Syllabus and Textbooks

The required reading will be mainly from select journal/conference articles (listed below for each lecture), which are all available online, as well as a few chapters of the following textbook:

Syllabus

Prior to every class, we provide a list of papers - select research papers relevant to a specific lecture. Students are encouraged to read these papers before the class. See further details in Policies section.

Week 1: Introduction and Overview

  • Ch.01 Overview

  • What is Twitter, a social network or a news media? Kwak, H., et al. WWW’10

  • Understanding the demographics of twitter users. Mislove, A., et al. AAAI’11

Week 2: Data Crawling

  • tutorial Tutorial: Twitter API.

  • tutorial Tutorial: Reddit data.

  • video lecture or talk Assignment 1 (out), Project Proposal (out)

Week 3: Social Relations

  • Ch.02 Graphs

  • Ch.03 Strong and Weak Ties

  • Fragile online relationship: a first look at unfollow dynamics in twitter. Kwak, H., et al. SIGCHI’11.

  • The anatomy of the facebook social graph. Ugander, J., et al. arXiv’11.

  • Graph structure in the web. Broder, A., et al. Computer networks’00.

Week 4: Power Laws and Popularity

  • Ch.14 Link Analysis and Web search

  • Ch.18 Power Laws

  • Inequality and unpredictability in an artificial cultural market. Salganik, M.J., et al. Science’06.

  • The small world problem . Milgram S. Psychology Today’1967.

  • Four degrees of separation . Backstrom L., et al. WebSci’12.

  • Documentary: Connected: the power of six degrees. A documentary on networks, social and otherwise. 2008.

  • video lecture or talk Assignment 1 (in), Assignment 2 (out).

Week 5: Information Cascades I

  • Ch.16 Information Cascades

  • Ch.19 Cascading Behavior in Networks

  • TED talk: How to start a movement. Sivers, D. 2010.

  • video lecture or talk Assignment 2 (in), Assignment 3 (out).

Week 6: Information Cascades II

  • Do cascades recur? Cheng, J., et al. WWW’16.

  • Searching for superspreaders of information in real-world social media. Pei, S., et al. Scientific reports’14.

  • Emerging topic detection for organizations from microblogs. Chen, Y., et al. SIGIR’13.

  • Lecture: Information diffusion on Twitter. Nikolov, S. 2012.

  • video lecture or talk Assignment 3 (in).

Week 7: Exam

Week 8: Spring break

  • no class.

Week 9: Noisy Text Processing I

  • Spotting spurious data with neural networks. Amiri, H. NAACL’18.

  • Part-of-speech tagging for twitter: annotation, features, and experiments. Gimpel, K., et al. ACL’11.

  • tutorial Tutorial: Crowdsourcing

  • tutorial Tutorial: TweetNLP

  • video lecture or talk Assignment 4 (out).

Week 10: Projects

  • Proposal presentations

  • See details on Blackboard.

  • video lecture or talk Project Proposal (in), Project Report (out).

Week 11: Noisy Text Processing II

  • Attentive multiview text representation for differential diagnosis. Amiri, H., et al. ACL’21.

  • Vector of locally aggregated embeddings for text representation. Amiri, H. et al. NAACL’19.

  • tutorial Tutorial: Vowpal Wabbit

  • video lecture or talk Assignment 4 (in), Assignment 5 (out).

Week 12: Redundancy, Specificity & Polarization in Social Media

  • Linguistic redundancy in twitter. Zanzotto, F.M., et al. EMNLP’11.

  • Global connectivity and multilinguals in the Twitter network. Hale, S.A. SIGCHI’14.

  • Analyzing polarization in social media. Demszky, D., et al. NAACL’19.

  • Predicting and analyzing language specificity in social media posts. Gao, Yifan, et al. AAAI’19.

Week 13: Search and Factuality in Social Media

  • Guest Lecture: Mitra Mohatarami

  • Overview of the trec-2014 microblog track. Lin J., et al. TREC’14.

  • Attentive moment retrieval in videos. Liu, M., et al. SIGIR’18.

  • Automatic stance detection using end-to-end memory networks. Mohtarami, M., et al. NAACL’18.

  • Contrastive language adaptation for cross-lingual stance detection. Mohtarami, M., et al. EMNLP’19.

  • Learning time to event. Dehghani, N., et al. ACL’21.

  • video lecture or talk Assignment 5 (in).

Week 14: Health Informatics in Social Media

  • Guest Lecture: TBA

  • Estimating county health statistics with twitter. Culotta, A. CHI’14.

  • Semantic mapping of natural language input to database entries via CNNs. Korpusik, M., et al. ICASSP’17.

  • Toward large-scale and multi-facet analysis of first-person alcohol drinking. Amiri, H., et al. AMIA’18

  • Learning to estimate nutrition facts from food descriptions. Amiri, H., et al. AMIA’19

  • Machine learning in medicine. Rajkomar, A., et al. NEJM’19.

Week 15: Projects

  • Final project presentations.

  • See details on Blackboard.

  • video lecture or talk Project Report (in).