Cluttered writing: adjectives and adverbs in academia


Scientific writing is about communicating ideas. Clutter doesn’t help—texts should be
as simple as possible. Today, simplicity is more important than ever. Scientist are overwhelmed
with new information. The overall growth rate for scientific publication over
the last few decades has been at least 4.7% per year, which means doubling publication
volume every 15 years. How do we keep up with the literature?
We can use computers to extract meaning from texts (Hopkins and King 2007). Better yet,
I propose here, we should be writing research in machine readable format, say, using
Extensible Markup Language (XML). I think, it is the only way for scientists to cope with
the volume of research in the future. But the first step is to start writing as simply as
possible to minimize the volume and maximize the meaning. Readability of scientific
writing matters not only for scientists. Readable scientific writing could reach wider
audience and have a bigger impact outside of academia.

So how do we produce readable and clean scientific writing? One of the good elements
of style is to avoid adverbs and adjectives. Adjectives and adverbs sprinkle
paper with unnecessary clutter. This clutter does not convey information but distracts and
has no point especially in academic writing, say, as opposed to literary prose or poetry.
William Zinnser, one of the writing experts, advises

Why measuring readability by counting adjectives and adverbs? There are many
readability measures, for instance: Gunning Fog Index, Automated Readability Index,
Coleman–Liau Index, Flesch–Kincaid Reading Ease, Flesch–Kincaid Grade Level, SMOG
Index, FORCAST Readability Formula. They are based on counts of words, difficult words
(many syllables), and sentences. And the calculated measure is usually a grade level
required to understand the text. I did not use these measures for two reasons. First, to
calculate these indices I would need full texts of published research, and it appears that as
of 2012 I cannot bulk download enough full texts to have a representative sample of a
discipline. Second, counting syllables is not a trivial task, and it appears that there are
many ways to do it, and the software is not very mature.

At the same time, adjectives and adverbs counts are a relatively useful measure. They
can be calculated using mature Natural Language Toolkit module for Python. 
NLTK is a module for Python programming language that can be used for
analysis of human language, for instance, to calculate proportion of adjectives and adverbs
in text. Both NLTK and Python are free and run on Linux, Mac, and Windows. They can
be downloaded from python.org. You will also find
extensive documentation and tutorials at the above addresses. NLTK comes with a number
of dictionaries that can be used to identify parts of speech, say adjectives and adverbs.
I use data from JSTOR Data For Research. The sample is about
1,000 articles randomly selected from all articles published in each of seven academic
fields between 2000 and 2010. I made the following selection from JSTOR:

Content type: Journal (to analyze research, not the other option: Pamphlets)
2. Page count: (5–100) (to avoid short letters, notes, and overly long essays; fewer than
five pages may not offer enough to evaluate text, and longer than 100 may have a
totally different style than the typical one for a given field)
3. Article type: Research article (other types such as book reviews may contain lengthy
quotes, etc)
4. Language: English
5. Year of Publication: (2000–2010) (only recent research; did not select 2011, 2012,
since for some fields JSTOR does not offer most recent publications—the number of
available articles in most recent years dramatically drops, based on a JSTOR graph
available at the selection).

I identify parts of speech using Penn Tree Bank in Python NLTK module. I
calculate the proportion of adjectives and adverbs for each academic discipline, and divide
it by the smallest proportion, so that results show proportion increase over the discipline
with the smallest proportion of the adjective–adverb clutter. Figure 1 shows that natural
science uses the fewest adjectives and adverbs, while social science uses the most (about
15% more than natural science).

Proportion of adjectives
and adverbs in published research
by academic discipline group
relative to the field with the
smallest proportion. 95%
confidence intervals shown.

Is there a reason that a social scientist cannot write as clearly as a natural scientist?
Again, adjectives and adverbs are often meaningless and sometimes misleading. And there
is a software to check for the proportions of parts of speech: Python’s NLTK.
Following Mark Twain, the scientists should kill much of the adjectives and adverbs to
make the academic prose readable and spare us from the unnecessary increase in the
volume of research output.