\section{Related Work}

\subsection{Compensated Crowd-sourced writing}
Useful for gathering lots of controlled text, suffers from low quality in many cases, forcing complex workflows or templates. 
\begin{enumerate}
    \item Basic text generation: Image + Video annotations (ImageNet, COCO, Survey paper on visual datasets)
    \item Simple writing: Short (3-10 sentences) written by single individual, usually for NLI or NLG (ROC, Visual Storytelling, SNLI, Machine in loop)
    \item High-level writing: Complex collaborative writing and editing (Soylent, MNovel, WearWrite). Possibly add in other complex crowdsourcing tasks (like IdeaHound) 
\end{enumerate}

\subsection{Collecting uncompensated writing}
Most cases of this is scraping text online: Useful for gathering natural writing, but hard to control for anything, making comparative analysis pretty difficult (controlling for author, topic, etc.). Researcher has no real control over the text getting written, except in very basic cases (e.g., Image annotation)
\begin{enumerate}
    \item Naturally occuring: Twitter, QA sites, reviews, articles, Wikipedia
    \item Basic: Zooniverse, CitSci for image annotation
\end{enumerate}

Overall hole: Paid crowdsourcing for text has covered the continuum of basic, short snippets of text all the way up to novels and journal articles; however, at each level there are examples of intense difficulties in quality control. These complex workflows work at times, but there still exists artifacts of low quality writing in numerous NLP datasets (Story Cloze, SNLI), suggesting that the workflows do not completely control quality. Gathering natural text online provides large amounts of good data, but researchers cannot adequately control this generation, limiting the possibilities for analyzing this text. Our methodology aims to combine the control afforded by crowdsourcing, but with the natural and high quality aspects of intrinsically motivated writing with volunteer-based crowdsourcing. 

\cite{banerjee2014keystroke} measured keystroke patterns for deception rating.


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