Researching Visual Social Media Platforms

Dhiraj Murthy is an Associate Professor of Journalism and Sociology at the University of Texas at Austin. He founded and directs the Computational Media Lab there. Murthy’s research explores social media, computational social science, race/ethnicity, qualitative/mixed methods, and disasters. Dr. Murthy has edited 3 journal special issues and authored over 60 articles, book chapters, and papers. Murthy wrote the first scholarly book about Twitter (second edition published by Polity Press, 2018). He is currently funded by the National Science Foundation’s Civil, Mechanical and Manufacturing Innovation (CMMI) Division for pioneering work on using the social media networks of journalists for damage reconnaissance during Hurricane Florence. Dr. Murthy’s work also uniquely explores the potential role of social technologies in diversity and community inclusion.With the meteoric rise of Instagram, Snapchat and YouTube, it is clear that image- and video- based platforms have become tremendously important to our social, political, and economic lives. However, there are unique challenges associated with data collection and analysis on visual social media platforms. This workshop explores the following questions in detail: How do we integrate and weigh Big Data questions with more in-depth contextualized analysis of social media content? How do we categorize textual and visual content, addressing issues of ontology? How can we scale small data to big data in visual spaces? Ultimately, it is argued that image/video data produced and consumed on social media has real value in helping us understand the social experience of everyday and profound events, but studying these types of data often requires innovations in theory and methods. Hands-on methods work will involve participants collecting data from YouTube and understanding structured metadata and unstructured data involving visual content

Big data analytics:Computational intelligence techniques and application areas

Big Data has significant impact in developing functional smart cities and supporting modern societies. In this paper, we investigate the importance of Big Data in modern life and economy, and discuss challenges arising from Big Data utilization. Different computational intelligence techniques have been considered as tools for Big Data analytics. We also explore the powerful combination of Big Data and Computational Intelligence (CI) and identify a number of areas, where novel applications in real world smart city problems can be developed by utilizing these powerful tools and techniques. We present a case study for intelligent transportation in the context of a smart city, and a novel data modelling methodology based on a biologically inspired universal generative modelling approach called Hierarchical Spatial-Temporal State Machine (HSTSM). We further discuss various implications of policy, protection, valuation and commercialization related to Big Data, its applications and deployment

The Profiling Potential of Computer Vision and the Challenge of Computational Empiricism

Computer vision and other biometrics data science applications have commenced a new project of profiling people. Rather than using 'transaction generated information', these systems measure the 'real world' and produce an assessment of the 'world state' - in this case an assessment of some individual trait. Instead of using proxies or scores to evaluate people, they increasingly deploy a logic of revealing the truth about reality and the people within it. While these profiling knowledge claims are sometimes tentative, they increasingly suggest that only through computation can these excesses of reality be captured and understood. This article explores the bases of those claims in the systems of measurement, representation, and classification deployed in computer vision. It asks if there is something new in this type of knowledge claim, sketches an account of a new form of computational empiricism being operationalised, and questions what kind of human subject is being constructed by these technological systems and practices. Finally, the article explores legal mechanisms for contesting the emergence of computational empiricism as the dominant knowledge platform for understanding the world and the people within it

Towards Learning ‘Self’ and Emotional Knowledge in Social and Cultural Human-Agent Interactions

Original article can be found at: http://www.igi-global.com/articles/details.asp?ID=35052 Copyright IGI. Posted by permission of the publisher.This article presents research towards the development of a virtual learning environment (VLE) inhabited by intelligent virtual agents (IVAs) and modeling a scenario of inter-cultural interactions. The ultimate aim of this VLE is to allow users to reflect upon and learn about intercultural communication and collaboration. Rather than predefining the interactions among the virtual agents and scripting the possible interactions afforded by this environment, we pursue a bottomup approach whereby inter-cultural communication emerges from interactions with and among autonomous agents and the user(s). The intelligent virtual agents that are inhabiting this environment are expected to be able to broaden their knowledge about the world and other agents, which may be of different cultural backgrounds, through interactions. This work is part of a collaborative effort within a European research project called eCIRCUS. Specifically, this article focuses on our continuing research concerned with emotional knowledge learning in autobiographic social agents.Peer reviewe

The Web Science Observatory

To understand and enable the evolution of the Web and to help address grand societal challenges, the Web must be observable at scale across space and time. That requires a globally distributed and collaborative Web Observatory