Assessing candidate preference through web browsing history

Predicting election outcomes is of considerable interest to candidates, political scientists, and the public at large. We propose the use of Web browsing history as a new indicator of candidate preference among the electorate, one that has potential to overcome a number of the drawbacks of election polls. However, there are a number of challenges that must be overcome to effectively use Web browsing for assessing candidate preference—including the lack of suitable ground truth data and the heterogeneity of user populations in time and space. We address these challenges, and show that the resulting methods can shed considerable light on the dynamics of voters’ candidate preferences in ways that are difficult to achieve using polls.Accepted manuscrip

Modeling the formation of attentive publics in social media: the case of Donald Trump

Previous research has shown the importance of Donald Trump’s Twitter activity, and that of his Twitter following, in spreading his message during the primary and general election campaigns of 2015–2016. However, we know little about how the publics who followed Trump and amplified his messages took shape. We take this case as an opportunity to theorize and test questions about the assembly of what we call “attentive publics” in social media. We situate our study in the context of current discussions of audience formation, attention flow, and hybridity in the United States’ political media system. From this we derive propositions concerning how attentive publics aggregate around a particular object, in this case Trump himself, which we test using time series modeling. We also present an exploration of the possible role of automated accounts in these processes. Our results reiterate the media hybridity described by others, while emphasizing the importance of news media coverage in building social media attentive publics.Accepted manuscrip

Co-Following on Twitter

We present an in-depth study of co-following on Twitter based on the observation that two Twitter users whose followers have similar friends are also similar, even though they might not share any direct links or a single mutual follower. We show how this observation contributes to (i) a better understanding of language-agnostic user classification on Twitter, (ii) eliciting opportunities for Computational Social Science, and (iii) improving online marketing by identifying cross-selling opportunities. We start with a machine learning problem of predicting a user's preference among two alternative choices of Twitter friends. We show that co-following information provides strong signals for diverse classification tasks and that these signals persist even when (i) the most discriminative features are removed and (ii) only relatively "sparse" users with fewer than 152 but more than 43 Twitter friends are considered. Going beyond mere classification performance optimization, we present applications of our methodology to Computational Social Science. Here we confirm stereotypes such as that the country singer Kenny Chesney (@kennychesney) is more popular among @GOP followers, whereas Lady Gaga (@ladygaga) enjoys more support from @TheDemocrats followers. In the domain of marketing we give evidence that celebrity endorsement is reflected in co-following and we demonstrate how our methodology can be used to reveal the audience similarities between Apple and Puma and, less obviously, between Nike and Coca-Cola. Concerning a user's popularity we find a statistically significant connection between having a more "average" followership and having more followers than direct rivals. Interestingly, a \emph{larger} audience also seems to be linked to a \emph{less diverse} audience in terms of their co-following.Comment: full version of a short paper at Hypertext 201

Measuring relative opinion from location-based social media: A case study of the 2016 U.S. presidential election

Social media has become an emerging alternative to opinion polls for public opinion collection, while it is still posing many challenges as a passive data source, such as structurelessness, quantifiability, and representativeness. Social media data with geotags provide new opportunities to unveil the geographic locations of users expressing their opinions. This paper aims to answer two questions: 1) whether quantifiable measurement of public opinion can be obtained from social media and 2) whether it can produce better or complementary measures compared to opinion polls. This research proposes a novel approach to measure the relative opinion of Twitter users towards public issues in order to accommodate more complex opinion structures and take advantage of the geography pertaining to the public issues. To ensure that this new measure is technically feasible, a modeling framework is developed including building a training dataset by adopting a state-of-the-art approach and devising a new deep learning method called Opinion-Oriented Word Embedding. With a case study of the tweets selected for the 2016 U.S. presidential election, we demonstrate the predictive superiority of our relative opinion approach and we show how it can aid visual analytics and support opinion predictions. Although the relative opinion measure is proved to be more robust compared to polling, our study also suggests that the former can advantageously complement the later in opinion prediction

Political Homophily in Independence Movements: Analysing and Classifying Social Media Users by National Identity

Social media and data mining are increasingly being used to analyse political and societal issues. Here we undertake the classification of social media users as supporting or opposing ongoing independence movements in their territories. Independence movements occur in territories whose citizens have conflicting national identities; users with opposing national identities will then support or oppose the sense of being part of an independent nation that differs from the officially recognised country. We describe a methodology that relies on users' self-reported location to build large-scale datasets for three territories -- Catalonia, the Basque Country and Scotland. An analysis of these datasets shows that homophily plays an important role in determining who people connect with, as users predominantly choose to follow and interact with others from the same national identity. We show that a classifier relying on users' follow networks can achieve accurate, language-independent classification performances ranging from 85% to 97% for the three territories.Comment: Accepted for publication in IEEE Intelligent System