scholarly journals Event-Based User Classification in Weibo Media

2014 ◽  
Vol 2014 ◽  
pp. 1-11 ◽  
Author(s):  
Liang Guo ◽  
Wendong Wang ◽  
Shiduan Cheng ◽  
Xirong Que
Keyword(s):  
Social Network ◽  
Real Data ◽  
Network Information ◽  
User Classification ◽  
Social Events ◽  
The Social ◽  
Access Information ◽  
Primary Groups ◽  
Event Based ◽  
Content Information

Weibo media, known as the real-time microblogging services, has attracted massive attention and support from social network users. Weibo platform offers an opportunity for people to access information and changes the way people acquire and disseminate information significantly. Meanwhile, it enables people to respond to the social events in a more convenient way. Much of the information in Weibo media is related to some events. Users who post different contents, and exert different behavior or attitude may lead to different contribution to the specific event. Therefore, classifying the large amount of uncategorized social circles generated in Weibo media automatically from the perspective of events has been a promising task. Under this circumstance, in order to effectively organize and manage the huge amounts of users, thereby further managing their contents, we address the task of user classification in a more granular, event-based approach in this paper. By analyzing real data collected from Sina Weibo, we investigate the Weibo properties and utilize both content information and social network information to classify the numerous users into four primary groups: celebrities, organizations/media accounts, grassroots stars, and ordinary individuals. The experiments results show that our method identifies the user categories accurately.

Competence Management over Social Networks Through Dynamic Taxonomies

2013 ◽  
pp. 103-120
Author(s):  
Giuseppe Berio ◽  
Antonio Di Leva ◽  
Mounira Harzallah ◽  
Giovanni M. Sacco
Keyword(s):  
Social Networks ◽  
Social Network ◽  
Real World ◽  
Reference Model ◽  
Effective Solution ◽  
Network Information ◽  
The Social ◽  
Competence Management

The exploitation and integration of social network information in a competence reference model (CRAI, Competence, Resource, Aspect, Individual) are discussed. The Social-CRAI model, which extends CRAI to social networks, provides an effective solution to this problem and is discussed in detail. Finally, dynamic taxonomies, a model supporting explorative conceptual search, are introduced and their use in the context of the Social-CRAI model for exploring retrieved information available in social networks is discussed. A real-world example is provided.

Social Network Mining from the Web

2011 ◽  
pp. 149-175 ◽  
Author(s):  
Yutaka Matsuo ◽  
Junichiro Mori ◽  
Mitsuru Ishizuka
Keyword(s):  
Social Network ◽  
Search Engine ◽  
Relevant Information ◽  
Web Content ◽  
Message Boards ◽  
Network Information ◽  
Network Mining ◽  
Social Network Mining ◽  
The Social ◽  
The Web

This chapter describes social network mining from the Web. Since the end of the 1990s, several attempts have been made to mine social network information from e-mail messages, message boards, Web linkage structure, and Web content. In this chapter, we specifically examine the social network extraction from the Web using a search engine. The Web is a huge source of information about relations among persons. Therefore, we can build a social network by merging the information distributed on the Web. The growth of information on the Web, in addition to the development of a search engine, opens new possibilities to process the vast amounts of relevant information and mine important structures and knowledge.

Toward Social-Semantic Recommender Systems

2016 ◽  
Vol 7 (1) ◽  
pp. 1-30 ◽  
Author(s):  
Dalia Sulieman ◽  
Maria Malek ◽  
Hubert Kadima ◽  
Dominique Laurent
Keyword(s):  
Social Network ◽  
Collaborative Filtering ◽  
Recommender Systems ◽  
Semantic Information ◽  
Basic Problem ◽  
Real Data ◽  
Data Sets ◽  
The Social ◽  
Recommendation Algorithms ◽  
Hybrid Recommendation

In this article, the authors consider the basic problem of recommender systems that is identifying a set of users to whom a given item is to be recommended. In practice recommender systems are run against huge sets of users, and the problem is then to avoid scanning the whole user set in order to produce the recommendation list. To cope with problem, they consider that users are connected through a social network and that taxonomy over the items has been defined. These two kinds of information are respectively called social and semantic information. In their contribution the authors suggest combining social information with semantic information in one algorithm in order to compute recommendation lists by visiting a limited part of the social network. In their experiments, the authors use two real data sets, namely Amazon.com and MovieLens, and they compare their algorithms with the standard item-based collaborative filtering and hybrid recommendation algorithms. The results show satisfying accuracy values and a very significant improvement of performance, by exploring a small part of the graph instead of exploring the whole graph.

Opinion evolution in society with stubborn agents using Temporal Model for Opinion and Triad Network Formation (TMOTNF)

2021 ◽  
Vol 9 (5) ◽  
Author(s):  
Eeti Jain ◽  
Anurag Singh
Keyword(s):  
Social Network ◽  
Information Diffusion ◽  
Network Formation ◽  
Threshold Value ◽  
Network Information ◽  
The Social ◽  
Convergence Point ◽  
Temporal Modelling ◽  
Temporal Model ◽  
Opinion Evolution

Abstract Information diffusion is an important part of the social network. Information flows between the individuals in the social networks to shape and update their opinions about various topics. The updated opinion values of them further spread the information in the network. The social network is always evolving by nature, leading to the dynamics of the network. Connections keep on changing among the individuals based on the various characteristics of the networks and individuals. Opinions of individuals may again be affected by the changes in the network which leads to dynamics on the network. Therefore, the co-evolving nature of dynamics on/of the network is proposed. Co-evolving Temporal Model for Opinion and Triad Network Formation is modelled to evaluate the opinion convergence. Some fully stubborn agents are chosen in the network to affect opinion evolution, framing society’s opinion. It is also analysed how these agents can divert the whole network towards their opinion values. When temporal modelling is done using all the three conditions, Triadic Closure, Opinion Threshold value and the Page Rank value over the network, the network does not reach consensus at the convergence point. Various individuals with different opinion values still exist.

scholarly journals The Influence of Social Network Structure on the Farmer Group Participation in Indonesia

2016 ◽  
Vol 12 (3) ◽  
pp. 119 ◽  
Author(s):  
Alia Bihrajihant Raya
Keyword(s):  
Social Network ◽  
Network Structure ◽  
Group Leaders ◽  
Neighborhood Association ◽  
Social Network Structure ◽  
Local Institution ◽  
The Social ◽  
Group Members ◽  
Access Information ◽  
The Relationship

<p>The development of farmer groups in Indonesia is being stagnant because of the function of farmer group could not afford the needs of farmer group members. Participation of members is crucial to be assessed in order to promote the development of farmer group. To increase the participation of members, the social network structure between members and leaders should be taken into consideration. In this paper, the function of local institution leaders together with the function of farmer group leaders are measured in the social network structure. Through the graph of social network, it found that members will access information easily through the routine meeting in the local institution (neighborhood association) while the farmer group leaders are functioning as a legitimate of farmer group agenda. This paper suggests that the relationship between member and leader on the social network structure influences the member participation in the farmer group.</p>

Ontology Combined Based on the Social Network Information

2015 ◽  
pp. 821-828
Author(s):  
Zhengyang Wu ◽  
Yong Tang ◽  
Shaowen Hong ◽  
Chengzhe Yuan ◽  
Huiqiang Mai
Keyword(s):  
Social Network ◽  
Network Information ◽  
The Social

scholarly journals Study Partners Recommendation for xMOOCs Learners

2015 ◽  
Vol 2015 ◽  
pp. 1-10 ◽  
Author(s):  
Bin Xu ◽  
Dan Yang
Keyword(s):  
Social Network ◽  
Large Scale ◽  
Topic Model ◽  
Online Courses ◽  
Completion Rate ◽  
Network Information ◽  
Teachers And Students ◽  
Study Partner ◽  
The Relationship ◽  
Content Information

Massive open online courses (MOOCs) provide an opportunity for people to access free courses offered by top universities in the world and therefore attracted great attention and engagement from college teachers and students. However, with contrast to large scale enrollment, the completion rate of these courses is really low. One of the reasons for students to quit learning process is problems which they face that could not be solved by discussing them with classmates. In order to keep them staying in the course, thereby further improving the completion rate, we address the task of study partner recommendation for students based on both content information and social network information. By analyzing the content of messages posted by learners in course discussion forum, we investigated the learners’ behavior features to classify the learners into three groups. Then we proposed a topic model to measure learners’ course knowledge awareness. Finally, a social network was constructed based on their activities in the course forum, and the relationship in the network was then employed to recommend study partners for target learner combined with their behavior features and course knowledge awareness. The experiment results show that our method achieves better performance than recommending method only based on content information.

scholarly journals Spontaneous Neural Encoding of Social Network Position

2017 ◽  
Author(s):  
Carolyn M. Parkinson ◽  
Adam M. Kleinbaum ◽  
Thalia Wheatley
Keyword(s):  
Social Networks ◽  
Social Network ◽  
Human Brain ◽  
Brain Evolution ◽  
Neural Encoding ◽  
Form Complex ◽  
Network Information ◽  
Human Brain Evolution ◽  
The Social ◽  
Social Network Position

Humans form complex social networks that include numerous non-reproductive bonds with non-kin. Navigating these networks presents a considerable cognitive challenge thought to have comprised a driving force in human brain evolution. Yet, little is known about how and to what extent the human brain encodes the structure of the social networks in which it is embedded. By combining social network analysis and multi-voxel pattern analysis of functional magnetic resonance imaging (fMRI) data, we show that social network information about direct relationships, bonds between third parties, and aspects of the broader network topology is accurately perceived and automatically activated upon seeing a familiar other.

scholarly journals General practice and patients’ views of the social networks of patients with multimorbidity

2018 ◽  
Vol 10 (3) ◽  
pp. 258 ◽  
Author(s):  
Eileen McKinlay ◽  
Jessica Young ◽  
Ben Gray
Keyword(s):  
Social Networks ◽  
General Practice ◽  
Social Network ◽  
Clinical Practice ◽  
Active Role ◽  
Practice Staff ◽  
Network Information ◽  
The Social ◽  
Practice Approach ◽  
Practice Knowledge

ABSTRACT INTRODUCTION For patients with multimorbidity to live well, they need the support of not only health professionals but family, friends and organisations. These social networks provide support, potentially enabling the formation of a Community of Clinical Practice approach to multimorbidity care. AIM This study aimed to explore general practice knowledge of the social networks of patients with multimorbidity. METHODS Social network maps were completed by both patients and general practice. The social network maps of 22 patients with multimorbidity were compared with corresponding social network maps completed by general practice staff. RESULTS In 60% (13/22) of the patients, general practice staff held a high or moderate knowledge of individual patients’ social networks. Information on social networks was recalled from staff memory and not systematically recorded in patients’ electronic health records. DISCUSSION Social network information is not routinely collected, recorded or used by general practice to understand the support available to patients with multimorbidity. General practice could take an active role in coordinating social network supporters for certain patient groups with complex multimorbidity. For these groups, there is value in systematically recording and regularly updating their social network information for general practice to use as part of a coordinated Community of Clinical Practice.

scholarly journals Privacy-Preserving Discovery of Topic-Based Events from Social Sensor Signals: An Experimental Study on Twitter

2014 ◽  
Vol 2014 ◽  
pp. 1-5 ◽  
Author(s):  
Duc T. Nguyen ◽  
Jai E. Jung
Keyword(s):  
Social Network ◽  
Information Diffusion ◽  
Relevant Information ◽  
Network Services ◽  
Social Software ◽  
Smart Devices ◽  
Social Events ◽  
The Social ◽  
Social Network Services ◽  
Social Sensor

Social network services (e.g., Twitter and Facebook) can be regarded as social sensors which can capture a number of events in the society. Particularly, in terms of time and space, various smart devices have improved the accessibility to the social network services. In this paper, we present a social software platform to detect a number of meaningful events from information diffusion patterns on such social network services. The most important feature is to process the social sensor signal for understanding social events and to support users to share relevant information along the social links. The platform has been applied to fetch and cluster tweets from Twitter into relevant categories to reveal hot topics.

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