Experts Ranking in Online Communities Using Combination of Text and Link Analysis (original) (raw)
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A novel method for expert finding in online communities based on concept map and PageRank
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An online community is a virtual community where people can express their opinions and their knowledge freely. There are a great deal of information in online communities, however there is no way to determine its authenticity. Thus the knowledge which has been shared in online communities is not reliable. By determining expertise level of users and finding experts in online communities the accuracy of posted comments can be evaluated.In this study, a hybrid method for expert finding in online communities is presented which is based on content analysis and social network analysis. The content analysis is based on concept map and the social network analysis is based on PageRank algorithm. To evaluate the proposed method java online community was selected and then correlation between our results and scores prepared by java online community was calculated. Based on obtained results Spearman correlation for 11 subcategories of java online community using this method is 0.904, which is hi...
Expert Ranking using Reputation and Answer Quality of Co-existing Users
Online discussion forums provide knowledge sharing facilities to online communities. Usage of online discussion forums has increased tremendously due to the variety of services and their ability of common users to ask question and provide answers. With the passage of time, these forums can accumulate huge contents. Some of these posted discussions may not contain quality contents and may reflect users' personal opinions about topic which may contradict with a relevant answer. These low quality discussions indicate the existence of unprofessional users. Therefore, it is imperative to rank an expert in online forums. Most of the existing expert-ranking techniques consider only user's social network authority and content relevancy features as parameters of evaluating user expertise. But user reputation as a group member of thread repliers is not considered. In this context a novel solution of expert ranking in online discussion forums is proposed. We proposed two expert ranking techniques: The first technique is based on user and their co-existing user's reputation in different threaded discussions, and the second technique is based on user answers' quality and their category specialty features. Furthermore, we extended a technique expertise rank with our proposed features sets. The experimental study based on real dataset shows that the following proposed techniques perform better than existing techniques.
Context based Expert Finding in Online Communities using Social Network Analysis
Online communities are the most popular interactive environments on the Internet, which provide users with a platform to share their knowledge and expertise. The most important use of online communities in cyberspace is sharing knowledge. These communities are a great place to ask questions and find answers. The important challenges of these communities are the large volume of information and the lack of a method to determine their validity as well as expert finding which attracted a lot of attention in both industry and academia in. Therefore, identifying persons with relevant knowledge on a given topic and ranking them according to their expertise score can help to calculate the accuracy of the comments submitted on the internet. In this research, a model for finding experts and determining their domain expertise level by the aid of statistical calculations and the ant colony algorithm in the MetaFilter online community was presented. The WordNet Dictionary was used to determine the relevance of the user's questions with the intended domain. The proposed algorithm determines the level of people's expertise in the intended field by using the pheromone section of the Ant colony algorithm, which is based on the similarity of the questions sent by the users and the shared knowledge of the users from their interactions in the online community.
Information Technology and Management, 2014
Currently, online forums have become one of the most popular collaborative tools on the Internet where people are free to express their opinions. Forums supply facilities for knowledge management in which, their members can share their knowledge with each other. In this regard, The main problem regarding to the knowledge sharing on forums is the extensive amount of data on them without any mechanism to determine their validity. So, for knowledge seekers, knowing the expertise level of each member in a specific context is important in order to find valid answers. In this research, a novel algorithm is proposed to determine people's expertise level based on the context. AskMe forum is chosen for the evaluation process of the proposed method and its data has been processed in several stages. First of all, a special crawling program is developed to gather data from AskMe forum. Then, raw data is extracted, transformed, and loaded into a designed database using SQL server integration services. Afterwards, people's expertise level for specified context is calculated by applying the proposed method on the processed data. Finally, evaluation tests are applied in order to calculate the accuracy of the proposed method and compare it with other methods.
International Journal of Scientific Research in Science, Engineering and Technology, 2021
Question and Answer forums play an important role in our daily lives of sharing information and knowledge. Users post questions and then select questions to answer in the system. Due to the rapidly growing number of users and the number of questions, it is unlikely that the user will accidentally trip over the question to answer. high quality responses during a short wait. The main purpose of this paper is to improve the effectiveness of Q&A systems by sending queries to competent and willing users to answer questions. To date, we have developed and implemented Q&A .Question and Answer Forums (QAF) are important platforms for disseminating informal information and play an important role in problem solving and learning. Expert identification is still limited and links analysis methods do not take into account the size of the community.
Expertise networks in online communities
Proceedings of the 16th international conference on World Wide Web, 2007
Web-based communities have become important places for people to seek and share expertise. We find that networks in these communities typically differ in their topology from other online networks such as the World Wide Web. Systems targeted to augment web-based communities by automatically identifying users with expertise, for example, need to adapt to the underlying interaction dynamics. In this study, we analyze the Java Forum, a large online help-seeking community, using social network analysis methods. We test a set of network-based ranking algorithms, including PageRank and HITS, on this large size social network in order to identify users with high expertise. We then use simulations to identify a small number of simple simulation rules governing the question-answer dynamic in the network. These simple rules not only replicate the structural characteristics and algorithm performance on the empirically observed Java Forum, but also allow us to evaluate how other algorithms may perform in communities with different characteristics. We believe this approach will be fruitful for practical algorithm design and implementation for online expertise-sharing communities.
The expert identification in the cyber knowledge community
International Journal of Computational Science and Engineering, 2016
The cyber community has played an important role for knowledge sharing on the internet. By posting articles and comments in the cyber community, the members can share knowledge with each other conveniently. In the community, the members would like to ask someone to solve some specific problems. It would be valuable if the community platform could identify who the real expert is in a specific domain. In this research, we propose a volumetric ExpertRank algorithm to identify the real expert in the cyber community automatically. The algorithm is designed based on the articles' evaluation information, word-count of articles and PageRank algorithm. We have developed a prototype system and conducted an experiment to evaluate our algorithm. The experiment results show that our algorithm is better than the other automatic expert identification methods and it is helpful to identify experts accurately in the cyber community.
A novel method based on concept map for expert finding in online communities
Nowadays, online communities or virtual communities on the Internet are one of the most interactive environment in which people can express their opinions freely. There is too amount of information shared in online communities and there is no way to determine its authenticity, this is the most important challenge in the field of knowledge sharing in online communities. Expert finding in the online communities and determine their level of knowledge, can be used to determine the accuracy of posted comments. In this paper a new method for expert finding in online communities based on concept mapping, is presented. In the proposed method, two measures are used. One measure is distance between concepts of user response and concepts of question. Other measure is the number of concepts that are used in the user response. Proposed method is implemented and evaluated on Java Online Communities, and the results showed that the correlation exceeds 0.9.
Integrated Expert Recommendation Model for Online Communities
International journal of Web & Semantic Technology, 2013
Online communities have become vital places for Web 2.0 users to share knowledge and experiences. Recently, finding expertise user in community has become an important research issue. This paper proposes a novel cascaded model for expert recommendation using aggregated knowledge extracted from enormous contents and social network features. Vector space model is used to compute the relevance of published content with respect to a specific query while PageRank algorithm is applied to rank candidate experts. The experimental results show that the proposed model is an effective recommendation which can guarantee that the most candidate experts are both highly relevant to the specific queries and highly influential in corresponding areas.
Expert Finding on Social Network with Link Analysis Approach
With the appearance of social networks in the Internet, the communications between people took a new form. Nowadays, lots of people with different goals are registered in social networks and do wide range of activities. One of the most important feature of social networks is knowledge sharing. The main problem regarding to this issue is a wide range of shared knowledge and there is no mechanism to determine their validity. So, the knowledge shared on social networks could not be trusted. By finding experts in social networks and determining their level of knowledge, the validity of their posts could be determined. Therefore a solution to the mentioned problem is to provide a method for expert finding. In this research a novel model based on social network analysis is proposed to find the experts who are the members of social networks by means of business intelligence approach. This model is verified by real data from Friendfeed social network. First, data is extracted, transformed and loaded to data warehouse with ETL processes. Then a new ranking algorithm is proposed for finding the experts, and finally the obtained results are compared to the experts' opinions utilizing spearman's correlation function.