Can electoral popularity be predicted using socially generated big data?

Today, our more-than-ever digital lives leave significant footprints in cyberspace. Large scale collections of these socially generated footprints, often known as big data, could help us to re-investigate different aspects of our social collective behaviour in a quantitative framework. In this contribution we discuss one such possibility: the monitoring and predicting of popularity dynamics of candidates and parties through the analysis of socially generated data on the web during electoral campaigns. Such data offer considerable possibility for improving our awareness of popularity dynamics. However they also suffer from significant drawbacks in terms of representativeness and generalisability. In this paper we discuss potential ways around such problems, suggesting the nature of different political systems and contexts might lend differing levels of predictive power to certain types of data source. We offer an initial exploratory test of these ideas, focussing on two data streams, Wikipedia page views and Google search queries. On the basis of this data, we present popularity dynamics from real case examples of recent elections in three different countries.Comment: To appear in Information Technolog

Exploring Russian Cyberspace: Digitally-Mediated Collective Action and the Networked Public Sphere

This paper summarizes the major findings of a three-year research project to investigate the Internet's impact on Russian politics, media and society. We employed multiple methods to study online activity: the mapping and study of the structure, communities and content of the blogosphere; an analogous mapping and study of Twitter; content analysis of different media sources using automated and human-based evaluation approaches; and a survey of bloggers; augmented by infrastructure mapping, interviews and background research. We find the emergence of a vibrant and diverse networked public sphere that constitutes an independent alternative to the more tightly controlled offline media and political space, as well as the growing use of digital platforms in social mobilization and civic action. Despite various indirect efforts to shape cyberspace into an environment that is friendlier towards the government, we find that the Russian Internet remains generally open and free, although the current degree of Internet freedom is in no way a prediction of the future of this contested space

How Polarized Have We Become? A Multimodal Classification of Trump Followers and Clinton Followers

Polarization in American politics has been extensively documented and analyzed for decades, and the phenomenon became all the more apparent during the 2016 presidential election, where Trump and Clinton depicted two radically different pictures of America. Inspired by this gaping polarization and the extensive utilization of Twitter during the 2016 presidential campaign, in this paper we take the first step in measuring polarization in social media and we attempt to predict individuals' Twitter following behavior through analyzing ones' everyday tweets, profile images and posted pictures. As such, we treat polarization as a classification problem and study to what extent Trump followers and Clinton followers on Twitter can be distinguished, which in turn serves as a metric of polarization in general. We apply LSTM to processing tweet features and we extract visual features using the VGG neural network. Integrating these two sets of features boosts the overall performance. We are able to achieve an accuracy of 69%, suggesting that the high degree of polarization recorded in the literature has started to manifest itself in social media as well.Comment: 16 pages, SocInfo 2017, 9th International Conference on Social Informatic

Oblique strategies for ambient journalism

Alfred Hermida recently posited ‘ambient journalism’ as a new framework for para- and professional journalists, who use social networks like Twitter for story sources, and as a news delivery platform. Beginning with this framework, this article explores the following questions: How does Hermida define ‘ambient journalism’ and what is its significance? Are there alternative definitions? What lessons do current platforms provide for the design of future, real-time platforms that ‘ambient journalists’ might use? What lessons does the work of Brian Eno provide–the musician and producer who coined the term ‘ambient music’ over three decades ago? My aim here is to formulate an alternative definition of ambient journalism that emphasises craft, skills acquisition, and the mental models of professional journalists, which are the foundations more generally for journalism practices. Rather than Hermida’s participatory media context I emphasise ‘institutional adaptiveness’: how journalists and newsrooms in media institutions rely on craft and skills, and how emerging platforms can augment these foundations, rather than replace them

Crisis Communication Patterns in Social Media during Hurricane Sandy

Hurricane Sandy was one of the deadliest and costliest of hurricanes over the past few decades. Many states experienced significant power outage, however many people used social media to communicate while having limited or no access to traditional information sources. In this study, we explored the evolution of various communication patterns using machine learning techniques and determined user concerns that emerged over the course of Hurricane Sandy. The original data included ~52M tweets coming from ~13M users between October 14, 2012 and November 12, 2012. We run topic model on ~763K tweets from top 4,029 most frequent users who tweeted about Sandy at least 100 times. We identified 250 well-defined communication patterns based on perplexity. Conversations of most frequent and relevant users indicate the evolution of numerous storm-phase (warning, response, and recovery) specific topics. People were also concerned about storm location and time, media coverage, and activities of political leaders and celebrities. We also present each relevant keyword that contributed to one particular pattern of user concerns. Such keywords would be particularly meaningful in targeted information spreading and effective crisis communication in similar major disasters. Each of these words can also be helpful for efficient hash-tagging to reach target audience as needed via social media. The pattern recognition approach of this study can be used in identifying real time user needs in future crises