A Formula for Academic Papers: Related Work

The Related Work section of an academic paper is often the section that graduate students like writing the least. But it is also one of the most important sections to nail as the paper heads out for review. The Related Work section serves many purposes, several of which relate directly to reviewing:

• The person handling the submission will use the referenced papers to identify good reviewers,
• Reviewers will look at the references to confirm that the submission cites the appropriate work,
• Everyone will use the section to understand the paper's contributions given the state of existing research, and
• Future researchers will look to the Related Work section to identify other papers they should read.

Related Work: Find It If You Can

Find It If You Can: A Game for Modeling Different Types of Web Search Success Using Interaction Data
Mikhail Ageev, Qi Guo, Dmitry Lagun, Eugene Agichtein
SIGIR 2011

A lot of recent search research makes use of large-scale query log analysis. This poses a challenge for researchers who are not affiliated with a major search engine, because there are very few public logs available research community. Find It If You Can, by Ageev et al., provides a nice example of how information retrieval researchers might gather their own search logs. And in doing so, it also addresses a persistent challenge with using logs: the fact that while we know what users are doing, we don't actually know what they are thinking.

Related Work: Ensemble

Ensemble: Exploring Complementary Strengths of Leaders and Crowds in Creative Collaboration
Joy Kim, Justin Cheng, Michael S. Bernstein
CSCW 2014

This paper looks at crowd-supported creative writing. It presents a system, called Ensemble, that supports structured creative writing. There are a number of examples of massively collaborative writing, including:

• Ensemble: Structured creative writing, with the author leading crowd workers in the task.
• The Collabowriters: An experiment in collaborative novel writing. Users write short sentence candidates for the next sentence in the novel, and then vote on which one is the best.
• FoldingStory: A group storytelling game.
• Massively Distributed Authorship of Academic Papers: An experiment in collaborative academic writing by Bill Tomlinson and 29 others for alt.chi at CHI 2012.
• Soylent: A crowd-powered word processor that uses Mechanical Turk workers to help writers proofread and shorten their document.

WSDM 2014 Trip Report

Last week I attended the Seventh ACM International Conference on Web Search and Data Mining (WSDM 2014) in New York City. The conference hotel was located right in Times Square, and I enjoyed visiting Cornell Tech, eating delicious food, and catching up with college friends. I also enjoyed attending sessions. WSDM is single track, which means the research being presented isn’t always directly relevant to everyone, but conference attendees have a shared experience and get exposed to research they might not otherwise.

CSCW 2014 Trip Report

CSCW 2014 in Baltimore, MD was a lot of fun. There was so much good work presented that I often had a hard time choosing which sessions to attend. However, the common themes I paid attention to while there were:

Related Work: TaskGenies

TaskGenies: Automatically Providing Action Plans Helps People Complete Tasks

Nicolas Kokkalis, Thomas Köhn, Johannes Huebner, Moontae Lee, Florian Schulze and Scott Klemmer

TOCHI, 20 (5): 2013.

The proverb, “A journey of a thousand miles begins with a single step,” is attributed to the sixth century BCE philosopher Laozi. People have attempted to accomplish large tasks by decomposing them into manageable parts for millennia, and modern approaches to time management continue to take Laozi’s words to heart. For example, agile software development breaks large, failure-prone software projects into smaller tasks with corresponding time estimates.

Related Work: Measuring the Crowd Within

Measuring the Crowd Within: Probabilistic Representations Within Individuals
By Edward Vul and Harold Pashler
Psychological Science, 19(7): 2008.

Crowdsourcing researchers are familiar with the idea of the wisdom-of-crowds, whereby information from multiple different people can be pooled to produce a better outcome than any individual could produce alone. Vul and Pashler’s paper provides some fun common examples of wisdom-of-crowds in action, including:

• A group of people can guess the weight of an ox better than an individual.
• Polling the audience on Who Wants to be a Millionaire is successful.
• The combination of multiple financial forecasts better than individual forecasts.

This paper builds on the notion of the wisdom-of-crowds to ask: Can information collected multiple times from the same individual can also improve the outcome? The authors study this by asking participants to answer questions like, “What percentage of the world’s airports are in the United States?” twice. They find that when they average both answers they get a more accurate response than if they were to look at either answer individually.