Does Love Change on Twitter? The Dynamics of Topical Conversations in Microblogging
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vl3The document analyzes trends in conversations on Twitter over 47 weeks for 11 topics varying in subject and geography. It finds that topic matters more than time for identifying trends, and that the most consistent keywords are for subjective topics. Trends are also more dependent on global rather than local background noise. These results provide suggestions for building tools to monitor trends, such as frequency of data collection, whether to use short-term or long-term data, and if local or global data is better.
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Table of Contents
Course Description
Evaluation Procedures
Course Scope
Grading Scale
Course Objectives
Course Outline
Course Delivery Method
Policies
Resources
Academic Services
Course Description (Catalog)
This course examines information security issues and the law. The rapid growth of technology has given rise to legal issues surrounding technology. Information security is the practice of protecting information to ensure the goals of confidentiality, integrity and availability are met. Information security makes sure that accurate information is available to authorized individuals when it is needed. When governments, private organizations, and individuals do a poor job of protecting the information entrusted to them, legislatures respond with new laws. The course will examine key conflicts involving technology and the legal system to include: privacy issues, civil, criminal, and administrative law, Children’s Online Privacy Protection Act (COPPA), Sarbanes Oxley, and information security governance. This course examines current literature on such topics.
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The successful student will fulfill the following learning objectives:
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Does Love Change on Twitter? The Dynamics of Topical Conversations in Microblogging
- 1. The Dynamics of Topical Conversations in Microblogging Victoria Lai and William Rand SocialCom 2013 September 11, 2013
- 2. Keyword and trend identification Background noise Importance of time, subject, geography? 2
- 3. Twitter’s trending topics Real-time trend identification ◦ Mathioudakis and Koudas (2010) ◦ Cataldi, Di Caro, and Schifanella (2010) Other trend detection methods ◦ Benhardus and Kalita (2013) Trends in a geographic area ◦ Wilkinson and Thelwall (2012) 3
- 4. Daily Twitter REST API queries, 47 weeks Topics ◦ 11 keywords/phrases chosen for time, subject, and geographic variety (baseball, economy, global warming, love, London riots) ◦ 5 control (I, the, and, a, of) Geography ◦ Global ◦ 9 cities (Boston, D.C., London, Sydney) 4
- 5. Term frequency Inverse document frequency TF-IDF
- 6. Keyword lists as target/control sets vary Time, subject, geography Tf-idf and rank correlation 𝑓𝑓 𝑇𝑇𝑡𝑡∗, 𝑠𝑠∗, 𝑔𝑔∗ 𝐶𝐶𝑡𝑡, 𝑠𝑠, 𝑔𝑔 ) 6
- 7. … tf phrase 1 # phrase 2 # phrase 3 #C0, ∀sc, ∀g C1, ∀sc, ∀g C2, ∀sc, ∀g C3, ∀sc, ∀g idf phrase 1 # phrase 2 # phrase 3 # phrase 1 # phrase 2 # phrase 3 # … T0, s*, ∀g * SC = {I, the, and, a, of }
- 8. Control set variation by time ◦ Single week ◦ Cumulative week Control set variation by geography Target set variation by time 8
- 9. Topic matters, time doesn’t. t 9
- 10. Top keywords are very consistent, particularly for subjective topics. 10
- 11. The correlation decays as we add tweets from another week back in time. 11
- 12. 12
- 13. For trends relative to the current background noise, we only need a few weeks of control data. 13
- 14. Trends are independent of local background noise and more dependent on global background noise. 14
- 15. Some topics are more similar to background conversations than others.15
- 16. Subjective topics exhibit less vocabulary change over time. 16
- 17. The results of this analysis provide suggestions for how to build a trend monitoring tool. These recommendations help define and understand: Frequency of collection Short-term vs. long-term collections Local collections vs. global collections Keyword selection for best results 17
- 18. Subject variation Other methods of comparison Blogging data 18
- 19. 19