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Social media provides a massive amount of valuable information and shows us how language is actually used by lots of people. This course will give an overview of prominent research findings on language use in social media. The course will also cover several machine learning algorithms and the core natural language processing techniques for obtaining and processing Twitter data.

Alan Ritter
Time/Place new
     Spring 2018, CSE 5539-0010 The Ohio State University
     Derby Hall Room 049 | Thursday 10:40AM – 12:30PM
     dual-listed undergraduate and graduate course
     [Office Hour] Dreese 595 | Tuesday 4:00PM – 5:00PM
In order to succeed in this course, you should know basic probability and statistics, such as the chain rule of probability and Bayes’ rule; some basic calculus and linear algebra will also help, such as knowing what is gradient. On the programming side, all projects will be in Python. You should understand basic computer science concepts (like recursion), basic data structures (trees, graphs), and basic algorithms (search, sorting, etc).

Course Readings
     Each lecture has an accompanying set of academic papers

     Piazza (discussion, announcements and restricted resources)
     Carmen (homework submission and grades)

Grading (subject to change)
This is a research-oriented project-based course (total 100 points). Instead of exams, each student will do two hands-on programming assignments and an optional research project. The class will assign one paper for reading each week. Each student should read the assigned paper and submit a short critique (between 100-200 words) online in Carmen before 10:00am on the day of class. These reviews should not be simple summaries, but discuss positive aspects of the paper and limitations (some examples), or suggestions for how the work could be improved or extended. Students are allowed to skip two reviews throughout the semester. Please email your reviews or homeworks to the instructor if there are any technical issues with online submission.
  • programming homework #1 (15 points/individual)
  • programming homework #2 (30 points/individual)
  • take-home quizzes (10 points/individual, about 5 quizzes)
  • paper summaries (20 points/individual, about 10 papers)
  • in-class presentation (15 points/group of two)
  • participation in class discussions - including Piazza (10 points/individual)
  • a research project or programming homework #3 (optional, 20 bonus points)

Previous Offerings
  • Fall 2017, The Ohio State University (Instructor: Wei Xu)
  • Fall 2016, The Ohio State University (Instructor: Wei Xu)
  • Summer 2016, NASSLLI: The North American Summer School on Logic, Language, and Information (Instructor: Wei Xu)
         Teaching evaluation was 5.72 out of 6 at NASSLLI; average across all instructors was 5.23.
  • Summer 2015, University of Pennsylvania (Instructor: Wei Xu)