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The hallmark of President Trump's Twitter feed is that it sounds like him - grammatical miscues and all. But it's not always Trump tapping out a Tweet, even when it sounds like his voice. West Wing employees who draft proposed tweets intentionally employ suspect grammar and staccato syntax in order to mimic the president's style, according to two people familiar with the process. They overuse the exclamation point! They Capitalize random words for emphasis. Fragments. Loosely connected ideas. Trump's staff has become so adept at replicating the President's tone that people who follow his feed closely say it is getting harder to discern which tweets were actually crafted by Trump sitting in his bathrobe and watching "Fox & Friends" and which were concocted by his communications team. Staff-written tweets do go through a West Wing process of sorts. When a White House employee wants the president to tweet about a topic, the official writes a memo to the president that includes three or four sample tweets, according to those familiar with the process. Those familiar with the process wouldn't fess up to which tweets were staff-written. But an algorithm crafted by a writer at The Atlantic to determine real versus staff-written tweets suggested several were not written by the president, despite the unusual use of the language.

Analyses show that Trump uses his android phone to tweet, while his staff uses an iPhone to write campaigns, announcements, and other business tweets. Angrier and exaggerated words like "bad" and "weak" tended to come from Trump's android writing, and overall, the majority of negative sentiment in tweets were from Trump's android, not his staff's iPhone.

In the article, Stuart McGurk talks about how in Trump's tweets, the words that be used that is usually used in the empathic way, does not communicate Trump's actual feelings. In the article, it is stated that Trump used the word sorry in 72 tweets, but never actually used in the ways were someone would feel sorrow for something. Instead, for example, he used it in a way were if Obama said sorry, he would have more respect for him. See how he was able to use a sympathetic word but not in the way that he is feeling sorry. Also, in certain circumstances, the wording that Trump uses is not appropriate. For example, in light of the Las Vegas shooting, Trump tweeted, "My warmest condolences and sympathies to the victims and families of the terrible Las Vegas shooting." No one would use the phrase "Warmest Condolences" in this situation.

Yikes: "warmest condolences"! Our president: Master Malaprop. LOL!

"According to a paper delivered at the annual meeting of the American Dialect Society in January by Brice Russ, a graduate student at Ohio State University, the 200 million or so messages posted each day in the supposedly placeless world of Twitter may end up being a rich source of information about regional difference. ... it may allow them to track linguistic patterns on a vast scale and in something close to real time, identifying phenomena that can then be investigated more deeply by traditional fieldwork."

Nikolas Cruz killed 17 people in the Parkland, Florida mass school shooting. President Trump tweeted about the incident and called Cruz a "savage sicko" and an "insane monster" who acts "nuts" and "crazy". Cruz was diagnosed with mental disorders but those disorders could not have predicted he would be violent. Trump's tweet was seen by many experts and was said that could be harmful to other people who have disorders but are not voilent. Many people have notified the issue and talked about how the presidents use of language could affect many people.

Two graduate economics students from Washington State University used Twitter to analyze how sports pundits' reputation was affected by their confidence and accuracy at predicting the outcomes to sporting events. They analysed tweets in which professional pundits and fans made predictions about the winners of a series of high-profile baseball and American Football matches. Each tweet was given a "confidence" rating depending on its language, with words like "destroy" and "annihilate" scoring higher than "beat", for example. Both the pundits and fans' predictions were worse than chance, with the professional analysts only proving correct 47 per cent of the time and amateurs 45 per cent of the time. Yet pundits' confidence was measured as 50 per cent higher than amateurs, and they gained more followers on the networking website as a result, the researchers said. Presenting their findings at the Annual Meeting of the Academy of Economics and Finance earlier this year, the researchers explained that being confident could increase a pundit's following by 17 per cent, while predicting every game correctly only raised it by 3.4 per cent.There was a similar pattern among amateurs, with brash people increasing their following by 20 per cent but correct guesses only raising it by 7.3 per cent. In general, pundits are better served by being brash and making people excited, they claimed.

"The verb tweet is gradually developing its own syntax according to what it means and what its users regard as its combinatory possibilities. That is a really interesting, though unintended, large-scale natural experiment in how syntactic change works."

Charting linguistic change was once a painstakingly slow task, but a new analysis of nearly one billion Tweets - presented on 17 April 2018 at the Evolang International Conference on Language Evolution in Torun, Poland - now offers us an unprecedented glimpse of this process in action. According to this new research, most of the more recent coinages will have originated in one of five distinct hotspots that are driving American English through continual change. More than 20% of Americans were using Twitter at the time of the study - and each Tweet is timestamped and geocoded, offering precise information on the time and place that particular terms entered conversations. The researcher behind the study, Jack Grieve at the University of Birmingham, UK, analysed more than 980 million Tweets in total - consisting of 8.9 billion words - posted between October 2013 and November 2014, and spanning 3,075 of the 3,108 US counties. From this huge dataset, Grieve first identified any terms that were rare at the beginning of the study (occurring less than once per billion words in the last quarter of 2013) but which had then steadily risen in popularity over the course of the following year. He then filtered the subsequent list for proper nouns (such as Timehop) and those appearing in commercial adverts, and he also removed any words that were already in Merriam-Webster Dictionary. Acronyms, however, were included. The result was a list of 54 terms, which covered everything from sex and relationships (such as "baeless" - a synonym for single), people's appearance ("gainz" to describe the increased muscle mass from bulking up at the gym), and technology ("celfie" - an alternative spelling of selfie). Others reflected the infiltration of Japanese culture (such as "senpai", which means teacher or master). They also described general feelings, like "litt" (or "litty" - which means impressive or good - or affirmations such as "yaaaas

Bradley Hayes, a post-doc student at the Massachusetts Institute of Technology, has invented @DeepDrumpf, an amusing bit of artificial intelligence. DeepDrumpf is a bot trained on the publicly available speech transcripts, tweets, and debate remarks of Donald Trump. Using a machine learning model known as a Recurrent Neural Network, the bot generates sequences of words based on priming text and the statistical structure found within its training data. Created to highlight the absurdity of this election cycle, it has amassed over 20,000 followers and has been viewed over 12 million times -- showcasing the consequences of training a machine learning model on a dataset that embodies fearmongering, bigotry, xenophobia, and hypernationalism. Here's a sample tweet: "We have to end education. What they do is unbelievable, how bad. Nobody can do that like me. Believe me."

"Among Twitter's triumphs is the reinvention of self-mockery. By hijacking a feature known as the hashtag, Twitter users have found new ways to inject tiny packages of self-directed sarcasm into their tweets."

"Microbloggers may think they're interacting in one big Twitterverse, but researchers at Carnegie Mellon University's School of Computer Science find that regional slang and dialects are as evident in tweets as they are in everyday conversations."

allows you to enter your twitter handle and have your tweets fed through a language analysis program for psychological profile (developed by researchers in psychology at UT Austin and University of Aukland).

Quinn Norton is a technology writer whose job offer from The New York Times was rescinded after tweets from her past caused backlash on social media. In her essay, Norton describes how the controversy built and destroyed a falsely-constructed version of herself. The article talks about the potential perils of social media use, including context collapse, where online culture that was meant for a particular in-group becomes disseminated to other groups via social-media platforms. Consequently, it can be taken out of context and recontextualized easily and accidentally.

Twitter - the social network where users write tweets of 140 characters is changing how poets and novelists write. They are being forced to strip down language to its bare minimum.

Why has Twitter spawned a whole twitterverse of new words from tweet cred to twitterrhoea? This article examines the birth of Twitter-based neologisms, offering some theories underlying the surge of tw- prefixed words.

That's the conclusion of new research from the UK's Cambridge University Press, which has looked at the way we talk about men and women in sport. Analyzing over 160 million words from decades of newspapers, academic papers, tweets and blogs, the study finds men are three times more likely than women to be mentioned in a sporting context, while women are disproportionately described in relation to their marital status, age or appearance.

Throughout a series of tweets recently, writer/producer Eric Heisserer explained not only how the circular speech symbols came to be, but also the "bespoke logogram analytic code" that translated the language when the cameras were rolling. "In several shots in the film, the analytics you see are working in real-time to dissect a logogram," Heisserer writes.

Donald Trump has begun casting all unfavorable news coverage as fake news. In one tweet, he even went so far as to say that "any negative polls are fake news." And many of his supporters have picked up and run with his new definition. The ability to reshape language - even a little - is an awesome power to have. According to language experts on both sides of the aisle, the rebranding of fake news could be a genuine threat to democracy.