Opinion Leaders In Social Networks Research Articles

Recognition of opinion leaders in blockchain-based social networks by structural information and content contribution.

Exploring the influence of social network users in the blockchain environment and identifying opinion leaders can help understand the information dissemination characteristics of blockchain social networks, direct the discovery of quality content, and avoid the spread of rumors. Members of blockchain-based social networks are given new responsibilities by token awards and consensus voting, which alters how users connect to the network and engage with one another. Based on blockchain theory and the relevant theories of opinion leaders in social networks, this article combines structural information and content contributions to identify opinion leaders. Firstly, user influence indicators are defined from the perspective of network structure and behavioral characteristics of user contributions. Then, ECWM is constructed, which combines the entropy weight method and the criteria importance through intercriteria correlation (CRITIC) weighting method to address the correlation and diversity among indicators. Furthermore, an improved Technique for Order Preference by Similarity to Ideal Solution (TOPSIS), called ECWM-TOPSIS, is proposed to identify opinion leaders in blockchain social networks. Moreover, to verify the effectiveness of the method, we conducted a comparative analysis of the proposed algorithm on the blockchain social platform Steemit by using two different methods (voting score and forwarding rate). The results show that ECWM-TOPSIS produces significantly higher performance than other methods for all selected top N opinion leaders.

Accelerating new product diffusion: How lead users serve as opinion leaders in social networks

In social networks or, more specifically, online communities on tech-products, opinion leaders are important sources of advice for other consumers in the adoption and diffusion of new products. However, possibilities for potential users to exert their influence on opinion leadership are ignored. This study determines whether and how lead users may serve as opinion leaders in social networks and advise other consumers in the adoption and diffusion of new products. Our survey with 308 users in the Xiaomi and Huawei communities suggests that higher lead userness is positively and significantly associated with the likelihood of opinion giving and passing. Product-possessing innovativeness has a higher impact compared with information-possessing innovativeness. Product involvement does not enhance the effect of information-possessing innovativeness. The findings provide a better understanding of the formation of opinion leadership in social networks for an accelerated diffusion of new products.

Recognition of Opinion Leaders in Social Networks Using Text Posts’ Trajectory Scoring and Users’ Comments Sentiment Analysis

Recognition of Opinion Leaders in Social Networks Using Text Posts’ Trajectory Scoring and Users’ Comments Sentiment Analysis

Learning to rank complex network node based on the self-supervised graph convolution model

Node ranking in complex networks is of great value in many fields, such as identifying opinion leaders in social networks and risky institutions in financial networks. However, traditional ranking methods are built on heuristic rules and may be effective in certain datasets and fail in others. To address the issues, we formulate node ranking on complex networks as a learning to rank problem, and propose a novel model based on self-supervised learning and graph convolution model to rank nodes based on integrated information from node features and network structure. (1) We develop a self-supervised pretext task to extract information about node location and global topology from complex networks. (2) To train the model more efficiently, multi-task learning is adopted, which includes ranking task and regression task besides the self-supervised pretext task. Our model works well with only a small number of nodes that have ranking labels. Comprehensive experiments on different datasets demonstrate that it outperforms the existing state-of-the-art methods, which verify the effectiveness and robustness of the proposed approach.

Two sides of the same leader: an agent-based model to analyze the effect of ambivalent opinion leaders in social networks

Opinion leaders (OLs) are becoming increasingly relevant on social networking sites as their visibility can help to shape their followers’ attitudes toward a variety of issues. While earlier research provided initial evidence on the effect of OLs using agent-based modeling, it remains unclear how OLs affect their network environment and, therefore, the opinion climate when: (a) they publicly hold ambivalent attitudes, and (b) they not only express support for their own stance but also discredit or ‘debunk’ the opposing side. This paper presents an agent-based model that determines the influence of OLs in social networks in relation to ambivalence and discreditation. The model draws on theoretical foundations of OLs as well as attitudinal ambivalence and was implemented using two network topologies. Results indicate that OLs have significant influence on the opinion climate and that an unequal number of OLs of different opinion camps lead to an imbalance in the opinion climate only in certain situations. Furthermore, OLs can dominate the opinion climate and turn their stance into a majority opinion more effectively when discrediting the opposing side. Ambivalent OLs, on the other hand, can contribute to greater balance in the opinion climate. These findings provide a more nuanced analysis of OLs in social networks by pointing to potential amplifications as well as boundaries of their influence. Implications are discussed with a focus on human and artificial key actors in online networks and their efficacy therein.

From past to present: Spam detection and identifying opinion leaders in social networks

From past to present: Spam detection and identifying opinion leaders in social networks

Influence identification of opinion leaders in social networks: an agent-based simulation on competing advertisements

In social networks, factors that influence the spread of information are essential for companies to comprehend. This study uses the opinion dynamic theory to investigate the influence of multiple advertisement opinion leaders in social networks. We construct an integrated bounded confidence model to simulate the evolution of followers’ opinions under two advertisement opinion leaders. Through experimental simulation, we found that the weight of influence on advertisements has a dual effect on the evolution of followers’ opinions, and the probability that information is transmitted by opinion leaders has a significant impact on the evolution of collective opinions. The results show that, for competitive products, companies should properly understand the propaganda power of product advertisements and improve the probability of information being successfully transmitted by opinion leaders.

Identifying Topic-based Opinion Leaders in Social Networks by Content and User Information

Social media is like a revolution since it has changed many things in people’s lifestyles by bringing new trends in communication, shopping, working…etc. In microblogging sites of the social media, more and more users meet every day, a wide range of ideas are quickly emerging, spreading, and creating interactive environments. In this context, social media can be seen as one of the most important sources of information affecting public opinion. Being inspired by the importance of social media, we propose Opinion Leader Detection (OLED) system in this paper. OLED has two main parts. A network was created by collecting users' information and their tweets shared within a specific time. In the first part, the tweets were labeled with their categories by semantic kernels for topic-based analysis. After the category information is obtained, the second part is attempt to detect whether the users are opinion leaders in their category. Then, the leadership scores were calculated with the formula generated and opinion leaders were determined in each category. We performed our experiments on a data collection gathered from Twitter that includes 17,234,924 tweets and 38,727 users. According to topic modeling and user modeling results, we give leadership scores to each user in the network. Users with highest scores are stated as opinion leaders. In order to evaluate OLED’s performance we also run PageRank algorithm on the same dataset. The experimental results show that our framework OLED generates remarkable performance in compare to PageRank algorithm nearly in all topics and all selected topN opinion leaders.

Effective Communication of Ukrainian Medical Opinion Leaders in Social Network

Effective Communication of Ukrainian Medical Opinion Leaders in Social Network

An Opinion-Leader Mining Method in Social Networks With a Phased-Clustering Perspective

Opinion-leader mining in social networks is a critical problem in research of the information dissemination process and in public opinion guidance and supervision. Not every social network user has a high probability to be an opinion leader. However, most mining methods identify opinion leaders among users in the whole network, which adds unnecessary calculations. To solve this problem, we propose a rank after clustering (RaC) algorithm to mine opinion leaders in social networks with a phased-clustering perspective, which has the following aspects: (1) Aiming to reduce the scale of calculation, the clustering stage clusters users in social networks using a K-means algorithm according to topological information to find the set of opinion leader candidates; (2) The ranking stage determines the user ranks of opinion leader candidates by both their activeness and influence, and we accumulate the followers' influence weighted by degree of attention when assessing user influence. In experiments, a new indicator, the C-value, and simulations based on the linear threshold model are used to evaluate the performance of the RaC algorithm. The results show that RaC is effective and accurate.

Research on the discovery of opinion leaders in social networks

Since entering the era of Wed 2.0, social networks have been an indispensable part of human life. At present, these social networks are the main source of information dissemination and alienation, and they play a key role in the process of dissemination and are the central users of social networks. Central users have an incredible ability to communicate in their community, and their knowledge and experience of things have a great impact on other users in the community. This research mainly uses the analysis method of social network to analyze the community and find the central users in the community. We use Louvain algorithm to identify the community structure in social networks, and on this basis we use PageRank algorithm to analyze the central users in each community.

On the Algorithms of Identifying Opinion Leaders in Social Networks

The present paper is dedicated to the algorithms of opinion leaders’ identification in social networks on the basis of social network theory and machine learning methods. The main applications of such algorithms for social network analysis are discussed, examples of existing opinion leaders search algorithms and their advantages and disadvantages are given. A modified algorithm for finding the most influential social network nodes, which takes into account not only the position of the node in the network, but also signs of its activity, such as the number of posts, the number of comments and the number of reposts is proposed. The suggested algorithm is more accurate than the algorithms, which are based only on the position of the node in the network. Limitations of using the algorithms as well as possible problems with machine learning application in the field of social network analysis are described.

A novel algorithm for mining opinion leaders in social networks

Recently, social network analysis (SNA) has attracted researchers’ attention due to its practicability and popularity. Several mining techniques have been developed for extracting useful knowledge from users’ regularities. Opinion leader discovery is one essential task which has great commercial and political values. By identifying the opinion leaders, companies or governments could manipulate the selling or guiding public opinion, respectively. Additionally, detecting the influential comments is able to understand the source and trend of public opinion formation. However, mining opinion leaders in a huge social network is a challenge task because of the complexity of graph processing and leadership analysis. In this study, a novel algorithm, OLMiner, is proposed to efficiently find the opinion leaders from a social network. OLMiner utilizes a community detection method to tackle the influence overlapping issue and shrink the size of candidate generation. Then, we propose a novel clustering-based leadership analysis to find out the opinion leader in a social network. The experimental study shows that the proposed algorithm can effectively discover the influential opinion leaders in different real datasets with efficiency and has graceful scalability.

Identifying opinion leaders in social networks with topic limitation

The social networks have been an important platform for people to share and exchange information in their daily life. There are some most critical users called opinion leaders who are always used to achieve the maximization of information transmission and suppress the diffusion of rumours in a short time. Many methods have been proposed by researches for these users. For identifying more accurately and efficiently, we make a further analysis for the real information spread and find that the information is commonly topic sensitive and opinion leaders are always topic limited. In the certain topic area, the users with higher authority in the topic area always play a more crucial role for the information spread. What’s more, in order to quantify the authority in certain topic area, we apply a series of rigorous definitions and topic model. Finally, comparing with the other widely used methods, the result shows the effective performance of our method.

The Finding and Dynamic Detection of Opinion Leaders in Social Network

It is valuable for the real world to find the opinion leaders. Because different data sources usually have different characteristics, there does not exist a standard algorithm to find and detect the opinion leaders in different data sources. Every data source has its own structural characteristics, and also has its own detection algorithm to find the opinion leaders. Experimental results show the opinion leaders and theirs characteristics can be found among the comments from the Weibo social network of China, which is like Facebook or Twitter in USA.

The Emergence of Opinion Leaders in Social Networks

A major challenge in many word-of-mouth marketing campaigns is the cost-effective identification of opinion leaders, consumers who exert disproportionate influence on the purchase decisions of other consumers. However, empirical studies agree on few consumer characteristics that are strong predictors of opinion leadership. In the present research, we attempt to explain these null or weak findings through a game-theoretic perspective on opinion leader-follower relationships. Our model combines social network, word-of-mouth communication, consumer heterogeneity, and time preference in a setting in which each consumer makes once-in-a-lifetime choice between a new product and an outside option. We find that: (a) opinion leaders emerge in equilibrium whenever certain general conditions are met; (b) counter-intuitively, the most patient consumer with the highest time discount factor may become an opinion leader; and (c) equilibria in which opinion leaders are highly-connected and/or have low time discount factors may be neither efficient to consumers nor profit-maximizing to firms, even though they are likely to be focal outcomes.