In this paper, we propose a framework to infer the topic preferences of Donald Trump's followers on Twitter. We first use latent Dirichlet allocation (LDA) to derive the weighted mixture of topics for each Trump tweet. Then we use negative binomial regression to model the "likes," with the weights of each topic serving as explanatory variables. Our study shows that attacking Democrats such as President Obama and former Secretary of State Hillary Clinton earns Trump the most "likes." Our framework of inference is generalizable to the study of other politicians.
There are also quite a few studies modeling individual behaviors in social media. @cite_3 models the decision to retweet, using Twitter user features such as agreeableness, number of tweets posted, and daily tweeting patterns. @cite_2 models individuals' waiting time before replying to a tweet based on their previous replying patterns. Our study models the number of likes'' that a Trump's tweet receives. Our innovation is to use tweet-specific features instead of individual-specific features, as done in the above-cited literature.
@cite_3 @cite_2
2025310159 2223407974
There has been much effort on studying how social media sites, such as Twitter, help propagate information in different situations, including spreading alerts and SOS messages in an emergency. However, existing work has not addressed how to actively identify and engage the right strangers at the right time on social media to help effectively propagate intended information within a desired time frame. To address this problem, we have developed three models: (1) a feature-based model that leverages people's exhibited social behavior, including the content of their tweets and social interactions, to characterize their willingness and readiness to propagate information on Twitter via the act of retweeting; (2) a wait-time model based on a user's previous retweeting wait times to predict his or her next retweeting time when asked; and (3) a subset selection model that automatically selects a subset of people from a set of available people using probabilities predicted by the feature-based model and maximizes retweeting rate. Based on these three models, we build a recommender system that predicts the likelihood of a stranger to retweet information when asked, within a specific time window, and recommends the top-N qualified strangers to engage with. Our experiments, including live studies in the real world, demonstrate the effectiveness of our work. We present a study analyzing the response times of users to questions on Twitter. We investigate estimating these response times using an exponential distribution-based wait time model learned from users’ previous responses. Our analysis considers several different model building approaches, including personalized models for each user, general models built for all users, and time-sensitive models specific to a day of the week or hour of the day. Our evaluation using a real world question-answer dataset shows the effectiveness of our approach.