What kind of celebrities do they obtain tweets from?
Amitabh Bachchan, Ariana Grande, Barack Obama, Bill Gates, Donald Trump,
Ellen DeGeneres, J K Rowling, Jimmy Fallon, Justin Bieber, Kevin Durant, Kim Kardashian, Lady Gaga, LeBron James,Narendra Modi, Oprah Winfrey
The sequence of documents produced by any given author varies in style and content, but some documents are more typical or representative of the source than others. We quantify the extent to which a given short text is characteristic of a specific person, using a dataset of tweets from fifteen celebrities. Such analysis is useful for generating excerpts of high-volume Twitter profiles, and understanding how representativeness relates to tweet popularity. We first consider the related task of binary author detection (is x the author of text T?), and report a test accuracy of 90.37% for the best of five approaches to this problem. We then use these models to compute characterization scores among all of an author's texts. A user study shows human evaluators agree with our characterization model for all 15 celebrities in our dataset, each with p-value < 0.05. We use these classifiers to show surprisingly strong correlations between characterization scores and the popularity of the associated texts. Indeed, we demonstrate a statistically significant correlation between this score and tweet popularity (likes/replies/retweets) for 13 of the 15 celebrities in our study.
Social media platforms, particularly microblogging services such as Twitter, have become increasingly popular BIBREF0 as a means to express thoughts and opinions. Twitter users emit tweets about a wide variety of topics, which vary in the extent to which they reflect a user's personality, brand and interests. This observation motivates the question we consider here, of how to quantify the degree to which tweets are characteristic of their author? People who are familiar with a given author appear to be able to make such judgments confidently. For example, consider the following pair of tweets written by US President Donald Trump, at the extreme sides of our characterization scores (0.9996 vs. 0.0013) for him:  Tweet 1: Thank you for joining us at the Lincoln Memorial tonight- a very special evening! Together, we are going to MAKE AMERICA GREAT AGAIN! Tweet 2: “The bend in the road is not the end of the road unless you refuse to take the turn." - Anonymous Although both these tweets are from the same account, we assert that Tweet 1 sounds more characteristic of Donald Trump than Tweet 2. We might also guess that the first is more popular than second. Indeed, Tweet 1 received 155,000 likes as opposed to only 234 for Tweet 2. Such an author characterization score has many possible applications. With the ability to identify the most/least characteristic tweets from a person, we can generate reduced excerpts for high-volume Twitter profiles. Similarly, identifying the least characteristic tweets can highlight unusual content or suspicious activity. A run of sufficiently unrepresentative tweets might be indicative that a hacker has taken control of a user's account. But more fundamentally, our work provides the necessary tool to study the question of how “characteristic-ness" or novelty are related to tweet popularity. Do tweets that are more characteristic of the user get more likes, replies and retweets? Is such a relationship universal, or does it depend upon the personality or domain of the author? Twitter users with a large follower base can employ our methods to understand how characteristic a new potential tweet sounds, and obtain an estimate of how popular it is likely to become. To answer these questions, we formally define the problem of author representativeness testing, and model the task as a binary classification problem. Our primary contributions in this paper include: Five approaches to authorship verification: As a proxy for the question of representativeness testing (which has no convincing source of ground truth without extensive human annotation), we consider the task of distinguishing tweets written by a given author from others they did not write. We compare five distinct computational approaches to such binary tweet classification (user vs. non-user). Our best model achieves a test accuracy of 90.37% over a dataset of 15 Twitter celebrities. We use the best performing model to compute a score (the probability of authorship), which quantifies how characteristic of the user a given tweet is. Human evaluation study: To verify that our results are in agreement with human judgment of how `characteristic' a tweet is, we ask human evaluators which of a pair of tweets sounds more characteristic of the given celebrity. The human evaluators are in agreement with our model 70.40% of the time, significant above the $0.05$ level for each of our 15 celebrities. Correlation analysis for popularity: Our characterization score exhibits strikingly high absolute correlation with popularity (likes, replies and retweets), despite the fact that tweet text is the only feature used to train the classifier which yields these scores. For 13 of the 15 celebrities in our dataset, we observe a statistically significant correlation between characterization score and popularity. Figure FIGREF4 shows the relation between tweet score and tweet popularity for Donald Trump and Justin Bieber respectively. The figure shows that the sign of this association differs for various celebrities, reflecting whether their audience seeks novelty or reinforcement. Iterative sampling for class imbalance: Our task requires distinguishing a user's tweets (perhaps 1,000 positive training examples) from the sea of all other user's tweets (implying billions of possible negative training examples). We present an iterative sampling technique to exploit this class imbalance, which improves the test accuracy for negative examples by 2.62%.