Bibliographic record
Abstract
During 2017, Donald J. Trump posted an average of more than seven tweets a day.In the immediately previous years, Trump had employed tweets as a campaign strategy in his run for the U.S. presidency (Enli, 2017;Lee & Lim, 2016;Ott, 2017;Wang, Luo, Niemi, Li,& Hu, 2016), and he continued to tweet at a high rate once in office.Twitter is a relatively new communication technology.Presidents Obama and Trump both tweeted, but Trump tweeted at a much higher rate.As reported in sources described in the Methodology section below, Obama tweeted 321 times in his last 19 months in office, while Trump tweeted 2,602 times in his first year.This research addresses the pleasantness/unpleasantness of the language in Trump's 2017 tweets.It questions whether this pleasantness/unpleasantness is related to tweet characteristics (such as their length in number of words, their inclusion of a hash tag, or their status as a reply) or to information included within tweet text (such as an American flag emoji, an exclamation mark, or a mention of CNN).These variables are employed to predict pleasantness, and pleasantness in turn is employed with the other predictors to identify popular tweets that were retweeted at a high rate. Measuring the Pleasantness/Unpleasantness of TweetsSentiment analysis is an approach that examines the emotion conveyed-in tweets, in this case-by looking at the words in them.Some forms of sentiment analysis depend on lists of words indicative of various emotional states; they evaluate the occurrence of these words in tweets and infer emotionality from occurrence, sometimes in very sophisticated ways (e.g., Mohammad, Kiritchenko, & Zhu, 2013;Saif, Fernandez, He, & Alani, 2016).The approach employed in this research depends instead on the rated emotional connotations of many thousands of words, including some very common ones (Whissell, 2009).Ratings contained in the Dictionary of Affect in Language (Whissell, 2009) 1 were provided by multiple raters.They had been obtained, several years before the current research, in a totally independent setting where participants rated context-free words on their pleasantness.The scale employed in this research is a simple linear transformation of the original scale.The tool has been applied to many different types of data: from Shakespeare (Whissell, 2010a) to President Clinton's communications (Whissell, 2010b). 1 Referred to in the rest of this article as the Dictionary, or the Dictionary of Affect Abstract: President Trump's 2017 tweets (N = 2,602) were studied in terms of their pleasantness (as measured by the Dictionary of Affect; Whissell, 2009), their popularity (defined in terms of retweets and likes), message characteristics (e.g., message length), and message content (e.g., whether the message included mentions of Melania, CNN, Hillary Clinton, or Democrats).Pleasantness and popularity were significantly predicted (p<.001) on the basis of message characteristics and content variables.Table 3 highlights the presence of two types of message: pleasant/unpopular tweets and unpleasant/popular ones.On the basis of their characteristics and contents, the first type of tweet was labeled as celebratory/ congratulatory and the second as antagonistic/accusatory. Antagonistic/accusatory tweets tended to mention Trump's political opponents (e.g., Clinton, Democrats, CNN) and to be posted between 7:00 pm and midnight.Trump's tweets were mildly pleasant in tone, but not as pleasant as tweets posted by President Obama in 2015-2016.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.098 | 0.066 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".