Predictive Analysis for Social Processes II: Predictability and Warning Analysis

This two-part paper presents a new approach to predictive analysis for social processes. Part I identifies a class of social processes, called positive externality processes, which are both important and difficult to predict, and introduces a multi-scale, stochastic hybrid system modeling framework for these systems. In Part II of the paper we develop a systems theory-based, computationally tractable approach to predictive analysis for these systems. Among other capabilities, this analytic methodology enables assessment of process predictability, identification of measurables which have predictive power, discovery of reliable early indicators for events of interest, and robust, scalable prediction. The potential of the proposed approach is illustrated through case studies involving online markets, social movements, and protest behavior

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

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

On the influence of social bots in online protests. Preliminary findings of a Mexican case study

Social bots can affect online communication among humans. We study this phenomenon by focusing on #YaMeCanse, the most active protest hashtag in the history of Twitter in Mexico. Accounts using the hashtag are classified using the BotOrNot bot detection tool. Our preliminary analysis suggests that bots played a critical role in disrupting online communication about the protest movement.Comment: 10 page

Taboo, the Game: Patent Office Edition—The New Preissuance Submissions Under the America Invents Act

Thorough patent examination ensures that issued patents confer constitutionally granted incentives to innovate but do not create inappropriately broad monopolies. Examiners at the United States Patent and Trademark Office are alone tasked with striking this proper balance, in part by searching the universe of existing published knowledge to determine the originality of the applied-for invention. In 2011, Congress enacted the Leahy-Smith America Invents Act, which included a provision allowing the public to present examiners with relevant publications that the examiners’ own searches might not otherwise uncover. However, this “preissuance submissions” provision and its related administrative rule are tempered by 35 U.S.C. § 122(c) (2006), which prohibits any third-party, pre-grant “protest or other form of [preissuance] opposition” to an application. Thus, although a party may describe to an examiner how its submission is relevant to an application, that party is prohibited from arguing how the submission renders that application unpatentable. This Note argues that Congress should amend § 122(c) to permit preissuance third-party argumentation for two reasons. First, the current scheme arguably violates that law already. Second, a rule allowing submitter argumentation would better incentivize participation by competitive parties who fear that examiners might not recognize their submitted publications\u27 full invalidating potential

Success of Digital Activism: Roles of Structures and Media Strategies

This research explored how the structures of digital activist movements (movement causes, target audience, and duration) and the strategic use of media applications affected their final outcomes. Survey data from the 2013 Global Digital Activism Data Set (Digital Activism Research Project) were supplemented with insights from four professional interviewees who had experience and knowledge about activism in both offline and digital fora as well as several case studies of successful and unsuccessful digital movements. The mixed methods analyses offered three insights. Digital activism about human right and political issues was less likely to succeed than ones about civic development concerns. Activism that targeted governments was also less likely to succeed than if the targets were informal groups/individuals or institutions/organizations. These findings were supported by the structural inequality axiom. In addition, as predicted by the value-added proposition in social movement theory, the strategic use of media applications (using public media applications for collaboration purposes) as well as multiple fora (combining online and offline) increased the possibility of activism’s success. Sample case studies were used to illustrate the broad contours of the survey findings