Validation of Twitter opinion trends with national polling aggregates: Hillary Clinton vs Donald Trump

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 the social networks formed by the interactions among millions of Twitter users and infer the support of each user to the presidential candidates. The resulting Twitter trends follow the New York Times National Polling Average, which represents an aggregate of hundreds of independent traditional polls, with remarkable accuracy. Moreover, the Twitter opinion trend precedes the aggregated NYT polls by 10 days, showing that Twitter can be an early signal of global opinion trends. Our analytics unleash the power of Twitter to uncover social trends from elections, brands to political movements, and at a fraction of the cost of national polls

From digital positivism and administrative big data analytics towards critical digital and social media research!

This essay argues for a paradigm shift in the study of the Internet and digital/social media. Big data analytics is the dominant paradigm. It receives large amounts of funding, is administrative and a form of digital positivism. Critical social media research is an alternative approach that combines critical social media theory, critical digital methods and critical-realist social media research ethics. Strengthening the second approach is a material question of power in academia

From Causality to Emergence: re-evaluating social media’s role in the 2011 English riots

This paper is an attempt to re-evaluate the role of social media on the riots. It draws upon interviews and qualitative analysis of tweets posted during the riots to examine how digital modalities reconfigure power relations between vulnerable and invulnerable populations as collectives seek to enact social change. The importance of social media for understanding collective action, I argue, lies in its relevance for conveying what one could call the performativity of public space. My thesis emerges in response to the rise of big data analytics as a means to predict and respond to political unrest, exploring the limits of predictive analyses with regard to issues of trust, power, memory and emotions. My claim is that understanding the power of the digital requires a more sophisticated understanding of emotions. To this end, I emphasize the need to employ multi-method approaches to study new forms of “mediated crowd” membership that combine digital methods with more traditional approaches to emotions research

Social media usage in B2B firms

Digital marketing is defined as a mix of web and social media interactions between different stakeholders. Most of the empirical studies have looked at business-to-customers and customers-to-customers while few have considered its strategic importance in business-to-business and/or organizations-to-organizations. This study aims to fill a gap in the literature by exploring the current use of the Social Media platform, such as Facebook, by Business-to-Business (B2B) organizations in promoting their products and/or services as well as in engaging with key players. Key digital metrics, including SEO rankings and keywords, are looked at through the use of various marketing analytics tools (e.g. SEMrush, Ahrefs). A combination of archival data and netnography have been used to analyse market gaps with regards to social media interactions in B2B contexts. Results of this study indicate that Facebook is an important marketing platform under-utilized by marketing specialists to gain potential customer groups (individuals and organizations). Social media design and its integration using marketing analytics softwares is necessary to explore new market opportunities in B2B contexts. By proper utilization of Facebook as a part of the marketing mix, B2B firms can harness the power of organic advertising to increase customer knowledge and facilitate buyer's information search. However, it is suggested to do so respecting fundamentals of ethics and corporate responsibilities towards societal goods

Digital Analytics tools and their predictive power on performance: an analysis of the brazilian auto market

The objective of this paper is to understand how these companies are being mapped and analyzed in relation to sales, with the support of Digital Analytics tools. It was selected the top 10 automotive companies present in Brazil with the highest sales and data were collected on their pages through Digital Analytics tools during two months. It was chosen the multivariate technique of multiple linear regressions by analyzing the relation between the independent variables (collected attributes) with the dependent variable (sales). As a result, it was found that some tools have a better set of parameters that explains the sales of automakers. From the seven Digital Analytics software's observed, six-showed significance in explanatory power. This research was purely quantitative, in which the independent variables that were significant for this study could be grouped into: "Social Media" and "Not Social Media", overcoming a greater concentration of variables "Not Social Media".The objective of this paper is to understand how these companies are being mapped and analyzed in relation to sales, with the support of Digital Analytics tools. It was selected the top 10 automotive companies present in Brazil with the highest sales and data were collected on their pages through Digital Analytics tools during two months. It was chosen the multivariate technique of multiple linear regressions by analyzing the relation between the independent variables (collected attributes) with the dependent variable (sales). As a result, it was found that some tools have a better set of parameters that explains the sales of automakers. From the seven Digital Analytics software's observed, six-showed significance in explanatory power. This research was purely quantitative, in which the independent variables that were significant for this study could be grouped into: "Social Media" and "Not Social Media", overcoming a greater concentration of variables "Not Social Media".Universidade Federal de São Paulo – Unifesp, BrasilUniversidade Federal de São Paulo – Unifesp, Brasi

Early Warning Analysis for Social Diffusion Events

There is considerable interest in developing predictive capabilities for social diffusion processes, for instance to permit early identification of emerging contentious situations, rapid detection of disease outbreaks, or accurate forecasting of the ultimate reach of potentially viral ideas or behaviors. This paper proposes a new approach to this predictive analytics problem, in which analysis of meso-scale network dynamics is leveraged to generate useful predictions for complex social phenomena. We begin by deriving a stochastic hybrid dynamical systems (S-HDS) model for diffusion processes taking place over social networks with realistic topologies; this modeling approach is inspired by recent work in biology demonstrating that S-HDS offer a useful mathematical formalism with which to represent complex, multi-scale biological network dynamics. We then perform formal stochastic reachability analysis with this S-HDS model and conclude that the outcomes of social diffusion processes may depend crucially upon the way the early dynamics of the process interacts with the underlying network's community structure and core-periphery structure. This theoretical finding provides the foundations for developing a machine learning algorithm that enables accurate early warning analysis for social diffusion events. The utility of the warning algorithm, and the power of network-based predictive metrics, are demonstrated through an empirical investigation of the propagation of political memes over social media networks. Additionally, we illustrate the potential of the approach for security informatics applications through case studies involving early warning analysis of large-scale protests events and politically-motivated cyber attacks

Time well spent”: the ideology of temporal disconnection as a means for digital wellbeing

After facing an intense negative reaction to their accumulation of social, political, and economic power and influence, several tech and social media companies rolled out “digital wellbeing” tools during the second half of 2018. This article examines the technological and discursive construction of “digital wellbeing” as enacted through operating system-based tools (Screen Time and Do Not Disturb— iOS, Digital Wellbeing—Android, My Analytics—Microsoft), and social media platforms application functions (Your Time—Facebook, Time Watched—YouTube, Your Activity—Instagram). While the companies’ discourse deploys an imaginary centered around ethics and a normative experience accentuating the willfulness and empowerment of the user, the socio-material analysis of the interfaces and features shows that they envisage simple, familiar, and limited possibilities of disconnecting. Therefore, agency is limited, and the wellbeing outcomes are indeterminate, restricted to quantifying time or controlling the intentionality of connectivity

Counting the Population in Need of International Protection Globally

Statistical data and evidence-based claims are increasingly central to our everyday lives. Critically examining ‘Big Data’, this book charts the recent explosion in sources of data, including those precipitated by global developments and technological change. It sets out changes and controversies related to data harvesting and construction, dissemination and data analytics by a range of private, governmental and social organisations in multiple settings. Analysing the power of data to shape political debate, the presentation of ideas to us by the media, and issues surrounding data ownership and access, the authors suggest how data can be used to uncover injustices and to advance social progress