like Facebook (3 billion) and Twitter (556 million).

Twitter

  • Twitter is effective due to its concise message format and use of hashtags.
  • Tweets were initially limited in size before expanding to 240 characters.

Ambient Geographic Information

  • Social media data is not volunteer geographic information (VGI).
  • Researchers harvest ambient information for analysis.
  • Tools and techniques extract geographic and textual components.
  • The characteristics of social media data: volume, velocity, variety, and fine-grained nature

Spatial Information in Social Media

  • Spatial information can be explicit (latitude, longitude, timestamp) or implicit (place names).
  • Geotagging offers precise location data; geocoding assigns coordinates based on place names.
  • The location of the user matters, differentiating between being present at an event versus commenting remotely.

Mapping Data

  • Explicit data allows direct plotting using latitude and longitude.
  • Twitter removed the geotagging facility in 2019 due to privacy concerns.

Geocoding

  • Geocoding involves assigning coordinates to a location based on place names.
  • Accuracy depends on the size of the place; smaller regions provide better accuracy.
  • For local analysis, detailed address strings and postcodes are necessary.
  • Forward geocoding converts addresses to coordinates; reverse geocoding finds addresses from coordinates.

Geoparsing

  • Geoparsing extracts location information from text using machine learning.
  • It identifies place names and contextual clues to infer locations.
  • Challenges include misspellings, abbreviations, and colloquial terms.
  • Algorithms analyze surrounding words to confirm spatial references.
  • Tools like the Edinburgh Geoparcer can automatically identify and map place names in text.

Usage and Analysis

  • Analysis involves studying the time and location of posts, content, and user interactions.
  • Applications include tracking traffic accidents, natural disasters, and reactions to events.
  • Studies have analyzed events like the Arab Spring, earthquakes in New Zealand, and disasters in Haiti.
  • The analysis allows to track