90 resources related to Social Analytics
- Topics related to Social Analytics
- IEEE Organizations related to Social Analytics
- Conferences related to Social Analytics
- Periodicals related to Social Analytics
- Most published Xplore authors for Social Analytics
Since 1980, the IEEE Symposium on Security and Privacy has been the premier forum for presenting developments in computer security and electronic privacy, and for bringing together researchers and practitioners in the field.
HRI is a highly selective annual conference that showcases the very best research and thinking in human-robot interaction. HRI is inherently interdisciplinary and multidisciplinary, reflecting work from researchers in robotics, psychology, cognitive science, HCI, human factors, artificial intelligence, organizational behavior, anthropology, and many other fields.
ICPR will be an international forum for discussions on recent advances in the fields of Pattern Recognition, Machine Learning and Computer Vision, and on applications of these technologies in various fields
Computer in Technical Systems, Intelligent Systems, Distributed Computing and VisualizationSystems, Communication Systems, Information Systems Security, Digital Economy, Computersin Education, Microelectronics, Electronic Technology, Education
The conference covers theory, design and application of computer networks and distributed systems.
The IEEE Transactions on Automation Sciences and Engineering (T-ASE) publishes fundamental papers on Automation, emphasizing scientific results that advance efficiency, quality, productivity, and reliability. T-ASE encourages interdisciplinary approaches from computer science, control systems, electrical engineering, mathematics, mechanical engineering, operations research, and other fields. We welcome results relevant to industries such as agriculture, biotechnology, healthcare, home automation, maintenance, manufacturing, pharmaceuticals, retail, ...
Computer, the flagship publication of the IEEE Computer Society, publishes peer-reviewed technical content that covers all aspects of computer science, computer engineering, technology, and applications. Computer is a resource that practitioners, researchers, and managers can rely on to provide timely information about current research developments, trends, best practices, and changes in the profession.
IEEE Computer Graphics and Applications (CG&A) bridges the theory and practice of computer graphics. From specific algorithms to full system implementations, CG&A offers a strong combination of peer-reviewed feature articles and refereed departments, including news and product announcements. Special Applications sidebars relate research stories to commercial development. Cover stories focus on creative applications of the technology by an artist or ...
IEEE Transactions on Industrial Informatics focuses on knowledge-based factory automation as a means to enhance industrial fabrication and manufacturing processes. This embraces a collection of techniques that use information analysis, manipulation, and distribution to achieve higher efficiency, effectiveness, reliability, and/or security within the industrial environment. The scope of the Transaction includes reporting, defining, providing a forum for discourse, and informing ...
IEEE Intelligent Systems, a bimonthly publication of the IEEE Computer Society, provides peer-reviewed, cutting-edge articles on the theory and applications of systems that perceive, reason, learn, and act intelligently. The editorial staff collaborates with authors to produce technically accurate, timely, useful, and readable articles as part of a consistent and consistently valuable editorial product. The magazine serves software engineers, systems ...
2015 IEEE 15th International Conference on Advanced Learning Technologies, 2015
Online learning communities have become an important place serving informal learning due to the prevalence of online social networking services during the past few years. This paper proposes a social analytics framework aiming to boost recommendation service catering for the different learning demands of learners. Based on the traditional collaborative filtering approach, this study focuses on constructing topic-specific user credibility ...
2012 Proceedings of the 35th International Convention MIPRO, 2012
Today's in highly competitive market place, push mobile operators to optimize their subscriber's life time value by implementing efficient marketing campaigns. Social analytics offer the means to act upon subscriber information with the right target, the right timing and the right offer. Mobile operators can target campaigns, increasing customer loyalty, preventing churn, maximizing ARPU based on Alpha Score and therefore ...
2012 Brazilian Symposium on Collaborative Systems, 2012
This paper contributes to the emerging field of social-analytics in computer- supported cooperative work by introducing a novel methodology for investigating the "work-networks" that emerge from everyday interactions among workers, artifacts, and organizational structures. Particularly, it presents and discusses an early implementation of this methodology in the context of a large, global IT service delivery organization. It analyzes the pattern ...
2017 IEEE/ACS 14th International Conference on Computer Systems and Applications (AICCSA), 2017
In this work, we describe a methodology for leveraging large amounts of customer interaction data with online content from major social media platforms in order to isolate meaningful customer segments. The methodology is robust in that it can rapidly identify diverse customer segments using solely online behaviors and then associate these behavioral customer segments with the related distinct demographic segments, ...
2015 10th International Workshop on Semantic and Social Media Adaptation and Personalization (SMAP), 2015
In this paper, we propose an ontology schema towards linking semantified Twitter social analytics with the Linked Open Data cloud. The ontology is deployed over a publicly available service that measures how influential a Twitter account is by combining its social activity in Twitter. According to our knowledge this is the first work that combines social analytics with the Linked ...
Panel: IoT - Smart Networks & Social Innovations - VIC Summit 2019
A Conversation with…Richard Mallah: IEEE TechEthics
IEEE Themes - Science of Social Networking
Panel: Ethics in AI - Impacts of (Anti?) Social Robotics - VIC Summit 2019
Shaping the Future Workforce: Transformative Impacts of Emerging Technologies | IEEE TechEthics Public Forum
How Fog Computing Can Enhance Public Safety - Eileen Healy and Aakanksha Chowdhery, Fog World Congress 2017
IEEE Themes - Efficient networking services underpin social networks
Big Data Analytics: Tools and Technologies - Big Data Analytics Tutorial Part 2
Performance Analytics, Valuation Trends & the M&A Market
Mahmoud Daneshmand on IoT and Big Data Analytics: IoT: Even Bigger Data
Jeff Voas on the Internet of Things and Big Data Analytics - WF-IoT 2015
Analytics for Anomaly detection & Classification | DSBC 2020
Luciano Oviedo on the Social Impact of Fog Computing - Fog World Congress
Big Data and Analytics at Verizon
Netflix Analytics: Movie Recommendation using Correlations | DSBC 2020
Gender-Based Occupational Stereotypes: New Behaviors, Old Attitudes - Carolyn Matheus & Elizabeth Quinn - IEEE WIE Forum USA East 2017
Kazunori Iwasa: Challenges in Controlling Data Center Facilities - IoT Challenges Industry Forum Panel: WF IoT 2016
Challenges and SP Tools for Big Data Analytics
IEEE Themes - Social Networks: Dynamic Social Interaction Data
Online learning communities have become an important place serving informal learning due to the prevalence of online social networking services during the past few years. This paper proposes a social analytics framework aiming to boost recommendation service catering for the different learning demands of learners. Based on the traditional collaborative filtering approach, this study focuses on constructing topic-specific user credibility network by considering social relations and user behaviors. Both direct and indirect connections evidence from social analytics provide complementary information to construct user trust network. Regarding the topic-specific user credibility network, two features including influence and expertise are also computed to refine the credibility value between users. Furthermore, the performances of learners were further investigated in terms of longevity and centrality that could be referred when selecting suitable people for recommendation.
Today's in highly competitive market place, push mobile operators to optimize their subscriber's life time value by implementing efficient marketing campaigns. Social analytics offer the means to act upon subscriber information with the right target, the right timing and the right offer. Mobile operators can target campaigns, increasing customer loyalty, preventing churn, maximizing ARPU based on Alpha Score and therefore optimizes the acquisition and retention investments.
This paper contributes to the emerging field of social-analytics in computer- supported cooperative work by introducing a novel methodology for investigating the "work-networks" that emerge from everyday interactions among workers, artifacts, and organizational structures. Particularly, it presents and discusses an early implementation of this methodology in the context of a large, global IT service delivery organization. It analyzes the pattern of work interactions that emerge from the mining of common use of internal social media systems, log data of service delivery management systems, and organizational structures. Such analyses enabled us to unearth potential deficiencies in the ways in which the organization make use of collaborative systems, share and spread knowledge among its workers.
In this work, we describe a methodology for leveraging large amounts of customer interaction data with online content from major social media platforms in order to isolate meaningful customer segments. The methodology is robust in that it can rapidly identify diverse customer segments using solely online behaviors and then associate these behavioral customer segments with the related distinct demographic segments, presenting a holistic picture of the customer base of an organization. We validate our methodology via the implementation of a working system that rapidly and in near real-time processes tens of millions of online customer interactions with content posted on major social media platforms in order to identify both the distinct behavioral segments and corresponding impactful demographic segments. We illustrate the functionality of the methodology with real data from a major online content provider with millions of online interactions from more than thirty countries. We further show one possible use for such information via the automatic generation of personas for an organization, which can be used for the formulation of marketing strategy, implementation of advertising plans, or development of products. The research results offer insights into competitive marketing and product preferences for the consumers of online digital content. We conclude with a discussion of areas for future work.
In this paper, we propose an ontology schema towards linking semantified Twitter social analytics with the Linked Open Data cloud. The ontology is deployed over a publicly available service that measures how influential a Twitter account is by combining its social activity in Twitter. According to our knowledge this is the first work that combines social analytics with the Linked Open Data (LOD) cloud.
On behalf of the rapidly and widely disseminated smartphone technology into the public, lots of social network sites and location-based social applications are accumulating a huge volume of massive crowd's daily experiences and thoughts in an unprecedented scale. We can regard them as novel data sources for accomplishing various social analytics, which have usually required lots of efforts to collect crowds' opinion and behavioral data. Thus, we can take advantages of abundant social datasets by integrating them appropriately. However, when we integrate disparate sources to derive a comprehensive view for a survey, it is necessary to know intrinsic exclusive values of each data source compared to others in an intuitive and succinct way. In fact, lots of efforts and time are wasted to overview various datasets consequently to confidently choose a dataset to be integrated in a final result. In this paper, we propose a complementarity index, which can estimate the exclusive usefulness of data sources in terms of spatial and topical coverage when selecting data sources for social analytics purposes. We conducted an experiment about complementarity measurement with two real social datasets from Twitter and VoiceTra; the latter is a speech-to-speech translation app, with which we can additionally obtain crowds' verbal translation logs. With the proposed complementarity index, we can measure the capability of a dataset comparing to others before integrating datasets, thus enabling analysts to examine much more datasets from as many related data sources as possible by focusing on exclusive coverage and relative strength of relevant topics.
With the help of several emerging computing paradigms, such as Ubiquitous Computing, Social Computing, and Mobile Computing, OLP (Open Learning Process) has become an important research issue in the online learning environment. It becomes possible to share the learning process related information in and cross systems to meet the needs of all stakeholders, such as teachers, learners and managers. In this study, we focus on the OLP in the social learning environment based on the personal and social analytics, which can provide users with the process-oriented learning support. We present a data processing framework to collect, analyze, and organize the learning-related data in a user-centric way. Three important issues regarding to the unified data management, socialized learning analytics, and sharable open learning process are discussed towards the process-oriented learning innovation. Based on these, an open learning platform is proposed and designed to provide users with the individualized support in the collaborative learning process.
Betweenness centrality is a measure that determines the relative importance of a vertex (or an edge) within a graph based on shortest paths. Recently, large- scale graphs have emerged in many different domains, as social networks, road networks, protein interaction networks, etc., and they are too large to fit into the memory of a single SMP. The algorithm proposed by Edmonds et al.  is capable of running on distributed memory systems. However, the algorithm does not expose intra-node parallelism. In this paper we investigated the inter- and intra-node parallelism of computing betweenness centrality on distributed memory systems. We developed the implementation based on the algorithm proposed by Edmonds et al. using X10 programming language . We further improved the performance of the implementation by optimizing the network transport of the X10 runtime. We thoroughly evaluated the performance of our implementation on synthetic graphs of various scales against the existing implementation of Edmonds' algorithm from PBGL. We estimated the betweenness centrality of the huge Twitter networks  and found that its distribution follows a power law.
Although customers become more and more vocal in expressing their experiences, demands and needs in various social networks, companies of any size typically fail to effectively gain insights from such social data and to eventually catch the market realm. This paper introduces the Anlzer analytics engine that aims at leveraging the "social" data deluge to help companies in their quest for deeper understanding of their products' perceptions as well as of the emerging trends in order to early embed them into their product design phase. The proposed approach brings together polarity detection and trend analysis techniques as presented in the architecture and demonstrated through a simple walkthrough in the Anlzer solution. The Anlzer implementation is by design domain-independent and is being tested in the furniture domain at the moment, yet it brings significant added value to software design and development, as well, through its experimentation playground that may provide indirect feedback on future software features while monitoring the reactions to existing releases.
When providing customers with a personalized shopping experience, there is tremendous value in understanding and applying social data shared by those consumers. Understanding this data and how best to generate business value from it is the core challenge of many businesses today. Friends, family, and experts alike influence consumers in their shopping preferences and purchase decisions. Yet, the ability of a business to analyze data on such influence, and recommend products and services that best respond to its customers' needs or aspirations, is typically limited by fragmented capabilities; a business relies heavily on the use of spreadsheets, manual market analysis, isolated software, or reactive messaging. This paper offers a solution to this fragmentary approach by introducing a social analytics platform for smarter commerce. This platform provides a holistic understanding of the customer by making use of social and enterprise data to present recommendations and related opinions, and to isolate influencers so as to ultimately provide customers with a personalized shopping experience. The functionality described in this paper is in the context of the retail industry but can be applied to other industries. The paper describes the architecture of the social analytics platform and the various analytics components currently implemented as part of the platform.
No standards are currently tagged "Social Analytics"