than five years the U.S. has added jobs every month, more than two million each
year, despite the development of more
capable machines. Humans are creative
Jonathan Grudin, Redmond, WA
What NBA Players’ Tweets
Say About Emotion
The article “Hidden In-Game Intelligence in NBA Players’ Tweets” by
Chenyan Xu et al. (Nov. 2015) lacked, in
my opinion, a complete understanding
of the topics it covered. The measures it
cited were not adequately reported; for
example, not clear was what the dependent variable consisted of, so readers
were unable to judge what the coefficients mean or the adequacy of a 1% adjusted R2 in Table 5, an effect size that
was most likely meaningless.
Moreover, the sample size was not
explained clearly. There were initially
91,659 tweets in the sample, and 266
players tweeted at least 100 tweets during the season in question. Other than
in a small note in Table 1, the article did
not mention there are only 82 games
in a regular NBA season, resulting in
at least 1. 22 tweets per game for those
266 players; this is not an appropriate sample size, and the distribution is
most likely a long tail. With 353 players
tweeting and 82 games, the sample size
should be 28,946 player-games (the unit
of analysis), yet the reported sample size
was a fraction of that— 3,443 or 3,344.
That would be fewer than 10 games per
player and not an adequate sample size.
Also unclear was if players with more
tweets before a game can have higher
emotion scores, as this measure seems
to be an aggregate; the article said, “The
total score represents a player’s mood
… The higher the aggregated score, the
more positive the player’s mood,” emphasis added. More tweets do not mean
more emotion. The article also did not
address if there is a difference between
original tweets and replies to other
The article also made a huge assump-
tion about the truthfulness of tweets.
NBA players are performers and know
their tweets are public. The article dis-
missed this, saying, “Its confounding
effect is minimal due to players’ sponta-
neous and genuine use of Twitter,” yet
offered no evidence, whether statistical,
theoretical, or factual, that this is so.
The article coded angry emoticons
(such as >:-o ) to the negative mood
condition, as in Table 3. This emoticon-
mood mapping is incorrect, as anger
can be positively channeled into focus
and energy on the court. Smileys and
frowns were given a weighting of +/− 2
on a scale of + 5 to − 5, but not explained
was why this is theoretically defensible.
NBA coaches do not seek to maxi-
mize performance at the level of an
individual player but at the level of a
team as a whole across an entire game
and season. Bench players usually can-
not replace starting players; the starters
start for very good reasons.
The article’s conclusion said the au-
thors had analyzed 91,659 tweets, yet
footnote b said, “Of the 51,847 original
posts, 47,468 were in English,” imply-
ing they analyzed at most 87,280 tweets.
Restating the number 91,659 was itself
misleading, as tweets were not the unit
of analysis—player-games were—and
the authors had only 3,443 such obser-
vations, at most.
The one claim reviewers and editors
should definitely have caught is in foot-
note c: A metric that can capture the un-
quantifiable? I am so speechless I might
have to use an emoticon myself.
Nathaniel Poor, Brooklyn, NY
Our study explored whether and how NBA
players’ tweets can be used to extract
information about players’ pre-game
emotional state (X) based on the psychology
and sports literature and how it might
affect players’ in-game performance (Y).
To generate X for a player before a game,
we purged pure re-tweets, information-oriented tweets, and non-English tweets.
Based on the remaining valid tweets, we then
extracted, aggregated, and normalized the
data, as in Table 5. We still find it intriguing
X explains up to 1% of the total variations in
Y, whereas other standard variables explain
Chenyan Xu, Galloway, NJ,
Yang Yu, Rochester, NY, and
Chun K Hoi, Rochester, NY
Communications welcomes your opinion. To submit
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less, and send to email@example.com.
© 2016 ACM 0001-0782/16/02 $15.00
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