Supercharge your paper submissions with 5 powerful LaTeX snippets.
I've perfected them in 10,000+ hours of coding.
Unlock the secret to lightning-fast paper editing. ↓
1. Use the right documentclass options before submitting your paper to CHI
How it works:
- Comment out this line of code with % \documentclass[sigconf,authordraft]{acmart}
- Then add \documentclass[manuscript,screen,review, anonymous]{acmart}
This is the right review format.
2. Format nicer-looking research questions
How it works:
Load in LaTeX doc header:
\usepackage{enumerate}
\usepackage[shortlabels]{enumitem}
Type in LaTeX doc body:
\begin{enumerate}[label= \textbf{RQ\arabic*:}]
\item x
\end{enumerate}
3. Make sure to always define acronyms before use
How it works:
Load in LaTeX doc header:
\usepackage[nolist]{acronym}
Define acronyms:
\begin{acronym}
\acro{ANOVA}{Analysis of Variance}
\end{acronym}
Write the acronym in your text like this:
"We conducted an \ac{ANOVA}."
4. Create pretty quotes for qualitative findings
How it works:
Define a new command called \quoting:
\newcommand{\quoting}[2][P]{``\emph{#2}''\emph{[\textbf{#1}]}}
Use the command like this to quote participants:
\quoting[P13]{This prototype rocked my world.}.
TL;DR: 5 drops of my secret LaTeX sauce to write smooth #chi2023 papers
1. Use the right documentclass options for submission 2. Format nicer-looking RQs 3. Always define acronyms before use 4. Create pretty quotes for qualitative findings 5. Leave highlighted comments
Done like disco.
If you enjoyed this thread:
1. Follow me @acagamic for more tips on writing research papers 2. Join more than a thousand people on my newsletter (link in profile).
I once watched a researcher present at a conference.
Perfectly polished slides. Flawless delivery. Zero connection.
Then someone else got up-stumbled through their intro, admitted they weren't sure about one of their findings, showed messy preliminary data.
That's the one everyone wanted to talk to afterward.
Here's what I've learned helping academics turn research into content:
The polished version gets you respect.
The real version gets you readers.
I see this everywhere now.
Researchers afraid to share:
- Failed experiments
- Questions they don't have answers to
- The messy middle of their thinking
They think it undermines their credibility.
It does the opposite.
When you share the uncertainty, the struggle, the "I don't know yet" in public, that's when people lean in. Because that's what real research looks like. That's what real thinking looks like.
Your audience isn't looking for another perfectly packaged insight. They're suffocating in those.
Did you place a new brick on the wall of knowledge?
Or did you just describe the bricks already there?
Scientific merit isn't volume.
It's contribution.
Here's how to know the difference:
Repetition disguises itself as rigour.
• You run the same study in a different population.
• You replicate findings everyone already accepts.
• You add one more variable to an exhausted model.
It feels productive.
But you're repainting the same wall.
Contribution looks different:
• It answers a question nobody else asked
• It challenges assumptions your field takes for granted
• It opens doors instead of confirming what's behind them