We study the emergence of support for Donald Trump in Reddit's political discussion. With almost 800k subscribers, "r/The_Donald" is one of the largest communities on Reddit, and one of the main hubs for Trump supporters. It was created in 2015, shortly after Donald Trump began his presidential campaign. By using only data from 2012, we predict the likelihood of being a supporter of Donald Trump in 2016, the year of the last US presidential elections. To characterize the behavior of Trump supporters, we draw from three different sociological hypotheses: homophily, social influence, and social feedback. We operationalize each hypothesis as a set of features for each user, and train classifiers to predict their participation in r/The_Donald.
We find that homophily-based and social feedback-based features are the most predictive signals. Conversely, we do not observe a strong impact of social influence mechanisms. We also perform an introspection of the best-performing model to build a "persona" of the typical supporter of Donald Trump on Reddit. We find evidence that the most prominent traits include a predominance of masculine interests, a conservative and libertarian political leaning, and links with politically incorrect and conspiratorial content.
In this article we present our exploratory research into the occurrence of ageist attitudes within the discussion related to the US 2016 presidential election. We use natural processing techniques to analyze the content tweets related to Hillary Clinton and Donald Trump. Content analysis shows that although ageist attitudes are scarce in the discussion, they are mostly focused on Hillary Clinton rather than Donald Trump. Also, ageist arguments against Donald Trump appear mostly as a reply to controversies connected with the health of Hillary Clinton.
In this paper, we propose a data-driven method to measure the impact of the 'woman card' exchange between Hillary Clinton and Donald Trump. Building from a unique dataset of the two candidates' Twitter followers, we first examine the transition dynamics of the two candidates' Twitter followers one week before the exchange and one week after. Then we train a convolutional neural network to classify the gender of the followers and unfollowers, and study how women in particular are reacting to the 'woman card' exchange. Our study suggests that the 'woman card' comment has made women more likely t…
From a crowded field with 17 candidates, Hillary Clinton and Donald Trump have emerged as the two front-runners in the 2016 U.S. presidential campaign. The two candidates each boast more than 5 million followers on Twitter, and at the same time both have witnessed hundreds of thousands of people leave their camps. In this paper we attempt to characterize individuals who have left Hillary Clinton and Donald Trump between September 2015 and March 2016. Our study focuses on three dimensions of social demographics: social capital, gender, and age. Within each camp, we compare the characteristics o…
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.
Measuring and forecasting opinion trends from real-time social media is a long-standing goal of big-data analytics. Despite its importance, there has been no conclusive scientific evidence so far that social media activity can capture the opinion of the general population. Here we develop a method to infer the opinion of Twitter users regarding the candidates of the 2016 US Presidential Election by using a combination of statistical physics of complex networks and machine learning based on hashtags co-occurrence to develop an in-domain training set approaching 1 million tweets. We investigate …
Political trolls initiate online discord not only for the lulz (laughs) but also for ideological reasons, such as promoting their desired political candidates. Political troll groups recently gained spotlight because they were considered central in helping Donald Trump win the 2016 US presidential election, which involved difficult mass mobilizations. Political trolls face unique challenges as they must build their own communities while simultaneously disrupting others. However, little is known about how political trolls mobilize sufficient participation to suddenly become problems for others.…
In this paper, we study the likelihood of Bernie Sanders supporters voting for Donald Trump instead of Hillary Clinton. Building from a unique time-series dataset of the three candidates' Twitter followers, which we make public here, we first study the proportion of Sanders followers who simultaneously follow Trump (but not Clinton) and how this evolves over time. Then we train a convolutional neural network to classify the gender of Sanders followers, and study whether men are more likely to jump ship for Trump than women. Our study shows that between March and May an increasing proportion of…
This paper presents the first data-driven analysis of Gettr, a new social network platform launched by former US President Donald Trump's team. Among other things, we find that users on the platform heavily discuss politics, with a focus on the Trump campaign in the US and Bolsonaro's in Brazil. Activity on the platform has steadily been decreasing since its launch, although a core of verified users and early adopters kept posting and become central to it. Finally, although toxicity has been increasing over time, the average level of toxicity is still lower than the one recently observed on ot…
Putnam Science Academy（普特南科学院）；Princeton High School（普林斯顿高中）；Zhengzhou University（郑州大学）；Huazhong University of Science and Technology（华中科技大学）
This study verified the effectiveness of Donald Trump's Twitter campaign in guiding agen-da-setting and deflecting political risk and examined Trump's Twitter communication strategy and explores the communication effects of his tweet content during Covid-19 pandemic. We collected all tweets posted by Trump on the Twitter platform from January 1, 2020 to December 31, 2020.We used Ordinary Least Squares (OLS) regression analysis with a fixed effects model to analyze the existence of the Twitter strategy. The correlation between the number of con-firmed daily Covid-19 diagnoses and the number of …
Evaluating the effects of moderation interventions is a task of paramount importance, as it allows assessing the success of content moderation processes. So far, intervention effects have been almost solely evaluated at the aggregated platform or community levels. Here, we carry out a multidimensional evaluation of the user-level effects of the sequence of moderation interventions that targeted r/The_Donald: a community of Donald Trump adherents on Reddit. We demonstrate that the interventions: 1) strongly reduced user activity; 2) slightly increased the diversity of the subreddits in which us…
Echo chambers in online social networks, whereby users' beliefs are reinforced by interactions with like-minded peers and insulation from others' points of view, have been decried as a cause of political polarization. Here, we investigate their role in the debate around the 2016 US elections on Reddit, a fundamental platform for the success of Donald Trump. We identify Trump vs Clinton supporters and reconstruct their political interaction network. We observe a preference for cross-cutting political interactions between the two communities rather than within-group interactions, thus contradict…
Quantifying the Uniqueness and Divisiveness of Presidential Discourse
Department of Computer Science, The University of Chicago（芝加哥大学计算机科学系）；Department of Political Science, The University of Chicago（芝加哥大学政治学系）；Harris School of Public Policy, The University of Chicago（芝加哥大学哈里斯公共政策学院）；Harvard University（哈佛大学）
The QAnon conspiracy posits that Satan-worshiping Democrats operate a covert child sex-trafficking operation, which Donald Trump is destined to expose and annihilate. Emblematic of the ease with which political misconceptions can spread through social media, QAnon originated in late 2017 and rapidly grew to shape the political beliefs of millions. To illuminate the process by which a conspiracy theory spreads, we report two computational studies examining the social network structure and semantic content of tweets produced by users central to the early QAnon network on Twitter. Using data mine…
Institute of Informatics and Telematics, National Research Council (IIT-CNR)（国家研究委员会信息学与远程信息处理研究所）
On the morning of November 9th 2016, the world woke up to the shocking outcome of the US Presidential elections: Donald Trump was the 45th President of the United States of America. An unexpected event that still has tremendous consequences all over the world. Today, we know that a minority of social bots, automated social media accounts mimicking humans, played a central role in spreading divisive messages and disinformation, possibly contributing to Trump's victory. In the aftermath of the 2016 US elections, the world started to realize the gravity of widespread deception in social media. Fo…
Gender is playing an important role in the 2016 U.S. presidential election, especially with Hillary Clinton becoming the first female presidential nominee and Donald Trump being frequently accused of sexism. In this paper, we introduce computer vision to the study of gender politics and present an image-driven method that can measure the effects of gender in an accurate and timely manner. We first collect all the profile images of the candidates' Twitter followers. Then we train a convolutional neural network using images that contain gender labels. Lastly, we classify all the follower and unf…
When building a global brand of any kind -- a political actor, clothing style, or belief system -- developing widespread awareness is a primary goal. Short of knowing any of the stories or products of a brand, being talked about in whatever fashion -- raw fame -- is, as Oscar Wilde would have it, better than not being talked about at all. Here, we measure, examine, and contrast the day-to-day raw fame dynamics on Twitter for US Presidents and major US Presidential candidates from 2008 to 2020: Barack Obama, John McCain, Mitt Romney, Hillary Clinton, Donald Trump, and Joe Biden. We assign ``lex…
We propose a framework to measure, evaluate, and rank campaign effectiveness in the ongoing 2016 U.S. presidential election. Using Twitter data collected from Sept. 2015 to Jan. 2016, we first uncover the tweeting tactics of the candidates and second, using negative binomial regression and exploiting the variations in 'likes,' we evaluate the effectiveness of these tactics. Thirdly, we rank the candidates' campaign tactics by calculating the conditional expectation of their generated 'likes.' We show that while Ted Cruz and Marco Rubio put much weight on President Obama, this tactic is not bei…
