I like Twitter and I like scientific papers. So I like this new paper by University of the Philippines researchers Christian M. Alis and May T. Lim: Spatiotemporal variation of conversational utterances on Twitter Using Twitter's API, they downloaded 229 million 'conversational' tweets from 2009-2012. They defined as 'conversational' any tweet starting with the character @. These are messages directed at one or more specific users, although anyone can read them. The headline finding was that @ tweets are steadily getting shorter, as can be seen in the graph on the right showing linear downward trends over the three quartiles of the distribution:
Shorter how? The difference is mainly due to people using fewer words. The length of the most-used words didn't change very much, but the number of words per tweet fell:
So tweeters are becoming less verbose (within any given tweet), which the authors suggest might represent the development of more economical linguistic conventions adapted to Twitter. But is this true of everyone? Broadly speaking, yes - at least in terms of English-language tweets. The slope of the decline was similar in the USA and in tweets originating from the rest of the world. However within the US, Alis and Lim found a remarkable state-by-state variability (Bear in mind however that few tweets have geolocatable info, so the sample sizes,and representativeness, of these data here are lower)
The average @ utterence from Louisiana is just 27 characters, compared to 43 in Montana. Why? State average income and educational attainment were weak predictors of length, but Alis and Lim say that the biggest factor they found was... race. States with more African-Americans produced shorter tweets. The authors say that
A possible explanation is that Blacks converse more distinctly and more characteristically than other racial groups. Since utterances were only weakly correlated with income and education then perhaps the shorter utterance lengths is a characteristic of their race – perhaps pointing towards the controversial language of Ebonics [26]. The strong correlation does not imply causality however...
Alis CM, & Lim MT (2013). Spatio-temporal variation of conversational utterances on twitter. PloS one, 8 (10) PMID: 24204968
