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Introduction

This course provides an in-depth introduction to social media mining, focusing on platforms such as Facebook, Amazon, Yelp, and Twitter. Participants will learn how these platforms utilize user data and explore potential applications of this data. Key topics include the social aspects of the web, methods of collecting and analyzing social media data, community detection, and understanding user interactions. Additionally, we will discuss the challenges of big data in social media, including data distribution, sample reliability, noise removal, and evaluation dilemmas. Join us to gain insights into the fascinating world of social media mining.

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Introduction

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Presentation Transcript

  1. Social Media Mining Introduction

  2. Facebook • How does Facebook use your data? • Where do you think Facebook can use your data?

  3. Amazon

  4. Yelp

  5. Twitter

  6. Objectives of Our Course • Understand social aspects of the Web • Social Theories + Social media + Mining • Learn how to collect, clean, and represent social media data • How to measure important properties of social media and simulate social media models • Find and analyze communities in social media • Understanding friendships in social media, perform recommendations, and analyze behavior • Study or ask interesting research issues • e.g., start-up ideas • Learn representative algorithms and tools

  7. Overview – Dependency Graph

  8. Social Media

  9. Definition Social Media is the use of electronic and Internet tools for the purpose of sharing and discussing information and experiences with other human beings in more efficient ways.

  10. Social Media Mining is the process of representing, analyzing, and extracting meaningful patterns from social media data

  11. Social Media Mining Challenges • Big Data Paradox • Social media data is big, yet not evenly distributed. • Often little data is available for an individual • Obtaining Sufficient Samples • Are our samples reliable representatives of the full data? • Noise Removal Fallacy • Too much removal makes data more sparse • Noise definition is relative and complicated and is task-dependent • Evaluation Dilemma • When there is no ground truth, how can you evaluate?

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