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Proceedings ArticleDOI

Identification of topic-specific Opinion Leader using SPEAR algorithm in Online Knowledge communities

01 Dec 2016-pp 144-149
TL;DR: Modified SPEAR algorithm effectively identifies Opinion Leader by making use of additional influence measures in the form of credit score functions and analyses these measures and studies their effects while ranking the Opinion Leader(s) effectively.
Abstract: Currently Internet usage has increased a lot due to bandwidth availaility and technology advancements. Internet is widely used for knowledge sharing, online review of products etc. Many open forums, blogs are used for this purpose. Since many users are contributing their opinions towards any query submitted by information seeker, there is a possibility of confusion. Often opinions contradict with each other creating confusion in information seeker's mind. In these cases role of Opinion Leader(s) is very prominent. Opinion Leader is a person who has knowledge in the particular field, who's opinion makes difference and who can influence other's opinions. Identification of a person who has great experiences and/or knowledge, in a particular domain, is very helpful and useful in decision making, product marketing etc. This paper presents an approach for identification of Opinion Leader(s) using modified SPEAR (Spamming Resistant Expertise Analysis and Ranking) algorithm. The expertise of user is found out on different topics. Modified SPEAR algorithm effectively identifies Opinion Leader(s) by making use of additional influence measures in the form of credit score functions. It also analyses these measures and studies their effects while ranking the Opinion Leader(s) effectively.
Citations
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Journal ArticleDOI
TL;DR: This review of the well-known techniques for opinion leader detection problems is classified into descriptive approaches, statistical and stochastic methods, diffusion process based approaches, topological based methods, data mining and learning methods, and approaches based on hybrid content mining.
Abstract: A social network as an essential communication platform facilitates the interactions of online users. Based on the interactions, users can influence or be affected by the opinions of others. The users being able to influence and shape the opinions of others are considered as opinion leaders. The problem of identifying opinion leaders is an important task due to its wide applications in reality, including product adoption for marketing and societal analytics. The problem has been attracting proliferating studies over the recent years. To overview and provide insights of the methodologies and enlighten the future study, we review the well-known techniques for opinion leader detection problems. These techniques are classified into descriptive approaches, statistical and stochastic methods, diffusion process based approaches, topological based methods, data mining and learning methods, and approaches based on hybrid content mining. The advantages and drawbacks of each method are systematically analyzed and compared, to provide deep understanding into the existing research challenges and the direction of future trends. The findings of this review would be useful for those researchers are interested in identifying opinion leaders and influencers in social networks and related fields.

89 citations

Journal ArticleDOI
TL;DR: A new approach for detecting opinion leaders based on analyzing online community interactions and dealing with the dynamic aspect of social networks is presented.
Abstract: Social media networks have revolutionized the way users interact and express their opinion. Obviously, identifying opinion leaders has a widespread applicability. For instance, by detecting leaders, companies can manipulate the public opinion. However, this task is challenging due to the complexity and the ceaseless change of the social networks structure. Yet, existing opinion leaders 'detection methods have essentially focused on static social graphs neglecting the temporal characteristics. Therefore, the necessity of identifying opinion leaders seems to be more and more crucial. In this context, we present a new approach for detecting opinion leaders based on analyzing online community interactions and dealing with the dynamic aspect of social networks. The experiments are performed on real data and the comparison of the proposed approach with commonly used approaches showed a good performance.

17 citations

Proceedings ArticleDOI
25 May 2018
TL;DR: A social network is constructed based on the probability of communication, and a PCA-SNA recognition model of opinion leaders combined with social network analysis is built, valid on opinion leader discovery in SNS, and promisingly to be generalized and popularized.
Abstract: I209 recent years, with the rapid development of social networks, more and more researchers have focused on the discovery of opinion leaders in the social network. The social network site (SNS) is a typical social network. Whereby identifying the opinion leaders in the community, enterprises can carry out more targeted marketing activities, and the government can also use opinion leaders to guide the trend of public opinion. However, due to the high complexity of social network and the randomness and contingency of behaviors of opinion leaders, it is a challenging job to find opinion leaders in the network. Based on the perspective of communication, this paper constructs a social network based on the probability of communication, and builds a PCA-SNA recognition model of opinion leaders combined with social network analysis. Empirical studies show that the model is valid on opinion leader discovery in SNS, and promisingly to be generalized and popularized.

4 citations


Cites methods from "Identification of topic-specific Op..."

  • ...Using the SPEAR algorithm in the classification of mail messages, Shinde constructed an opinion leader identification model based on the value of information, and find that opinion leaders did not behave exactly the same in different topics [8]....

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Journal ArticleDOI
TL;DR: In this article , a comprehensive review of existing techniques used to infer the topic-based influential users in online social networks is presented, including data gathering, construction of influence network, quantifying the influence between two users and analyzing the impact of the detected influential user.
Abstract: Abstract Online Social networks have become an easy means of communication for users to share their opinion on various topics, including breaking news, public events, and products. The content posted by a user can influence or affect other users, and the users who could influence or affect a high number of users are called influential users. Identifying such influential users has a wide range of applications in the field of marketing, including product advertisement, recommendation, and brand evaluation. However, the users’ influence varies in different topics, and hence a tremendous interest has been shown towards identifying topic-based influential users over the past few years. Topic-level information in the content posted by the users can be used in various stages of the topic-based influential user detection (IUD) problem, including data gathering, construction of influence network, quantifying the influence between two users, and analyzing the impact of the detected influential user. This has opened up a wide range of opportunities to utilize the existing techniques to model and analyze the topic-level influence in online social networks. In this paper, we perform a comprehensive study of existing techniques used to infer the topic-based influential users in online social networks. We present a detailed review of these approaches in a taxonomy while highlighting the challenges and limitations associated with each technique. Moreover, we perform a detailed study of different evaluation techniques used in the literature to overcome the challenges that arise in evaluating topic-based IUD approaches. Furthermore, closely related research topics and open research questions in topic-based IUD are discussed to provide a deep understanding of the literature and future directions.

4 citations

Book ChapterDOI
27 Jun 2019
TL;DR: In this paper, the authors proposed an integrated method by looking at not only essential indicators of reviewers but also the review characteristics, which can scientifically and effectively identify the opinion leaders and analyze the influence of opinion leaders.
Abstract: Opinion leaders are attracting increasing attention on practitioners and academics. Opinion leaders’ online Word-of-Mouth (WOM) plays a guiding and decisive role in reducing risks and uncertainty faced by users in online shopping. It is of great significance of businesses and enterprises to effectively identify opinion leaders. This study proposes an integrated method by looking at not only essential indicators of reviewers but also the review characteristics. The RFM model is used to evaluate the activity of reviewers. Four variables L (text length), T (period time), P (with or without a picture) and S (sentiment intensity) are derived to measure review helpfulness from review text. And two effective networks are built using the Artificial Neural Network (ANN). This study utilizes a real-life data set from Dianping.com for analysis and designs three different experiments to verify the identification effect. The results show that this method can scientifically and effectively identify the opinion leaders and analyze the influence of opinion leaders.

1 citations

References
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Proceedings Article
16 May 2010
TL;DR: An in-depth comparison of three measures of influence, using a large amount of data collected from Twitter, is presented, suggesting that topological measures such as indegree alone reveals very little about the influence of a user.
Abstract: Directed links in social media could represent anything from intimate friendships to common interests, or even a passion for breaking news or celebrity gossip. Such directed links determine the flow of information and hence indicate a user's influence on others — a concept that is crucial in sociology and viral marketing. In this paper, using a large amount of data collected from Twitter, we present an in-depth comparison of three measures of influence: indegree, retweets, and mentions. Based on these measures, we investigate the dynamics of user influence across topics and time. We make several interesting observations. First, popular users who have high indegree are not necessarily influential in terms of spawning retweets or mentions. Second, most influential users can hold significant influence over a variety of topics. Third, influence is not gained spontaneously or accidentally, but through concerted effort such as limiting tweets to a single topic. We believe that these findings provide new insights for viral marketing and suggest that topological measures such as indegree alone reveals very little about the influence of a user.

3,041 citations

Journal ArticleDOI
01 Feb 2013
TL;DR: A novel expert finding algorithm, ExpertRank, is proposed that evaluates expertise based on both document-based relevance and one's authority in his or her knowledge community and modified the PageRank algorithm to evaluate one's Authority so that it reduces the effect of certain biasing communication behavior in online communities.
Abstract: With increasing knowledge demands and limited availability of expertise and resources within organizations, professionals often rely on external sources when seeking knowledge. Online knowledge communities are Internet based virtual communities that specialize in knowledge seeking and sharing. They provide a virtual media environment where individuals with common interests seek and share knowledge across time and space. A large online community may have millions of participants who have accrued a large knowledge repository with millions of text documents. However, due to the low information quality of user-generated content, it is very challenging to develop an effective knowledge management system for facilitating knowledge seeking and sharing in online communities. Knowledge management literature suggests that effective knowledge management should make accessible not only written knowledge but also experts who are a source of information and can perform a given organizational or social function. Existing expert finding systems evaluate one's expertise based on either the contents of authored documents or one's social status within his or her knowledge community. However, very few studies consider both indicators collectively. In addition, very few studies focus on virtual communities where information quality is often poorer than that in organizational knowledge repositories. In this study we propose a novel expert finding algorithm, ExpertRank, that evaluates expertise based on both document-based relevance and one's authority in his or her knowledge community. We modify the PageRank algorithm to evaluate one's authority so that it reduces the effect of certain biasing communication behavior in online communities. We explore three different expert ranking strategies that combine document-based relevance and authority: linear combination, cascade ranking, and multiplication scaling. We evaluate ExpertRank using a popular online knowledge community. Experiments show that the proposed algorithm achieves the best performance when both document-based relevance and authority are considered.

186 citations

Proceedings ArticleDOI
06 Mar 2009
TL;DR: A survey of page ranking algorithms and comparison of some important algorithms in context of performance has been carried out.
Abstract: Web mining is an active research area in present scenario. Web Mining is defined as the application of data mining techniques on the World Wide Web to find hidden information, This hidden information i. e. knowledge could be contained in content of web pages or in link structure of WWW or in web server logs. Based upon the type of knowledge, web mining is usually divided in three categories: web content mining, web structure mining and web usage mining. An application of web mining can be seen in the case of search engines. Most of the search engines are ranking their search results in response to users' queries to make their search navigation easier. In this paper, a survey of page ranking algorithms and comparison of some important algorithms in context of performance has been carried out.

151 citations

Journal ArticleDOI
TL;DR: This paper proposes an improved mix framework for opinion leader identification in online learning communities based on four distinguishing features: expertise, novelty, influence, and activity and shows that experimental study on real datasets has shown that the framework effectively identifies opinion leaders in onlinelearning communities.
Abstract: With the widespread adoption of social media, online learning communities are perceived as a network of knowledge comprised of interconnected individuals with varying roles. Opinion leaders are important in social networks because of their ability to influence the attitudes and behaviours of others via their superior status, education, and social prestige. Many theories have been put forward to explain the formation, characteristics, and durability of social networks, but few address the issue of opinion leader identification. This paper proposes an improved mix framework for opinion leader identification in online learning communities. The framework is validated by an experimental study. By analysing textual content, user behaviour and time, this study ranked opinion leaders based on four distinguishing features: expertise, novelty, influence, and activity. Furthermore, the performances of opinion leaders were further investigated in terms of longevity and centrality. Experimental study on real datasets has shown that our framework effectively identifies opinion leaders in online learning communities.

110 citations


"Identification of topic-specific Op..." refers background in this paper

  • ...Yanyan Li, Shaoqian Ma, Yonghuai Huang, kinshuk [5] described four features for ranking leaders i....

    [...]

Journal ArticleDOI
TL;DR: In this article, the authors introduce how to use the social network analysis method and the UCINET software, together with traditional observations and investigations, to analyze and identify the opinion leaders in virtual communities by case study.
Abstract: Virtual communities are the uppermost communication spaces and channels for online word-of-mouth. And opinion leaders are the most important group for enterprises’ word-of-mouth communication. As enterprises are engaged in online word-of-mouth marketing activities, the key is to find out the opinion leaders in virtual communities. In this paper, after affirming the effects of opinion leaders and reviewing and summarizing the former ways of finding out opinion leaders, authors will introduce to us how to use the social network analysis method and the UCINET software, together with traditional observations and investigations, to analyze and identify the opinion leaders in virtual communities by case study.

41 citations