Designing Internet Research (Winter 2020)

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Designing Internet Research
COM528 - Department of Communication, University of Washington
Instructor: Benjamin Mako Hill (University of Washington)
Course Websites:
Course Catalog Description:
Focuses on designing Internet research, assessing the adaptation of proven methods to Internet tools and environments, and developing new methods in view of particular capacities and characteristics of Internet applications. Legal and ethical aspects of Internet research receive ongoing consideration.

Overview and Learning Objectives[edit]

What new lines of inquiry and approaches to social research are made possible and necessary by the Internet? In what ways have established research methods been affected by the Internet? How does the Internet challenge established methods of social research? How are researchers responding to these challenges?

These are some of the key questions we will explore in this course. The course will focus on assessing the incorporation of Internet tools in established and emergent methods of social research, the adaptation of social research methods to study online phenomena, and the development of new methods and tools that correspond with the particular capacities and characteristics of the Internet. The readings will include both descriptions of Internet-related research methods with an eye to introducing skills and examples of studies that use them. The legal and ethical aspects of Internet research will receive ongoing consideration throughout the course. The purpose of this course is to help prepare students to design high quality research projects that use the Internet to study online communicative, social, cultural, and political phenomena.

I will consider the course a complete success if every student is able to do all of these things at the end of the quarter:

  • Discuss and compare distinct types of Internet research including: web archiving; textual analysis; ethnography; interviews; network analyses of social and hyperlink networks; analysis of digital trace data, traditional, natural, and field experiments; design research; interviewing; survey research; and narrative and visual analyses.
  • Describe particular challenges and threats to research validity associated with each method.
  • For at least one method, be able to provide a detailed description of a research project and feel comfortable embarking on a formative study using this methodology.
  • Given a manuscript (e.g., in the context of a request for peer review), be able to evaluate an Internet-based study in terms of its use its methodological choices.
  • Use a modern programming language (like Python) to collect a dataset from a web API like those published by Twitter, Reddit, or Wikipedia.

Note About This Syllabus[edit]

You should expect this syllabus to be a dynamic document. Although the core expectations for this class are fixed, the details of readings and assignments will shift based on how the class goes, guest speakers that I arrange, my own readings in this area, etc. As a result, there are three important things to keep in mind:

  • Although details on this syllabus will change, I will try to ensure that I never change readings more than six days before they are due. I will send an announcement no later than before each Wednesday evening that fixes the schedule for the next week. This means that if I don't fill in a reading marked "[To Be Decided]" six days before it's due, it is dropped. If we don't change something marked "[Tentative]" before the deadline, then it is assigned. This means that if you plan to read more than six days ahead, contact the teaching team first.
  • Because this syllabus a wiki, you will be able to track every change by clicking the history button on this page when I make changes. I will summarize these changes in the weekly an announcement on Canvas sent that will be emailed to everybody in the class. Closely monitor your email or the announcements section on the course website on Canvas to make sure you don't miss these!
  • I will ask the class for voluntary anonymous feedback frequently — especially toward the beginning of the quarter. Please let me know what is working and what can be improved. In the past, I have made many adjustments to courses that I teach while the quarter progressed based on this feedback.

Books[edit]

This class has no textbook and I am not requiring you to buy any books for this class. That said, several required readings and many suggested readings, will come from several excellent books which you might want to consider adding to your library:

These books include:

  • Burgess, Jean, Alice Marwick, and Thomas Poell, eds. 2018. The SAGE Handbook of Social Media. London, UK: SAGE. [Available through UW libraries]
  • Foucault Welles, Brooke, and Sandra González-Bailón, eds. 2018. The Oxford Handbook of Networked Communication. London, UK: Oxford University Press. [Available through UW libraries]
  • Hargittai, Eszter, and Christian Sandvig, eds. 2015. Digital Research Confidential: The Secrets of Studying Behavior Online. MIT Press. [Available through UW libraries
  • Hesse-Biber, Sharlene Nagy, ed. 2011. The Handbook of Emergent Technologies in Social Research. Oxford, UK: Oxford University Press.
  • Hewson, Claire, Carl Vogel, and Dianna Laurent. 2016. Internet Research Methods. London, UK: SAGE. https://doi.org/10.4135/9781473920804. [Available through UW libraries]
  • Snee, Helene, Christine Hine, Yvette Morey, Steven Roberts, and Hayley Watson, eds. 2016. Digital Methods for Social Science: An Interdisciplinary Guide to Research Innovation. New York, NY: Palgrave-Macmillan. [Available through UW libraries]

Technical Skills[edit]

Nearly all of our structured in-person meetings and all of our readings will focus on teaching conceptual skills related to Internet research. These skills involve the "softer" skills of understanding, designing, and critiquing research plans. These are harder to teach, evaluate, and learn but are ultimately what will make a research project interesting, useful, or valid. When the course has been taught in the past by other faculty, it has been entirely focused on these types of conceptual skills.

That said, I also believe that any skilled Internet researcher must be comfortable writing code to collect a dataset from the web or, at the very least, should have enough experience doing so that they know what is involved and what is possible and impossible. This is essential even if your only goals is to manage somebody else writing code and gathering data or work productively with a collaborator who is doing so. As a result, being successful in this class will also require technical skills.

Because students are going to come to the class with different technical skillsets, we well be devoting a relatively small chunk of class time to developing technical skills. Instead, I'm requiring that students build these skills outside of our meetings together if they do not have them already.

In particular, I want every student to have the following three things:

  1. Basic skills in a general purpose high-level programming language used for Internet-based data collection and analysis. I strongly recommend the Python programming language although other programming languages like Ruby and Perl are also good choices. Generally speaking, statistical programming languages like R, Stata, Matlab are not well suited for this.
  2. Familiarity with the technologies of web APIs. In particular, students should understand what APIs are, how they work, and should be able to read, interpret, and process data in JSON.
  3. Knowledge of how to process and move data from a website or API into a format that they will be able to use for analysis. The final format will depend on the nature of the result but this might be a statistical programming environment like R, Stata, SAS, SPSS, etc or a qualitative data analysis tools like ATLAS.ti, NVivo or Dedoose.

If you are already comfortable doing these things, great.

If you are not yet comfortable, I am going to be organizing three free workshops called the Community Data Science Workshops on Saturdays in April and May and I extremely strongly recommend that you attend them. The workshops will teach exactly the skills I'm expecting you to have and attending the full series of workshops will be enough to fulfill this requirement.

The workshops will meet four times so please block these out on your calendar now:

  1. Friday January 17 6-9pm
  2. Saturday January 18 9:45am-4pm
  3. Saturday February 1 9:45am-4pm
  4. Saturday February 15 9:45am-4pm

I've offered this workshops four times previously and they have always been oversubscribed. As a result, you should register for these workshops immediately. You can find the registration link on this page. Please mention that you are in this class when you register so that we make sure that you accept your application.

I have taught these workshops five times before in 2014, 2015, and 2016. If you have taken them in the past, you do not need to take them again. If you took them before but are feeling unsure about your skills, you will be welcome to come back to review and brush up on the material.

If you do not have the technical skills required above and you will not attend the workshops, you're going to be responsible for learning this material on your own. Although this is totally fine, I suspect it present a major challenge to success in this class. If you will be in this situation, contact me before the quarter starts.

Assignments[edit]

The assignments in this class are designed to give you an opportunity to try your hand at using the conceptual material taught in the class. There will be no exams or quizzes. Unless otherwise noted, all assignments are due at the end of the day (i.e., 11:59pm on the day they are due).

Weekly Reflections[edit]

Deliverables
(1) Post a message in the appropriate course discussion board; (2) Respond to at least one of your classmates before class.
Due Date
(1) Every Monday (on a week with reading); (2) Every Tuesday at 1:30 (on a week with reading)
Maximum length
1,000 words

For every week that we have readings (i.e., every week except for the consulting weeks and and the final presentation weeks), I'm asking everybody to reflect on the readings by the day before class and to share their reflections with everybody else. Because we're skipping the first week, that works out to a total of six reflections.

Reflections should be no more than 1000 words (about one single-spaced page). So everyone will have a chance to read the reflections before class, response papers should be posted to our course website the day before (i.e., before midnight each Monday) so that we can all read and construct responses. Please also pose one or two open-ended discussion questions that may serve as jumping off points for our in-class conversation. Don't bother with summarizing (we've all done the reading after all) and focus on engaging with ideas.

In terms of content, response papers offer you an opportunity to engage the readings by identifying common or conflicting premises, thinking through potential implications, offering political or cultural examples, posing well-supported objections, or outlining critical extensions. In my experience, the most thought provoking reflections go beyond pointing out things that one wonders about or finds interesting and explain why you find it interesting.

Turn in your response paper to Canvas by posting a new message in the appropriate day in the course the discussion board.

I'd also like everybody read over everybody else's responses and respond to at least one person—evening things out so that not everybody response to one person would be nice, but use your judgement.

Research Project[edit]

As a demonstration of your learning in this course, you will design a plan for an internet research project and will, if possible, also collect (at least) an initial sample of a dataset that you will use to complete the project.

The genre of the paper you can produce can take one of the following three forms:

  1. A draft of a manuscript for submission to a conference or journal.
  2. A proposal for funding (e.g., for submission for the NSF for a graduate student fellowship).
  3. A draft of the methods chapter of your dissertation.

In any the three paths, I expect you take this opportunity to produce a document that will further your to academic career outside of the class.

Project Identification[edit]

Due Date
January 24
Maximum paper length
800 words (~3 pages)
Deliverables
Turn in [in the appropriate Canvas dropbox]

Early on, I want you to identify your final project. Your proposal should be short and can be either paragraphs or bullets. It should include the following things:

  • The genre of the project and a short description of how it fits into your career trajectory.
  • A one paragraph abstract of the proposed study and research question, theory, community, and/or groups you plan to study.
  • A short description of the type of data you plan to collect as part of your final project.

Final Project[edit]

Outline Due Date
February 23
Maximum outline length
2 pages
Presentation Date
March 10
Paper Due Date
March 20
Maximum outline length
8000 words (~27 pages)
All Deliverables
Turn in in the appropriate Canvas dropboxes

Because the emphasis in this class is on methods and because I'm not an expert in each of your areas or fields, I'm happy to assume that your paper, proposal, or thesis chapter has already established the relevance and significance of your study and has a comprehensive literature review, well-grounded conceptual approach, and compelling reason why this research is so important. Instead of providing all of this details, instead feel free to start with a brief summary of the purpose and importance of this research, and an introduction of your research questions or hypotheses. If your provide more detail, that's fine, but I won't give you detailed feedback on this parts.

The final paper should include:

  • a statement of the purpose, central focus, relevance and significance of this research;
  • a description of the specific Internet application(s) and/or environment(s) and/or objects to be studied and employed in the research;
  • key research questions or hypotheses;
  • operationalization of key concepts;
  • a description and rationale of the specific method(s), (if more than one method will be used, explain how the methods will produce complementary findings);
  • a description of the step-by-step plan for data collection;
  • description and rationale of the level(s), unit(s) and process of analysis (if more than one kind of data are generated, explain how each kind will be analyzed individually and/or comparatively);
  • an explanation of how these analyses will enable you to answer the RQs
  • a sample instrument (as appropriate);
  • a sample dataset and description of a formative analysis you have completed;
  • a description of actual or anticipated results and any potential problems with their interpretation;
  • a plan for publishing/disseminating the findings from this research
  • a summary of technical, ethical, human subjects and legal issues that may be encountered in this research, and how you will address them;
  • a schedule (using specific dates) and proposed budget.

I also expect each student to begin data collection for your project (i.e., using the technical skills you learn in the class) and describe your progress in this regard this in your paper. If collecting data for a proposed project is impractical (e.g., because of IRB applications, funding, etc) I would love for you to engage in the collection of public dataset as part of a pilot or formative study. If this is not feasible or useful, we can discuss other options.

I have a strong preference for you to write this paper individually but I'm open to the idea that you may want to work with others in the class.

Participation[edit]

The course relies heavily on participation and discussion. It is important to realize that we will not summarize reading in class and I will not cover it in lecture. I expect you all to have read it and we will jump in and start discussing it. The "Participation Rubric" section of my detailed page on assessment gives the rubric I will use in evaluating participation.

Assessment[edit]

I have put together a very detailed page that describes the way I approach assessment and grading—both in general and in this course. Please read it carefully I will assign grades for each of following items on the UW 4.0 grade scale according to the weights below:

  • Participation: 30%
  • Weekly Reflection: 15%
  • Proposal identification: 5%
  • Final paper outline: 5%
  • Final Presentation: 10%
  • Final Paper: 35%

Schedule[edit]

Week 1: Tuesday January 7: (I) Introduction and (II) Ethics[edit]

Part I: Introduction and Framing[edit]

Required Readings:

Optional Reading:

  • December, John. 1996. “Units of Analysis for Internet Communication.” Journal of Computer-Mediated Communication 1 (4): 0–0. https://doi.org/10.1111/j.1083-6101.1996.tb00173.x. [Available free online]
  • Sandvig, Christian, and Eszter Hargittai. 2015. “How to Think about Digital Research.” In Digital Research Confidential: The Secrets of Studying Behavior Online, edited by Eszter Hargittai and Christian Sandvig, 1–28. Cambridge, MA: MIT Press. [Available in Canvas]

Part II: Ethics[edit]

Required Readings:

To frame a conversation around research ethics, lets read this piece:

And these pieces that are all vaguely in response to it:

Optional Readings:

  • Department of Health, Education, and Welfare, and National Commission for the Protection of Human Subjects of Biomedical and Behavioral Research. 2014. “The Belmont Report. Ethical Principles and Guidelines for the Protection of Human Subjects of Research.” http://www.hhs.gov/ohrp/policy/belmont.html. [Available free online]
  • Frankel, Mark S., and Sanyin Siang. 1999. “Ethical and Legal Aspects of Human Subject Research on the Internet.” Workshop Report. Washington, DC: American Association for the Advancement of Science. [Available free online]

Week 2: Tuesday January 14: (I) Internet Data Collection (II) Textual Analysis[edit]

Part I: Internet Data Collection[edit]

Required Readings:

Optional Readings:

  • Ankerson, Megan Sapnar. 2015. “Read/Write the Digital Archive: Strategies for Historical Web Research.” In Digital Research Confidential: The Secrets of Studying Behavior Online, edited by Eszter Hargittai and Christian Sandvig, 29–54. Cambridge, MA: MIT Press. [Available in Canvas]
  • Spaniol, Marc, Dimitar Denev, Arturas Mazeika, Gerhard Weikum, and Pierre Senellart. 2009. “Data Quality in Web Archiving.” In Proceedings of the 3rd Workshop on Information Credibility on the Web, 19–26. WICOW ’09. New York, NY, USA: ACM. https://doi.org/10.1145/1526993.1526999. [Available through UW Libraries]
  • Schneider, Steven M., and Kirsten A. Foot. 2004. “The Web as an Object of Study.” New Media & Society 6 (1): 114–22. https://doi.org/10.1177/1461444804039912. [Available through UW Libraries]
  • Weber, Matthew S. 2014. “Observing the Web by Understanding the Past: Archival Internet Research.” In Proceedings of the Companion Publication of the 23rd International Conference on World Wide Web Companion, 1031–1036. WWW Companion ’14. Republic and Canton of Geneva, Switzerland: International World Wide Web Conferences Steering Committee. https://doi.org/10.1145/2567948.2579213. [Available through UW Libraries]

Optional readings related to the ethics of data collection online:

Two useful sources of data collection:

  • Archive Team is an online community that archives websites. They are a fantastic resource and include many pieces of detailed technical documentation on the practice of engaging in web archiving. For example, here are detailed explanations of mirroring a website with GNU wget which is the piece of free software I usually use to archive websites.
  • OpenHumans is an online community where people share personal data with each other and with researchers.

Part II: Textual Analyses[edit]

Required Readings:

Optional Readings:

I'm assuming you have at least a rough familiarity with content analysis as a methodology. If your not as comfortable with this, check out the Wikipedia article to start. These help provide more of a background into content analysis (in general, and online):

  • Van Selm, Martine & Jankowski, Nick, (2005) "Content Analysis of Internet-Based Documents." Unpublished Manuscript. [Available in Canvas]
  • Neuendorf, K. A. (2002). The content analysis guidebook. Thousand Oaks, Calif.: Sage Publications. [Available from Instructor]
  • Krippendorff, K. (2005). Content analysis: an introduction to its methodology. Thousand Oaks; London; New Delhi: Sage. [Available from Instructor]

Examples of more traditional content analysis using online content:

Another example of topic modeling from political science:

Week 2: Friday January 17: CDSW Session 0[edit]

As description in the section on technical skills above, I expect everybody who is not comfortable with at least basic programming and data collection to attend the Community Data Science Workshops (Winter 2020) which I am running concurrently with this class.

This session will run from 6-9pm and is the only session which can probably be missed. Please do contact me, however, if you will not be able to attend it.

Week 2: Saturday January 18: CDSW Session 1[edit]

As description in the section on technical skills above, I expect everybody who is not comfortable with at least basic programming and data collection to attend the Community Data Science Workshops (Winter 2020) which I am running concurrently with this class.

This session will run from 9am-3pm. Details on the CDSW Winter 2020 page.

Week 3: Tuesday January 21: (I) Network Analysis and (II) Hyperlink Networks[edit]

Part I: Network Analysis[edit]

Required Readings:

Optional Readings:

Network datasets:

Part II: Hyperlink Networks[edit]

Optional readings:

Tools for collecting hyperlink network data:

  • Issue Crawler — network mapping software by the Govcom.org Foundation, Amsterdam in a group run by Richard Rogers
  • Virtual Observatory for the Study of Online Networks (VOSON) — "web-based software incorporating web mining, data visualisation, and traditional empirical social science methods (e.g. social network analysis, SNA). Text analysis, dataset manipulation and visualisation, and social network analysis (SNA) are available within an integrated environment."

Week 4: Tuesday January 28: (I) Ethnography and (II) Interviews[edit]

Part I: Digital & Trace Ethnography[edit]

Required Readings:

More traditional ethnographic research in online settings:

  • Hines, Christine. 2017. “Ethnographies of Online Communities and Social Media: Modes, Varieties, Affordances.” In The SAGE Handbook of Online Research Methods, edited by Nigel G. Fielding, Raymond M. Lee, and Grant Blank, 2 edition, 401–15. London, UK: SAGE. [Available in Canvas]
  • [Selections] Jemielniak, Dariusz. 2014. Common Knowledge?: An Ethnography of Wikipedia. Stanford, California: Stanford University Press. ["Introduction" and "Appendix A: Methodology"; Available in Canvas]

Material on "Trace" and "network" ethnographies:

Optional Readings:

  • Hine, Christine. 2000. Virtual Ethnography. London, UK: SAGE Publications. [Available from the Instructor]
This is the canonical book-length account and the main citation in this space.
Note: You may also be interest in reading the essay by Hine that boyd is responding to. [Available in Canvas]
  • Hjorth, Larissa, Heather Horst, Anne Galloway, and Genevieve Bell, eds. 2016. The Routledge Companion to Digital Ethnography. New York, NY: Routledge. [Available from the instructor]
  • Sinanan, Jolynna, and Tom McDonald. 2018. “Ethnography.” In The SAGE Handbook of Social Media, 179–95. 55 City Road: SAGE Publications Ltd. https://doi.org/10.4135/9781473984066. [Available through UW libraries]
  • Maxwell, Joseph A. 2002. “Understanding and Validity in Qualitative Research.” In The Qualitative Researcher’s Companion, edited by A. M. Huberman and Matthew B. Miles, 37–64. London, UK: SAGE. [Available in Canvas]
  • Champion, Kaylea, Nora McDonald, Stephanie Bankes, Joseph Zhang, Rachel Greenstadt, Andrea Forte, and Benjamin Mako Hill. 2019. “A Forensic Qualitative Analysis of Contributions to Wikipedia from Anonymity Seeking Users.” Proceedings of the ACM on Human-Computer Interaction 3 (CSCW): 53:1–53:26. https://doi.org/10.1145/3359155. [Available through UW libraries]

These are all other interesting and/or frequently cited examples of Internet-based ethnographies:

Note: To conduct the study reported in this paper the authors created a used a fake profile in order to observe the psychological support offered to participants.
Note: Fantastic more general introduction but takeaways that are more specifically targeted toward people studying virtual reality type environments with virtual physicality.

Charlie's optional readings (virtual world ethnographies):

  • Bainbridge, William Sims. 2010. The Warcraft Civilization: Social Science in a Virtual World. Cambridge, Massachusetts: MIT. [mitpress https://mitpress.mit.edu/books/warcraft-civilization]
  • Nardi, Bonnie A. 2009. My Life as a Night Elf Priest: An Anthropological Account of World of Warcraft. Ann Arbor, Michigan: University of Michigan. [free pdfs https://muse.jhu.edu/book/1093]
  • Pearce, Celia, Tom Boellstorff, and Bonnie A. Nardi. 2011. Communities of Play: Emergent Cultures in Multiplayer Games and Virtual Worlds. The MIT Press. [mitpress https://mitpress.mit.edu/books/communities-play]
  • Boellstorff, Tom, Bonnie Nardi, Celia Pearce, T. L. Taylor, and George E. Marcus. 2012. Ethnography and Virtual Worlds: A Handbook of Method. Princeton: Princeton University Press. [1]

Part II: Online Interviewing[edit]

Required Readings:

  • O’Connor, Henrietta, and Clare Madge. 2017. “Internet-Based Interviewing.” In The SAGE Handbook of Online Research Methods, edited by Nigel G. Fielding, Raymond M. Lee, and Grant Blank, 2 edition, 416–34. London, UK: SAGE. [Available through Canvas]
  • Abrams, Katie M ., and Ted J. Gaiser. 2017. “Online Focus Groups.” In The SAGE Handbook of Online Research Methods, edited by Nigel G. Fielding, Raymond M. Lee, and Grant Blank, 2 edition, 435–50. London, UK: SAGE. [Available through Canvas]
  • Hanna, Paul. 2012. “Using Internet Technologies (Such as Skype) as a Research Medium: A Research Note.” Qualitative Research 12 (2): 239–42. https://doi.org/10.1177/1468794111426607. [Available through UW libraries]
Note: Short article you can basically skim. Read it quickly so you can cite it later.

Optional Readings:

  • boyd, danah. 2015. “Making Sense of Teen Life: Strategies for Capturing Ethnographic Data in a Networked Era.” In Digital Research Confidential: The Secrets of Studying Behavior Online, edited by Eszter Hargittai and Christian Sandvig. Cambridge, Massachusetts: MIT Press. [Available in Canvas]
Note: Strongly focused on ethnographic interviews with tons of very specific details. Fantastic article on interviewing, although perhaps a bit weak on Internet-specific advice.
  • Markham, Annette N. 1998. “The Shifting Project, The Shifting Self.” In Life Online: Researching Real Experience in Virtual Space, 61–83. Rowman Altamira. [Available from instructor]
Note: One of the earliest books on online life and one of the earliest attempts to do online interviewing. This is dated, but highlight some important challenge.

Alternate Accounts:

These texts are largely redundant to the required texts above but do provide a different perspective and examples:

  • Salmons, Janet. 2014. Qualitative Online Interviews: Strategies, Design, and Skills. SAGE Publications. [Preface, TOC, and Chapter 1; Available in Canvas]
This is a book that lays out what claims to be a comprehensive account to online interviewing. I have the book and am happy to loan my copy to anybody in the class that thinks this will be a core part of their research.

Optional readings related to the ethics of identify subjects:

Week 4: Saturday February 1: CDSW Session 2[edit]

As described in the section on technical skills above, I expect everybody who is not comfortable with at least basic programming and data collection to attend the Community Data Science Workshops (Winter 2020) which I am running concurrently with this class.

This session will run from 10 am-4 pm. Details on the CDSW Winter 2020 page.

Week 5: Tuesday February 4: (I) Surveys and (II) Experiments[edit]

Part I: Surveys[edit]

Required Readings:

  • Fricker, Jr., Ronald D., and Katja Lozar Manfreda. 2017. “Sampling Methods for Online Surveys.” In The SAGE Handbook of Online Research Methods, edited by Nigel G. Fielding, Raymond M. Lee, and Grant Blank, 2 edition, 162–83. London, UK: SAGE. [Available in Canvas]
  • Walejko, Gina. 2009. “Online Survey: Instant Publication, Instant Mistake, All of the Above.” In Research Confidential: Solutions to Problems Most Social Scientists Pretend They Never Have, edited by Eszter Hargittai, 101–21. Ann Arbor, MI: University of Michigan Press. [Available in Canvas]
  • Konstan, Joseph A., B. R. Simon Rosser, Michael W. Ross, Jeffrey Stanton, and Weston M. Edwards. 2005. “The Story of Subject Naught: A Cautionary but Optimistic Tale of Internet Survey Research.” Journal of Computer-Mediated Communication 10 (2): 00–00. https://doi.org/10.1111/j.1083-6101.2005.tb00248.x. [Free online]
  • Hill, Benjamin Mako, and Aaron Shaw. 2013. “The Wikipedia Gender Gap Revisited: Characterizing Survey Response Bias with Propensity Score Estimation.” PLoS ONE 8 (6): e65782. https://doi.org/10.1371/journal.pone.0065782. [Free online]
  • Salganik, Matthew J., and Karen E. C. Levy. 2015. “Wiki Surveys: Open and Quantifiable Social Data Collection.” PLOS ONE 10 (5): e0123483. https://doi.org/10.1371/journal.pone.0123483. [Free online]
Note: This journalistic account of the research may also be useful.
  • Alperin, Juan Pablo, Erik Warren Hanson, Kenneth Shores, and Stefanie Haustein. 2017. “Twitter Bot Surveys: A Discrete Choice Experiment to Increase Response Rates.” In Proceedings of the 8th International Conference on Social Media & Society, 1–4. #SMSociety17. Toronto, ON, Canada: Association for Computing Machinery. https://doi.org/10.1145/3097286.3097313. [Available through UW libraries]

Optional Readings:

  • Van Selm, Martine, and Nicholas W. Jankowski. 2006. “Conducting Online Surveys.” Quality and Quantity 40 (3): 435–56. https://doi.org/10.1007/s11135-005-8081-8. [Available through UW Libraries]
  • Vehovar, Vasja, and Katja Lozar Manfreda. 2017. “Overview: Online Surveys.” In The SAGE Handbook of Online Research Methods, edited by Nigel G. Fielding, Raymond M. Lee, and Grant Blank, 2 edition, 143–61. London, UK: SAGE. [Available in Canvas]
  • Kaczmirek, Lars. 2017. “Online Survey Software.” In The SAGE Handbook of Online Research Methods, edited by Nigel G. Fielding, Raymond M. Lee, and Grant Blank, 2 edition, 203–19. London, UK: SAGE. [Available in Canvas]
  • Toepoel, Vera. 2017. “Online Survey Design.” In The SAGE Handbook of Online Research Methods, edited by Nigel G. Fielding, Raymond M. Lee, and Grant Blank, 2 edition, 184–202. London, UK: SAGE. [Available in Canvas]
  • Mavletova, Aigul, and Mick P. Couper. 2014. “Mobile Web Survey Design: Scrolling versus Paging, SMS versus E-Mail Invitations.” Journal of Survey Statistics and Methodology 2 (4): 498–518. https://doi.org/10.1093/jssam/smu015. [Available through UW libraries]'
  • Yun, Gi Woong, and Craig W. Trumbo. 2000. “Comparative Response to a Survey Executed by Post, e-Mail, & Web Form.” Journal of Computer-Mediated Communication 6 (1): 0–0. https://doi.org/10.1111/j.1083-6101.2000.tb00112.x. [Free online]
  • Hargittai, Eszter, and Chris Karr. 2009. “WAT R U DOIN? Studying the Thumb Generation Using Text Messaging.” In Research Confidential: Solutions to Problems Most Social Scientists Pretend They Never Have, edited by Eszter Hargittai, 192–216. Ann Arbor, MI: University of Michigan Press. [Available in Canvas]

If you don't have a background in survey design, these two have been recommended by our guest speaker as good basic things to read:

  • Krosnick, Jon A. 1999. “Maximizing Measurement Quality: Principles of Good Questionnaire Design.” In Measures of Political Attitudes, edited by John P. Robinson, Phillip R. Shaver, and Lawrence S. Wrightsman. New York: Academic Press.
  • Krosnick, Jon A. 1999. “Survey Research.” Annual Review of Psychology 50 (1): 537–67. https://doi.org/10.1146/annurev.psych.50.1.537. [Available through UW libraries]

Tools for doing mobile surveys:

Part II: Experiments[edit]

Required Readings:

Optional Readings:

This piece is set as the intersection of networks and experiments. It's very important but is probably too technical to assign for the whole c.ass
  • Kohavi, Ron, Alex Deng, Brian Frasca, Toby Walker, Ya Xu, and Nils Pohlmann. 2013. “Online Controlled Experiments at Large Scale.” In Proceedings of the 19th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 1168–1176. KDD ’13. Chicago, Illinois, USA: Association for Computing Machinery. https://doi.org/10.1145/2487575.2488217. [Available through UW libraries]'
  • Reinecke, Katharina, and Krzysztof Z. Gajos. 2015. “LabintheWild: Conducting Large-Scale Online Experiments With Uncompensated Samples.” In Proceedings of the 18th ACM Conference on Computer Supported Cooperative Work & Social Computing, 1364–1378. CSCW ’15. New York, NY, USA: ACM. https://doi.org/10.1145/2675133.2675246. [Available through UW libraries]'
  • Zhu, Haiyi, Amy Zhang, Jiping He, Robert E. Kraut, and Aniket Kittur. 2013. “Effects of Peer Feedback on Contribution: A Field Experiment in Wikipedia.” In , 2253. ACM Press. https://doi.org/10.1145/2470654.2481311. [Available through UW libraries]'
  • Zhang, Xiaoquan (Michael), and Feng Zhu. 2011. “Group Size and Incentives to Contribute: A Natural Experiment at Chinese Wikipedia.” American Economic Review 101 (4): 1601–15. https://doi.org/10.1257/aer.101.4.1601. [Available through UW libraries]'
  • Weninger, Tim, Thomas James Johnston, and Maria Glenski. 2015. “Random Voting Effects in Social-Digital Spaces: A Case Study of Reddit Post Submissions.” Pp. 293–297 in Proceedings of the 26th ACM Conference on Hypertext & Social Media, HT ’15. Guzelyurt, Northern Cyprus: Association for Computing Machinery.

Week 6: Tuesday February 11: Crowdsourcing, Digital Labor Markets, and Human Computation[edit]

Note: I've marked things as [Required] below if they are required because I thought it made more sense to keep the topics groups of articles below intact.

MTurk documentation and guidelines:

Overviews of MTurk and issues of data quality:

Culture and work conditions for Turkers:

Systems to approve Turker experiences:

  • Salehi, Niloufar, Lilly C. Irani, Michael S. Bernstein, Ali Alkhatib, Eva Ogbe, Kristy Milland, and Clickhappier. 2015. “We Are Dynamo: Overcoming Stalling and Friction in Collective Action for Crowd Workers.” In Proceedings of the 33rd Annual ACM Conference on Human Factors in Computing Systems, 1621–1630. CHI ’15. Seoul, Republic of Korea: Association for Computing Machinery. https://doi.org/10.1145/2702123.2702508. [Available from UW libraries]
  • Irani, Lilly C., and M. Six Silberman. 2013. “Turkopticon: Interrupting Worker Invisibility in Amazon Mechanical Turk.” In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, 611–620. CHI ’13. Paris, France: Association for Computing Machinery. https://doi.org/10.1145/2470654.2470742. [Available from UW libraries]

Assignments to complete before class:

The first task is to complete a task a crowd worker:

  • If you are a US citizen: Sign up as a worker on MTurk. Find and complete at least 5 "hits" as a worker on Amazon Mechanical Turk. Note that to do this you will need to create a worker account on Mturk.
  • If you are not a US citizen or if you cannot sign up on MTurk for some other reason: Complete at least 3-4 classification tasks in at least 2 different Zooniverse projects of your choice. Also, complete at least one "study" in Lab in the Wild
  • In either case: Record (write down) details and notes about your tasks: What did you do? Who was the requester? What could you was the purpose of the task (as best you could tell)? What was the experience like? What research applications can you (not) imagine for this kind of system?

The second task is to get ready to launch a task as a requestor. We will design and launch tasks in class but I want you to do the following ahead of time:

  • Create a "requester" account on Amazon Mechnical Turk. Doing so may require up top 48 hours to be approved so please do that immediately so you have it ready to go in class.
  • Put money onto your requestor account to pay workers. A $5 budget should be sufficient for our class. They should take any payment that Amazon does.
  • Think of at least one small classification or coding task (e.g., of Tweets, images, etc) and one human subjects data collection tasks like a survey, a survey experiment, etc, that you would like to run. You will have a budget of $5 to run the task!
  • If running this task will involve some data (e.g., a set of images or URLs, a set of Tweets, etc), collect that material in a spreadsheet before class. If it will involve a survey, create your survey in a Google Form and/or a Survey Monkey or Qualtrics survey before class.

Week 6: Saturday February 15: CDSW Session 3[edit]

As description in the section on technical skills above, I expect everybody who is not comfortable with at least basic programming and data collection to attend the Community Data Science Workshops (Winter 2020) which I am running concurrently with this class.

This session will run from 9am-3pm. Details on the CDSW Winter 2020 page.

Week 7: Tuesday February 18: Consulting Week (i.e., no group meeting)[edit]

During this week, we not meet together. Instead, I will schedule one-on-one in person meetings of an hour with each student individually to catch up with you about your project and to work directly with you to resolve any technical issues you have run into with data collection, etc.

Week 8: Tuesday February 25: (I) Discourse Analysis and (II) Visual Analysis[edit]

Part I: Discourse Analysis[edit]

Required Readings:

Optional Readings:

Part II: Visual Analysis[edit]

Required Readings:

Optional Readings:

Note: This is a two-part (each part is one hour) lecture and tutorial by an expert in computer vision. I strongly recommend watching Part I. I think this gives you a good sense of the nature of the kinds of challenges that were (and still are) facing the field of computer vision and anybody trying to have their computer look at images.

These five papers are all technical approaches to doing image classification using datasets from Internet-based datasets of images like Flickr, Google Image Search, Google Street View, or Instagram. Each of these describes interesting and challenges technical issues. If you're interested, it would be a great idea to read these to get a sense for the state of the art and what is and isn't possible:

Week 9: Tuesday March 3: Consulting Week[edit]

During this week, we will not meet together. Instead, I will schedule one-on-one in-person meetings of an hour with each student individually to catch up with you about your project and to work directly with you to resolve any technical issues you have run into with data collected.

Week 10: Tuesday March 10: Final Presentations[edit]

Administrative Notes[edit]

Your Presence in Class[edit]

As detailed in the section on participation and in my page on assessment, class participation is a critical way that I will assess learning in the class. Obviously, you must be in class in order to participate. If you need to miss class for any reason, please contact me ahead of time (email is best). In the event of an absence, you are responsible for obtaining class notes, handouts, assignments, etc.

Office Hours[edit]

I will hold office hours on Thursdays 1-2 pm in Communications (CMU) 333. In addition to my scheduled office hours, I am generally in my lab in CMU 306. Feel free to stop by at any time or to contact me to arrange a time to meet.

Religious Accommodations[edit]

Washington state law requires that UW develop a policy for the accommodation of student absences or significant hardship due to reasons of faith or conscience, or for organized religious activities. The UW’s policy, including more information about how to request an accommodation, is available at Religious Accommodations Policy. Accommodations must be requested within the first two weeks of this course using the Religious Accommodations Request form.

Student Conduct[edit]

The University of Washington Student Conduct Code (WAC 478-121) defines prohibited academic and behavioral conduct and describes how the University holds students accountable as they pursue their academic goals. Allegations of misconduct by students may be referred to the appropriate campus office for investigation and resolution. More information can be found online at https://www.washington.edu/studentconduct/ Safety

Call SafeCampus at 206-685-7233 anytime–no matter where you work or study–to anonymously discuss safety and well-being concerns for yourself or others. SafeCampus’s team of caring professionals will provide individualized support while discussing short- and long-term solutions and connecting you with additional resources when requested.

Academic Dishonesty[edit]

This includes: cheating on assignments, plagiarizing (misrepresenting work by another author as your own, paraphrasing or quoting sources without acknowledging the original author, or using information from the internet without proper citation), and submitting the same or similar paper to meet the requirements of more than one course without instructor approval. Academic dishonesty in any part of this course is grounds for failure and further disciplinary action. The first incident of plagiarism will result in the student’s receiving a zero on the plagiarized assignment. The second incident of plagiarism will result in the student’s receiving a zero in the class.

Disability Resources[edit]

If you have already established accommodations with Disability Resources for Students (DRS), please communicate your approved accommodations to UW at your earliest convenience so we can discuss your needs in this course.

If you have not yet established services through DRS, but have a temporary health condition or permanent disability that requires accommodations (conditions include but not limited to; mental health, attention-related, learning, vision, hearing, physical or health impacts), you are welcome to contact DRS at 206-543-8924 or uwdrs@uw.edu or disability.uw.edu. DRS offers resources and coordinates reasonable accommodations for students with disabilities and/or temporary health conditions. Reasonable accommodations are established through an interactive process between you, your instructor(s) and DRS. It is the policy and practice of the University of Washington to create inclusive and accessible learning environments consistent with federal and state law.

Other Student Support[edit]

Any student who has difficulty affording groceries or accessing sufficient food to eat every day, or who lacks a safe and stable place to live, and believes this may affect their performance in the course, is urged to contact the graduate program advisor for support. Furthermore, please notify the professors if you are comfortable in doing so. This will enable us to provide any resources that we may possess (adapted from Sara Goldrick-Rab). Please also note the student food pantry, Any Hungry Husky at the ECC.

Credit and Notes[edit]

This will be the third time I have taught this course at UW in its current form. This syllabus draws heavily from previous versions. Syllabuses from earlier classes can be found online at:

This syllabus was inspired by and borrows with permission from, a syllabus from an earlier version of this class taught by Kirsten Foot. Professor Foot last taught the course in Spring 2014.

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