189,182 research outputs found
Integration of Exploration and Search: A Case Study of the M3 Model
International audienceEffective support for multimedia analytics applications requires exploration and search to be integrated seamlessly into a single interaction model. Media metadata can be seen as defining a multidimensional media space, casting multimedia analytics tasks as exploration, manipulation and augmentation of that space. We present an initial case study of integrating exploration and search within this multidimensional media space. We extend the M3 model, initially proposed as a pure exploration tool, and show that it can be elegantly extended to allow searching within an exploration context and exploring within a search context. We then evaluate the suitability of relational database management systems, as representatives of today’s data management technologies, for implementing the extended M3 model. Based on our results, we finally propose some research directions for scalability of multimedia analytics
Alexandria: Extensible Framework for Rapid Exploration of Social Media
The Alexandria system under development at IBM Research provides an
extensible framework and platform for supporting a variety of big-data
analytics and visualizations. The system is currently focused on enabling rapid
exploration of text-based social media data. The system provides tools to help
with constructing "domain models" (i.e., families of keywords and extractors to
enable focus on tweets and other social media documents relevant to a project),
to rapidly extract and segment the relevant social media and its authors, to
apply further analytics (such as finding trends and anomalous terms), and
visualizing the results. The system architecture is centered around a variety
of REST-based service APIs to enable flexible orchestration of the system
capabilities; these are especially useful to support knowledge-worker driven
iterative exploration of social phenomena. The architecture also enables rapid
integration of Alexandria capabilities with other social media analytics
system, as has been demonstrated through an integration with IBM Research's
SystemG. This paper describes a prototypical usage scenario for Alexandria,
along with the architecture and key underlying analytics.Comment: 8 page
What Ways Can We Use Big Data to Offer More Personalized and Tailored HR Services to our Employees?
Big data analytics—analytic techniques operating on big data—is continuing to disrupt the way decision-making is occurring. Instead of relying on intuition, decisions are made based on statistical analysis, emerging technologies and massive amounts of current and historical data. Predictive analytics, which will be featured in much of the research below, is a type of big data analytics that predicts an outcome by correlating the relationships of various factors. These predictions can be made utilizing a variety of organized structured data and disorganized unstructured data (i.e. social media posts, surveys, etc.
Social media analytics: a survey of techniques, tools and platforms
This paper is written for (social science) researchers seeking to analyze the wealth of social media now available. It presents a comprehensive review of software tools for social networking media, wikis, really simple syndication feeds, blogs, newsgroups, chat and news feeds. For completeness, it also includes introductions to social media scraping, storage, data cleaning and sentiment analysis. Although principally a review, the paper also provides a methodology and a critique of social media tools. Analyzing social media, in particular Twitter feeds for sentiment analysis, has become a major research and business activity due to the availability of web-based application programming interfaces (APIs) provided by Twitter, Facebook and News services. This has led to an ‘explosion’ of data services, software tools for scraping and analysis and social media analytics platforms. It is also a research area undergoing rapid change and evolution due to commercial pressures and the potential for using social media data for computational (social science) research. Using a simple taxonomy, this paper provides a review of leading software tools and how to use them to scrape, cleanse and analyze the spectrum of social media. In addition, it discussed the requirement of an experimental computational environment for social media research and presents as an illustration the system architecture of a social media (analytics) platform built by University College London. The principal contribution of this paper is to provide an overview (including code fragments) for scientists seeking to utilize social media scraping and analytics either in their research or business. The data retrieval techniques that are presented in this paper are valid at the time of writing this paper (June 2014), but they are subject to change since social media data scraping APIs are rapidly changing
Business Value of Big Data Analytics:A Systems-Theoretic Approach and Empirical Test
Although big data analytics have been widely considered a key driver of marketing and innovation processes, whether and how big data analytics create business value has not been fully understood and empirically validated at a large scale. Taking social media analytics as an example, this paper is among the first attempts to theoretically explain and empirically test the market performance impact of big data analytics. Drawing on the systems theory, we explain how and why social media analytics create super-additive value through the synergies in functional complementarity between social media diversity for gathering big data from diverse social media channels and big data analytics for analyzing the gathered big data. Furthermore, we deepen our theorizing by considering the difference between small and medium enterprises (SMEs) and large firms in the required integration effort that enables the synergies of social media diversity and big data analytics. In line with this theorizing, we empirically test the synergistic effect of social media diversity and big data analytics by using a recent large-scale survey data set from 18,816 firms in Italy. We find that social media diversity and big data analytics have a positive interaction effect on market performance, which is more salient for SMEs than for large firms
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Large-scale social-media analytics on stratosphere
The importance of social-media platforms and online communities - in business as well as public context - is more and more acknowledged and appreciated by industry and researchers alike. Consequently, a wide range of analytics has been proposed to understand, steer, and exploit the mechanics and laws driving their functionality and creating the resulting benefits. However, analysts usually face significant problems in scaling existing and novel approaches to match the data volume and size of modern online communities. In this work, we propose and demonstrate the usage of the massively parallel data processing system Stratosphere, based on second order functions as an extended notion of the MapReduce paradigm, to provide a new level of scalability to such social-media analytics. Based on the popular example of role analysis, we present and illustrate how this massively parallel approach can be leveraged to scale out complex data-mining tasks, while providing a programming approach that eases the formulation of complete analytical workflows
Effectiveness of Corporate Social Media Activities to Increase Relational Outcomes
This study applies social media analytics to investigate the impact of different corporate social media activities on user word of mouth and attitudinal loyalty. We conduct a multilevel analysis of approximately 5 million tweets regarding the main Twitter accounts of 28 large global companies. We empirically identify different social media activities in terms of social media management strategies (using social media management tools or the web-frontend client), account types (broadcasting or receiving information), and communicative approaches (conversational or disseminative). We find positive effects of social media management tools, broadcasting accounts, and conversational communication on public perception
Credibility by automation : Expectations of future knowledge production in social media analytics
Social media analytics is a burgeoning new field associated with high promises of societal relevance and business value but also methodological and practical problems. In this article, we build on the sociology of expectations literature and research on expertise in the interaction between humans and machines to examine how analysts and clients make their expectations about social media analytics credible in the face of recognized problems. To investigate how this happens in different contexts, we draw on thematic interviews with 10 social media analytics and client companies. In our material, social media analytics appears as a field facing both hopes and skepticism—toward data, analysis methods, or the users of analytics—from both the clients and analysts. In this setting, the idea of automated analysis through algorithmic methods emerges as a central notion that lends credibility to expectations about social media analytics. Automation is thought to, first, extend and make expert interpretation of messy social media data more rigorous; second, eliminate subjective judgments from measurement on social media; and third, allow for coordination of knowledge management inside organizations. Thus, ideas of automation importantly work to uphold the expectations of the value of analytics. Simultaneously, they shape what kinds of expertise, tools, and practices come to be involved in the future of analytics as knowledge production.Peer reviewe
Multimedia big data computing for in-depth event analysis
While the most part of ”big data” systems target text-based analytics, multimedia data, which makes up about 2/3 of internet traffic, provide unprecedented opportunities for understanding and responding to real world situations and
challenges. Multimedia Big Data Computing is the new topic
that focus on all aspects of distributed computing systems that
enable massive scale image and video analytics. During the
course of this paper we describe BPEM (Big Picture Event
Monitor), a Multimedia Big Data Computing framework that
operates over streams of digital photos generated by online
communities, and enables monitoring the relationship between
real world events and social media user reaction in real-time.
As a case example, the paper examines publicly available social media data that relate to the Mobile World Congress 2014 that has been harvested and analyzed using the described system.Peer ReviewedPostprint (author's final draft
#Liberty breach: An exploratory usage case of NodeXL Pro as a social media analytics tool for Twitter
Abstract: Social media analytics uses data mining tools, platforms, and analytics techniques to collect and analyse infinite amounts of social media data. Social media analytics tools extract patterns and connections from data, for insight into market sentiments and requirements, to enhance business intelligence. ‘Network Overview, Discovery and Exploration for Excel Pro’ (NodeXL Pro) is a social media analytics tool that simplifies basic network analysis tasks and supports the analysis of social media networks. NodeXL Pro does sophisticated ‘crawling’ (extracting data) across a range of social media platforms. Through a qualitative case study design, this study explores and describes the use of NodeXL Pro through empirical and multimodal analysis and social network visualisation of social media data of the Liberty Holdings Ltd data breach crisis case in June 2018. The hashtag ‘#Liberty breach’ resulted in 10 000 data sources (‘tweets’) from the social media platform Twitter. This study is unique on two levels. Firstly, it appears to be the first study in the South African marketing literature to use NodeXL Pro in social media analytics. Secondly, it presents the case study as a usage case to describe, in a step‐by‐step way, the functionalities of NodeXL Pro through social network analysis. The main finding of the paper focuses on the usability and manifold features (including the integrated visualisation tool) of NodeXL Pro. This social media analytics tool can open doors for marketing scholars and practitioners alike to measure, map, and model collections of connections
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