LIBR 559D (3) Social Media Intelligence


MLIS and Dual: Completion of MLIS Core or permission of iSchool Graduate Advisor

GOAL: This course provides a graduate-level introduction to Social Media Intelligence (SMI) and its associated methods. The goal of the course is to create an interactive learning community around the theme of SMI. This includes familiarizing students with the types and use of social media, and providing hands-on experience in managing, analyzing, and mining social data using available tools (e.g., dashboards) and automated methods (e.g., natural language processing and
machine learning technologies). We will read, discuss, and critique claims and findings from contemporary research related to SMI. We will also address practical issues related to successfully managing social media platforms, launching marketing campaigns, and using and building tools to analyze and mine social media.

Potential audiences for this course include, but are not limited to:

  • People interested in text analytics, information retrieval, information visualization, human-information interaction, natural language processing, machine learning, etc., who want to prepare for, or significantly advance, carrying out research in these fields.
  • People interested in practical experience on effective usage, management, analytics, and mining of social media in work places (e.g., libraries, museums, companies).


Upon completion of this course students will be able to:

  • Understand a wide range of social media usage, management, and mining concepts and tasks and their relevance to the information needs of diverse individuals, communities and organizations.
  • Enhance interpersonal and written communication skills.
  • Collaborate effectively with peers through course assignments.
  • Apply social media marketing, management, and mining methods to address information needs, questions, and issues.


  • Overview of social media types and use and their role
  • Running social media systems and campaigns
  • Social media management
  • Virality and engagement in social media
  • Social data crawling and engineering
  • Predictive analytics with natural language processing & machine learning
  • Practical social media intelligence tasks:
    • Age and gender detection
    • Sentiment analysis and emotion detection
    • Personality prediction
    • Sarcasm, humor, and occupational class detection
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