This document discusses integrating social search and social navigation to help users more efficiently find relevant information. It describes a system called Community Wisdom that monitors users' search and browsing behaviors to provide social support. An evaluation found users found more relevant papers 22% faster with less effort when social icons showed the activities of other users. Subjects noted the social icons helped them locate information easier on the ACM Digital Library.
Social Network Analysis (SNA) and its implications for knowledge discovery in...ACMBangalore
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Social Network Analysis (SNA) and its implications for knowledge discovery in Informal Networks- Talk by Dr Jai Ganesh, SETLabs, Infosys at Search and Social Platforms tutorial, as part of Compute 2009, ACM Bangalore
2013 NodeXL Social Media Network AnalysisMarc Smith
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Social media network analysis and visualization with NodeXL - the network overview discovery and exploration add-in for Excel. Map Twitter, Facebook, email, blogs, and the web with a point and click interface within the familiar spreadsheet.
In online social media platforms, users can express their ideas by posting original content or by adding comments and responses to existing posts, thus generating virtual discussions and conversations. Studying these conversations is essential for understanding the online communication behavior of users. This study proposes a novel approach to retrieve popular patterns on online conversations using network-based analysis. The analysis consists of two main stages: intent analysis and network generation. Usersâ intention is detected using keyword-based categorization of posts and comments, integrated with classification through NaĂŻve Bayes and Support Vector Machine algorithms for uncategorized comments. A continuous human-in-the-loop approach further improves the keyword-based classification. To build and understand communication patterns among the users, we build conversation graphs starting from the hierarchical structure of posts and comments, using a directed multigraph network. The experiments categorize 90% comments with 98% accuracy on a real social media dataset. The model then identifies relevant patterns in terms of shape and content; and finally determines the relevance and frequency of the patterns. Results show that the most popular online discussion patterns obtained from conversation graphs resemble real-life interactions and communication.
Social Network Analysis (SNA) and its implications for knowledge discovery in...ACMBangalore
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Social Network Analysis (SNA) and its implications for knowledge discovery in Informal Networks- Talk by Dr Jai Ganesh, SETLabs, Infosys at Search and Social Platforms tutorial, as part of Compute 2009, ACM Bangalore
2013 NodeXL Social Media Network AnalysisMarc Smith
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Social media network analysis and visualization with NodeXL - the network overview discovery and exploration add-in for Excel. Map Twitter, Facebook, email, blogs, and the web with a point and click interface within the familiar spreadsheet.
In online social media platforms, users can express their ideas by posting original content or by adding comments and responses to existing posts, thus generating virtual discussions and conversations. Studying these conversations is essential for understanding the online communication behavior of users. This study proposes a novel approach to retrieve popular patterns on online conversations using network-based analysis. The analysis consists of two main stages: intent analysis and network generation. Usersâ intention is detected using keyword-based categorization of posts and comments, integrated with classification through NaĂŻve Bayes and Support Vector Machine algorithms for uncategorized comments. A continuous human-in-the-loop approach further improves the keyword-based classification. To build and understand communication patterns among the users, we build conversation graphs starting from the hierarchical structure of posts and comments, using a directed multigraph network. The experiments categorize 90% comments with 98% accuracy on a real social media dataset. The model then identifies relevant patterns in terms of shape and content; and finally determines the relevance and frequency of the patterns. Results show that the most popular online discussion patterns obtained from conversation graphs resemble real-life interactions and communication.
2015 #MMeasure-Marc Smith-NodeXL Mapping social media using social network ma...Marc Smith
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Networks are a powerful way to understand social media.
This talk reviews the ways the NodeXL application can be used to reveal the social media networks structures around topics.
2014 TheNextWeb-Mapping connections with NodeXLMarc Smith
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Slides from a talk at the 2014 TheNextWeb in Amsterdam.
NodeXL social media network analysis of Twitter reveals six common structures in Twitter networks.
Think Link: Network Insights with No Programming SkillsMarc Smith
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Networks are everywhere, but the tools for end users to access, analyze, visualize and share insights into connected structures have been absent. NodeXL, the network overview discovery and exploration add-in for Excel makes network analysis as easy as making a pie chart.
UNIT II MODELING AND VISUALIZATION
Visualizing Online Social Networks - A Taxonomy of Visualizations - Graph Representation -
Centrality- Clustering - Node-Edge Diagrams - Visualizing Social Networks with Matrix-Based
Representations- Node-Link Diagrams - Hybrid Representations - Modelling and aggregating
social network data â Random Walks and their Applications âUse of Hadoop and Map Reduce -
Ontological representation of social individuals and relationships.
This thesis proposes to help analyzing the characteristics of the heterogeneous social networks that emerge from the use of web-based social applications, with an original contribution that leverages Social Network Analysis with Semantic Web frameworks. Social Network Analysis (SNA) proposes graph algorithms to characterize the structure of a social network and its strategic positions. Semantic Web frameworks allow representing and exchanging knowledge across web applications with a rich typed graph model (RDF), a query language (SPARQL) and schema definition frameworks (RDFS and OWL). In this thesis, we merge both models in order to go beyond the mining of the flat link structure of social graphs by integrating a semantic processing of the network typing and the emerging knowledge of online activities. In particular we investigate how (1) to bring online social data to ontology-based representations, (2) to conduct a social network analysis that takes advantage of the rich semantics of such representations, and (3) to semantically detect and label communities of online social networks and social tagging activities.
Social Media Mining - Chapter 8 (Influence and Homophily)SocialMediaMining
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R. Zafarani, M. A. Abbasi, and H. Liu, Social Media Mining: An Introduction, Cambridge University Press, 2014.
Free book and slides at http://socialmediamining.info/
This Presentation was prepared by Arif Khan
for the Seminar on Introduction to complex systems and social network analysis on 05/03/14 (Wednesday) Organized by BRAC University CSE Department in collaboration with BRAC University Computer Club (BUCC).
The power of the modern Web, which is frequently called the Social Web or Web 2.0, is frequently traced to the power of users as contributors of various kinds of contents through Wikis, blogs, and resource sharing sites. However, the community power impacts not only the production of Web content, but also the access to all kinds of Web content. A number of research groups worldwide explore what we call social information access techniques that help users get to the right information using âcollective wisdomâ distilled from actions of those who worked with this information earlier.
Social information access can be formally defined as a stream of research that explores methods for organizing users' past interaction with an information system (known as explicit and implicit feedback), in order to provide better access to information to the future users of the system. It covers a range of rather different systems and technologies from social navigation to collaborative filtering. An important feature of all social information access systems is self-organization. Social information access systems are able to work with little or no involvement of human indexers, organizers, or other kinds of experts. They are truly powered by a community of users. Due to this feature, social information access technologies are frequently considered as an alternative to the traditional (content-oriented) technologies. The goal of this tutorial is to provide an overview of the emerging social information access research stream and to provide some practical guidelines for building social information access systems.
2015 #MMeasure-Marc Smith-NodeXL Mapping social media using social network ma...Marc Smith
Â
Networks are a powerful way to understand social media.
This talk reviews the ways the NodeXL application can be used to reveal the social media networks structures around topics.
2014 TheNextWeb-Mapping connections with NodeXLMarc Smith
Â
Slides from a talk at the 2014 TheNextWeb in Amsterdam.
NodeXL social media network analysis of Twitter reveals six common structures in Twitter networks.
Think Link: Network Insights with No Programming SkillsMarc Smith
Â
Networks are everywhere, but the tools for end users to access, analyze, visualize and share insights into connected structures have been absent. NodeXL, the network overview discovery and exploration add-in for Excel makes network analysis as easy as making a pie chart.
UNIT II MODELING AND VISUALIZATION
Visualizing Online Social Networks - A Taxonomy of Visualizations - Graph Representation -
Centrality- Clustering - Node-Edge Diagrams - Visualizing Social Networks with Matrix-Based
Representations- Node-Link Diagrams - Hybrid Representations - Modelling and aggregating
social network data â Random Walks and their Applications âUse of Hadoop and Map Reduce -
Ontological representation of social individuals and relationships.
This thesis proposes to help analyzing the characteristics of the heterogeneous social networks that emerge from the use of web-based social applications, with an original contribution that leverages Social Network Analysis with Semantic Web frameworks. Social Network Analysis (SNA) proposes graph algorithms to characterize the structure of a social network and its strategic positions. Semantic Web frameworks allow representing and exchanging knowledge across web applications with a rich typed graph model (RDF), a query language (SPARQL) and schema definition frameworks (RDFS and OWL). In this thesis, we merge both models in order to go beyond the mining of the flat link structure of social graphs by integrating a semantic processing of the network typing and the emerging knowledge of online activities. In particular we investigate how (1) to bring online social data to ontology-based representations, (2) to conduct a social network analysis that takes advantage of the rich semantics of such representations, and (3) to semantically detect and label communities of online social networks and social tagging activities.
Social Media Mining - Chapter 8 (Influence and Homophily)SocialMediaMining
Â
R. Zafarani, M. A. Abbasi, and H. Liu, Social Media Mining: An Introduction, Cambridge University Press, 2014.
Free book and slides at http://socialmediamining.info/
This Presentation was prepared by Arif Khan
for the Seminar on Introduction to complex systems and social network analysis on 05/03/14 (Wednesday) Organized by BRAC University CSE Department in collaboration with BRAC University Computer Club (BUCC).
The power of the modern Web, which is frequently called the Social Web or Web 2.0, is frequently traced to the power of users as contributors of various kinds of contents through Wikis, blogs, and resource sharing sites. However, the community power impacts not only the production of Web content, but also the access to all kinds of Web content. A number of research groups worldwide explore what we call social information access techniques that help users get to the right information using âcollective wisdomâ distilled from actions of those who worked with this information earlier.
Social information access can be formally defined as a stream of research that explores methods for organizing users' past interaction with an information system (known as explicit and implicit feedback), in order to provide better access to information to the future users of the system. It covers a range of rather different systems and technologies from social navigation to collaborative filtering. An important feature of all social information access systems is self-organization. Social information access systems are able to work with little or no involvement of human indexers, organizers, or other kinds of experts. They are truly powered by a community of users. Due to this feature, social information access technologies are frequently considered as an alternative to the traditional (content-oriented) technologies. The goal of this tutorial is to provide an overview of the emerging social information access research stream and to provide some practical guidelines for building social information access systems.
Contextual model of recommending resources on an academic networking portalcsandit
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Artificial Intelligence techniques have been instrumental in helping users to handle the large
amount of information on the Internet. The idea of recommendation systems, custom search
engines, and intelligent software has been widely accepted among users who seek assistance in
searching, sorting, classifying, filtering and sharing this vast quantity of information. In this
paper, we present a contextual model of recommendation engine which keeping in mind the
context and activities of a user, recommends resources in an academic networking portal. The
proposed method uses the implicit method of feedback and the concepts relationship hierarchy
to determine the similarity between a user and the resources in the portal. The proposed
algorithm has been tested on an academic networking portal and the results are convincing.
CONTEXTUAL MODEL OF RECOMMENDING RESOURCES ON AN ACADEMIC NETWORKING PORTALcscpconf
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Artificial Intelligence techniques have been instrumental in helping users to handle the large amount of information on the Internet. The idea of recommendation systems, custom search engines, and intelligent software has been widely accepted among users who seek assistance insearching, sorting, classifying, filtering and sharing this vast quantity of information. In thispaper, we present a contextual model of recommendation engine which keeping in mind the context and activities of a user, recommends resources in an academic networking portal. Theproposed method uses the implicit method of feedback and the concepts relationship hierarchy to determine the similarity between a user and the resources in the portal. The proposed algorithm has been tested on an academic networking portal and the results are convincing
Global Redirective Practices: an online workshop for a clientSean Connolly
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This slidedeck is an exhaustive report consisting of research in sociological literature, user research in focus groups, competitive analysis of similar tools, and, designing for a client with no money and no technical ability.
[Because this was a presentation, much of the information is supplied by the presenter. Critical information of the presentation has been added to the slide deck as 'Notes:']
Cross discipline collaboration benefits from group think, a consolidation of soft system methodology and user focused design that all starts with design thinking that sees clients, designers, developers and information architects working together to address user problems and needs. As with any great adventure, design thinking starts with exploration and discovery.This presentation examines the high level tenants of system thinking, expands the scope of user thinking to include tools and devices that users employ to find out designs and delve into the specifics of design thinking, its methods and outcomes.
Recommender System (RS) has emerged as a significant research interest that aims to assist users to seek out items online by providing suggestions that closely match their interests. Recommender system, an information filtering technology employed in many items is presented in internet sites as per the interest of users, and is implemented in applications like movies, music, venue, books, research articles, tourism and social media normally. Recommender systems research is usually supported comparisons of predictive accuracy: the higher the evaluation scores, the higher the recommender. One amongst the leading approaches was the utilization of advice systems to proactively recommend scholarly papers to individual researchers. In today's world, time has more value and therefore the researchers haven't any much time to spend on trying to find the proper articles in line with their research domain. Recommender Systems are designed to suggest users the things that best fit the user needs and preferences. Recommender systems typically produce an inventory of recommendations in one among two ways -through collaborative or content-based filtering. Additionally, both the general public and also the non-public used descriptive metadata are used. The scope of the advice is therefore limited to variety of documents which are either publicly available or which are granted copyright permits. Recommendation systems (RS) support users and developers of varied computer and software systems to beat information overload, perform information discovery tasks and approximate computation, among others.
Recommender System (RS) has emerged as a significant research interest that aims to assist users to seek out items online by providing suggestions that closely match their interests. Recommender system, an information filtering technology employed in many items is presented in internet sites as per the interest of users, and is implemented in applications like movies, music, venue, books, research articles, tourism and social media normally. Recommender systems research is usually supported comparisons of predictive accuracy: the higher the evaluation scores, the higher the recommender. One amongst the leading approaches was the utilization of advice systems to proactively recommend scholarly papers to individual researchers. In today's world, time has more value and therefore the researchers haven't any much time to spend on trying to find the proper articles in line with their research domain. Recommender Systems are designed to suggest users the things that best fit the user needs and preferences. Recommender systems typically produce an inventory of recommendations in one among two ways -through collaborative or content-based filtering. Additionally, both the general public and also the non-public used descriptive metadata are used. The scope of the advice is therefore limited to variety of documents which are either publicly available or which are granted copyright permits. Recommendation systems (RS) support users and developers of varied computer and software systems to beat information overload, perform information discovery tasks and approximate computation, among others.
Social Information Access: A Personal UpdateDaqing He
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A Presentation given at Nanjing University of Science and Technology. Summarizes the relevant work developed at IRIS lab at School of Information Sciences, University of Pittsburgh.
Strategic impact of Social Media in tourism- Research paperAmbuj Saxena
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In today's world, social media has made it convenient for people to search, book and plan their next trip at the click of a mouse button. This research paper, co-authored by Ambuj Saxena, Dr. GG Saxena, and Dr. Sajeevan Rao takes you through the travel enthusiast's online journey while booking a leisure trip.
How does the travel enthusiast make up his mind to travel using social media and which social media channels affect him the most and how? What are the social, psychological and hedonic effects while planning a trip using social media and how reliable, entertaining and valuable do tourists consider social media sites while planning a trip. These are some of the important questions and customer behavior being brought out through this research paper that was presented in Jammu University's 4th International Conference on Sustainable Destination Excellence with the theme: Empowering Communities for Inclusive Tourism Development.
If you wish to know the experience of presenting my research paper in Jammu University, you may access my blog post: http://wp.me/p8r6B6-jQ
Sources of inspiration:
http://www.pewinternet.org/fact-sheets/social-networking-fact-sheet/
www.adweek.com
The Implementation of Social Media for Educational Objectivestheijes
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The International Journal of Engineering & Science is aimed at providing a platform for researchers, engineers, scientists, or educators to publish their original research results, to exchange new ideas, to disseminate information in innovative designs, engineering experiences and technological skills. It is also the Journal's objective to promote engineering and technology education. All papers submitted to the Journal will be blind peer-reviewed. Only original articles will be published.
The papers for publication in The International Journal of Engineering& Science are selected through rigorous peer reviews to ensure originality, timeliness, relevance, and readability.
Theoretical work submitted to the Journal should be original in its motivation or modeling structure. Empirical analysis should be based on a theoretical framework and should be capable of replication. It is expected that all materials required for replication (including computer programs and data sets) should be available upon request to the authors.
The International Journal of Engineering & Science would take much care in making your article published without much delay with your kind cooperation
Recommendation System Using Social Networking ijcseit
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With the proliferation of electronic commerce and knowledge economy environment both organizations and
individuals generate and consume a large amount of online information. With the huge availability of
product information on website, many times it becomes difficult for a consumer to locate item he wants to
buy. Recommendation Systems [RS] provide a solution to this. Many websites such as YouTube, e-Bay,
Amazon have come up with their own versions of Recommendation Systems. However Issues like lack of
data, changing data, changing user preferences and unpredictable items are faced by these
recommendation systems. In this paper we propose a model of Recommendation systems in e-commerce
domain which will address issues of cold start problem and change in user preference problem. Our work
proposes a novel recommendation system which incorporates user profile parameters obtained from Social
Networking website. Our proposed model SNetRS is a collaborative filtering based algorithm, which
focuses on user preferences obtained from FaceBook. We have taken domain of books to illustrate our
model.
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Jill Freyne - Collecting community wisdom: integrating social search and social navigation
1. Collecting Community Wisdom: Integrating Social Search & Social Navigation Jill Freyne, Rosta Farzan, Peter Brusilovsky, Barry Smyth & Maurice Coyle University College Dublin & University of Pittsburgh
14. Integration - Search CACM Search Engine Navigation Engine CWS Engine Hit Matrix Navigation (Browsing & Annotation) Records Result Set Re-ranked result With social search icons Re-ranked result With social search and social navigation icons Users start with search
15. Integration - Browsing Users start with browsing Navigation Engine CWS Engine Search Hit Matrix Navigation (Browsing & Annotation) Records Adding social search icons Adding social navigation icons Back to search Query based navigation
17. Search Support Relevance: 100% Related Queries: Social Navigation Computational Wear Last Selection: 30 mins ago Last Browse: 1 day ago Last Annotation: 1 day ago Browse Popularity 30% Annotations: Relevant to previous work by Dourish and Chalmers in Social Navigation
18. Browsing Support Queries: personalization business customer adaptive web Browse Popularity : 5% Annotation: âŚâŚâŚâŚ
29. Collecting Community Wisdom: Integrating Social Search & Social Navigation Jill Freyne, Rosta Farzan, Peter Brusilovsky, Barry Smyth & Maurice Coyle University College Dublin & University of Pittsburgh
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