\section{Related Work}
\ms{For Noah, Katharina: is there any thing missing?}

While there is a wealth work that studies the association between language and author mental state, most has focused on self-narratives \cite[e.g., diaries, social media posts][]{Pennebaker1999-gm,Hirsh2009-ic,schwartz2013personality}.
Little work has analyzed the author characteristics in prompted writing, with the essay analyses by \citet{pennebaker2014small} being a notable exception.

Despite cooperative story writing and natural language generation research heavily relying on human ratings of text for evaluation \cite[e.g.,][]{howNotToEval?,Other?}, a majority have ignored the rater's identity for such tasks.
On the other hand, other work has previously studied the effect of rater's identity on annotation tasks \cite{?}.
Our work is the first to investigate the effect of a rater's identity on the quality ratings of stories.

% \ms{Author demographics in text is a thing that people do (cite comp socioling, pennebaker, lucie flekova stuff, maybe survey paper?), but (1) not a lot of work focuses on stories, and (2) we're the first to look at reader demographics for evaluating stories (people usually just trust human eval at face value without looking at variation)
% Also mention (other than framing of stories).
% }



% crowdsource writing 
\ms{My sense is that this isn't relevant enough to include (esp. if we're over page limit)} \ta{Sure, happy taking it out.}
Most past work on collecting story writing from individuals have either scraped stories from online forums~\cite{writing prompts} or sourced small stories from paid crowdsourcing~\cite{roc}. Our focus on collecting author and rater demographics is unique and affords our analysis of the influence of both on story quality ratings.

Work in crowdsourcing has also explored collective story writing~\cite{mech novel} or writing support tools powered by crowdworkers~\cite{soylent}. Technological support for writing has also included NLG power systems~\cite{clark2018creative}, and systems for breaking writing tasks down into small microtasks to be completed in short moments, such as while mobile~\cite{}. In this work we instead focus on writing setup and reader and writer traits on story writing quality rather than introduce new writing support systems. 


% \ta{Crowdsourcing writing: Mech Novel, Soylent, Ensemble, ROC. Most of these are paid, interested to see how unpayed would work, also rarely focuses on author demographics, just their writing. }
