xinyunzh / umd-j479v-j779v-spring2016

JOUR479V / JOUR779V: Computational Journalism - Spring 2016###

University of Maryland, College Park, College of Journalism
Mondays 10:00am - 12:45pm
Location: Knight Hall Room 2107

Instructor: Assistant Professor Dr. Nicholas Diakopoulos, [email protected], nickdiakopoulos.com, @ndiakopoulos
Office Hours: Knight Hall Room 3207 from 4-5pm Mondays, or by appointment
Course Website: https://github.com/comp-journalism/UMD-J479V-J779V-Spring2016

####Course Description#### This course explores the conceptualization and application of computational and data-driven approaches to journalism practice. Students will examine how computational techniques are changing journalistic data gathering, curation, sensemaking, presentation, dissemination, and analytics of content. Methods from text analysis, social computing, automated news production, simulation / prediction / modeling, algorithmic accountability, and content analytics will be applied to real journalistic scenarios. Several assignments, both critical and creative in nature, as well as an integrative final project will serve to underscore the concepts taught and provide practice in producing artifacts of computational journalism.

####Learning Goals and Objectives#### By the end of the course students should expect to have gained:

• Knowledge of the opportunities that computing creates for journalism in data gathering, sensemaking, presentation, and dissemination, as well as a critical stance and understanding of implications and limitations of algorithms and computing in the media.
• An ability to constructively apply that knowledge to computational and data journalism projects and investigations by using computational thinking, data, algorithms, and programming as needed.
• Skills and knowledge of practical tools that enable computational and data journalism, such as Python, Jupyter Notebooks, and Github.

####Prerequisites####

• A university statistics course, or permission of the instructor

####Textbook and readings####

• There is no single textbook for the course. Readings will draw on a wide range of texts including books, articles, and research papers. Copyrighted readings may be linked to the syllabus and you may need to access the PDF via the library, or by following the link from a computer on campus (which will automatically authenticate and give you access). Since readings may be updated over the course of the semester please check the online schedule on a weekly basis in order to get that week’s readings.
• A recommended text for the skills aspects of the course is: Python for Data Analysis: Data Wrangling with Pandas, NumPy, and IPython, by Wes McKinney Amazon Link

####Other Resources####

• Computational Journalism Course @ Georgia Tech Current Class | Previous offerings
• Computational Journalism Course @ Columbia Link
• Computational Journalism Course @ Stanford Link
• Symposium on Computation + Journalism 2015
• Source Open News Link
• Curated list of data science blogs Link

####About the Instructor#### Dr. Nicholas Diakopoulos is an Assistant Professor at the University of Maryland College of Journalism, with courtesy appointments in the College of Information Studies and Department of Computer Science. His research is in computational and data journalism with an emphasis on algorithmic accountability, narrative data visualization, and social computing in the news. He received his Ph.D. in Computer Science from the School of Interactive Computing at Georgia Tech where he co-founded the program in Computational Journalism. Before UMD he worked as a researcher at Columbia University, Rutgers University, and CUNY studying the intersections of information science, innovation, and journalism.

####Attendance and punctuality#### It is important that you attend every class and arrive on time. To do otherwise will negatively affect your grade, because you will miss instruction and class discussions. Please notify the professor in advance, if possible, if you will be missing class due to illness or emergency.

####Religious Holidays#### There will be no tests or major assignments scheduled on religious holidays identified by the university. If you expect to miss a class due to a religious holiday, please notify the professor in writing before the start of the second class.

####Inclement weather#### If the university closes due to foul weather (hurricanes, tornadoes, earthquakes, blizzards, ice) or other emergencies and class must be canceled, students will be advised of assignment adjustments by the instructor. We will use email to make these notifications. Please check the university's home page if in doubt about whether or not classes have been canceled on campus.

####Academic integrity#### Along with certain rights, students have the responsibility to behave honorably in an academic environment. Academic dishonesty, including cheating, fabrication, facilitating academic dishonesty and plagiarism, will not be tolerated. Adhering to a high ethical standard is of special importance in journalism, where reliability and credibility are the cornerstones of the field. Therefore, the college has adopted a “zero tolerance” policy on academic dishonesty. Any abridgment of academic integrity standards in a College of Journalism course will be referred to the university’s Student Honor Council (see http://www.shc.umd.edu and the college's deans. To insure this is understood, all students are asked to sign an academic integrity pledge at the beginning of the semester that will cover all assignments in this course. Students found to have violated the university's honor code may face sanctions, including a grade of XF for the course, suspension or expulsion from the university.

####Students with Special Needs#### Students with a specific disability (permanent or temporary, physical or learning) needing special accommodation during the semester should make an appointment to meet with the professor immediately after the first class. Students may be asked to provide the instructor accommodation forms given to them after testing by the Disability Support Service on campus, 301-405-0813

####Assignments & Evaluation### All assignments and projects are due at the start of the class unless otherwise noted. Detailed instructions for each assignment and project will be provided as per the class schedule. Assignments will be evaluated based on accuracy, functionality, critical and analytic rigor, and writing quality and clarity.

• Assignments (45%)
  • For 479V there will be a total for three assignments due in the class (Asgn #1 worth 12%, Asgn #2 worth 15%, Asgn #3 worth 18%).
  • For 779V there will be four assignments due in the class (Asgn #1 worth 10%, Asgn #2 worth 13%, Asgn #3 worth 16%). The extra assignment in 779V is selecting, presenting, and leading discussion on a research paper that you will sign up for (worth 6%).
• Final Project Proposal (10%)
  • You will develop a final project that utilizes the knowledge you acquire throughout the semester in an investigation of an algorithmic system. Your project proposal will describe what you intend to investigate including the data you will collect and how you plan to analyze it for something newsworthy.
• Final Project (25%)
  • Your final project should demonstrate that you have integrated the knowledge you acquire in this class. Your grade will incorporate aspects of accuracy, critical and analytic rigor, clarity and newsworthiness of the story you tell, and how much your project has improved through various stages of iteration and criticism since the initial project proposal.
• Class Participation (20%)
  • Students are expected to read and engage with the assigned texts, and to be prepared to discuss those texts critically. In class you will be assessed according to the insightfulness of contributions, critiques, and questions you raise during class discussion.
  • To show that you are prepared to discuss an assigned article, you should prepare at least one question based on your reading.

#####Late Work Policy##### Assignments will be marked down by one full letter grade for every 24 hours (or fraction thereof) that the assignment is late past the posted deadline. For example, an assignment that would normally receive an A- if submitted on time would receive a B- if it was submitted 1 hour (or 23 hours) late. Assignments more than five days late will not be accepted. Work that is not turned in will receive zero points. In extreme cases (such as a death in the family, or severe illness), an extension may be granted, but students must communicate with the professor in advance of the deadline in these cases.

##Schedule## ###January 25 - Introduction to Computational Journalism: Thinking Computationally###

• Lecture Slides PPT

• Homework OUT

  • Python tutorial: "Variables, Strings, and Numbers" Link
  • Python tutorial: "Lists and Tuples" Link

• 779V Assignment: Research Paper Presentation Out

  • Research Paper Presentation. Link

• Wed. Jan. 27, 5-8pm in Knight 3207

  • Python "bootcamp": we'll gather as a group and work on the week's homework together in a collaborative atmosphere. RSVP and there will be Pizza!

###February 1 - Computational and Data Journalism###

• Lecture Slides PPT

• Readings DUE:

  • M. Coddington. Clarifying Journalism’s Quantitative Turn: A Typology for Evaluating Data Journalism, Computational Journalism, and Computer-Assisted Reporting. Digital Journalism, 3(3), 331–348. 2015. Article (access on campus or via library to download PDF)
  • N. Silver. What the Fox Knows. Five Thirty Eight. March, 2014. Article
  • A. Cairo. Data journalism needs to up its own standards. Nieman Lab. July, 2014. Article
  • N. Diakopoulos. The Rhetoric of Data. July, 2013. Article
  • P. Guo. Data Science Workflow: Overview and Challenges. CACM Blog. Oct. 2013. Post
  • J. Singer-Vine. What we’ve learned about sharing our data analysis. Source. Aug. 2015. Article | Original Buzzfeed Article

• Recommended Readings DUE:

  • C. Groskopf. The Quartz guide to bad data. Link

• In class tutorials:

  • Loading, Manipulating, and Visualizing Data with Pandas. Loading Notebook | Manipulating Notebook | Visualization Notebook

• Helpful resources:

  • Markdown Basics Link

• Homework OUT

  • Python tutorial: "Introducing Functions" Link
  • Python tutorial: "If Statements" Link

• Assignment One OUT

  • Computational and Data Journalism Critique Link

• Tues. Feb. 2, 5-8pm in Knight 3207

  • Python "bootcamp": we'll gather as a group and work on the week's homework together in a collaborative atmosphere. RSVP and there will be Pizza!

###February 8 - Document and Text Analytics### (Guest lecture by Dr. Jennifer Stark, Research Scientist at UMD Computational Journalism Lab)

• Readings DUE:

  • C. Felix, A. Vikram Pandey, E. Bertini, C. Ornstein and S. Klein. RevEx: Visual Investigative Journalism with A Million Healthcare Reviews. Symposium on Computation + Journalism. 2015. PDF | ProPublica Article
  • M Brehmer, S Ingram, J Stray, T Munzner. Overview: The design, adoption, and analysis of a visual document mining tool for investigative journalists. IEEE Transactions on Visualization and Computer Graphics, 20 (12), 2014. Article (access on campus or via library to download PDF)

• In class tutorial:

  • Text Analysis Link

• Homework OUT

  • Python tutorial: "While loops and Input" Link
  • Pandas background: "10 Minutes to pandas" Link

• 779V Assignment: Research Paper Selection DUE

• Wed. Feb. 10, 5-8pm in Knight 3207

  • Python "bootcamp": we'll gather as a group and work on the week's homework together in a collaborative atmosphere. RSVP and there will be Pizza!

###February 15 - Social Media Analytics###

• Lecture Slides PPT

• Readings DUE:

  • N. Diakopoulos, M. De Choudhury, M. Naaman. Finding and Assessing Social Media Information Sources in the Context of Journalism. Conference on Human Factors in Computing Systems (CHI). May, 2012. PDF
  • R. Schwartz, M. Naaman, R. Teodoro. Editorial Algorithms: Using Social Media to Discover and Report Local News. Proc. International Conference on Web and Social Media. 2015. PDF
  • A. Fitts. The new importance of ‘social listening’ tools. Columbia Journalism Review. July / August 2015. Article
  • B. Clifton, G. Lotan, E. Pierson. How to tell whether a Twitter user is pro-choice or pro-life without reading any of their tweets. Quartz. Oct 2015. Article

• In class tutorial

  • Social Media Analytics Link

• Assignment One DUE

• Assignment Two OUT

  • Reproducible Data Analysis with Jupyter. Link

• Wed. Feb. 17, 5-8pm in Knight 3207

  • Pandas "bootcamp": we'll gather as a group and work on learning more about the Pandas library in a collaborative atmosphere. RSVP and there will be Pizza!

###February 22 - Automated News Production###

• Lecture Slides PPT

• Readings DUE:

  • M. Carlson. The Robotic Reporter: Automated Journalism and the Redefinition of Labor, Compositional Forms, and Journalistic Authority. Digital Journalism. 2014. Article (access on campus or via library to download PDF)
  • A. Graefe. Guide to Automated Journalism. Tow Center Report. Jan. 2016. PDF
  • T. Lokot and N. Diakopoulos. News Bots: Automating News and Information Dissemination on Twitter. Digital Journalism. 2016. PDF
  • C. LeCompte. Automation in the Newsroom. Nieman Reports. Sept, 2015. Article
  • S. Wang. The New York Times built a Slack bot to help decide which stories to post to social media. Nieman Lab. Aug 2015. Link

• In class tutorials:

  • Using Tweepy for Fun and Profit Link

###February 29 - Simulation, Predicton, and Modeling###

• Lecture Slides PPT

• Readings DUE:

  • N. Silver. The Signal and the Noise. Penguin Books. 2012.
    • Chapter 4: For years you've been telling us that rain is green. PDF
    • Chapter 7: Role models. PDF
  • D. Lazar et al. The Parable of Google Flu: Traps in Big Data Analysis. Science Vol 343. March, 2014. PDF
  • I. Bogost, S. Ferrari, and B. Schweizer. Newsgames: Journalism at Play. 2010. Chapter 1: Newsgames. PDF

• In class tutorial / challenge

  • Predicting NYT Picks Comments Link

• Recommended Readings DUE:

  • A Visual Introduction to Machine Learning. Link

• Assignment Two DUE

• Assignment Three OUT

  • News Bot Design & Development. Link

###March 7 - Algorithmic Accountability & Transparency###

• Lecture Slides PPT

• Readings DUE:

  • N. Diakopoulos. Algorithmic Accountability: Journalistic Investigation of Computational Power Structures. Digital Journalism. 2015. PDF
  • J. Valentino-Devries, J. Singer-Vine, A. Soltani. Websites Vary Prices, Deals Based on Users' Information. Wall Street Journal. Dec. 2012. PDF
  • N. Diakopoulos. How Uber surge pricing really works. Washington Post. April, 2015. Link

###March 14 - Spring break, no class!###

###March 21 - Algorithmic Curation & Personalization###

• Readings DUE:

  • E. Bozdag. Bias in algorithmic filtering and personalization. Ethics and Information Technology 15, 3 (2013), 209–227 PDF
  • M. Sifry. Facebook Wants You to Vote on Tuesday. Here's How It Messed With Your Feed in 2012. Mother Jones. Oct. 2014. Article
  • A. Spangher. Building the Next New York Times Recommendation Engine. NYT Open Blog. Aug. 2015 Article
  • Y. Oren. Flipboard's Approach to Automatic Summarization. Oct. 2014. Post

• Final Project OUT

  • Link

• March 23rd Assignment Three DUE

###March 28 - Content Analytics & Impact###

• Readings DUE:

  • C. Castillo, et al. Characterizing the life cycle of online news stories using social media reactions. Proc. Conference on Computer Supported Cooperative Work & Social Computing (CSCW). 2014. PDF
  • E. Tandoc Jr. and R. Thomas. The Ethics of Web Analytics. Digital Journalism, 3:2, 243-258, 2015. Article (access on campus or via library to download PDF)
  • C. Breaux. You’ll Never Guess How Chartbeat’s Data Scientists Came Up With the Single Greatest Headline. Chartbeat Blog. Nov. 2015. Article
  • T. Fisher. Do Visual Stories Make People Care? NPR Visuals Blog. Nov. 2015. Article

• In class exercise

  • Content Analytics & Impact Link

###April 4 - Innovating CJ Tools & Platforms###

(Guest Lecture by Angela Wong, Digital Product Analyst at Washington Post)

• Readings DUE:

• N. Diakopoulos. Computational Journalism and the Emergence of News Platforms. The Routledge Companion to Digital Journalism Studies. Eds. Scott Eldridge II and Bob Franklin. June, 2016. PDF

• N. Diakopoulos. Cultivating the Landscape of Innovation in Computational Journalism. Tow-Knight Center for Entrepreneurial Journalism. April, 2012. PDF

• D.G. Park, S. Singh, N. Diakopoulos, N. Elmqvist. Interactive Identification of High Quality Online News Comments. Proc. Conference on Human Factors in Computing Systems (CHI). 2016. PDF

• Final Project Proposal DUE

###April 11 - Research Paper Presentations & Project work###

###April 18 - In Class Final Project Progress Report Presentations###

###April 25 - Emerging Technology: Drones & Sensors###

• Readings DUE:
  • M. Tremayne and A. Clark. New Perspectives from the Sky: Unmanned Aerial Vehicles and Journalism. Digital Journalism 2 (2). 2014. Article (access on campus or via library to download PDF)
  • A Code of Ethics for Drone Journalists. Professional Society of Drone Journalists (PSDJ). Post
  • F. Pitt. "USA Today - Ghost Factories" (pages 73 - 82). In: Sensors and Journalism. Tow Center Report. 2014. PDF | Original USA Today Package
  • L. Bui. A (Working) Typology of Sensor Journalism Projects. Oct. 2014. Post

###May 2 - In Class Final Project Presentations###