Identification of influential users by neighbors in online social networks

2017 ◽  
Vol 486 ◽  
pp. 517-534 ◽  
Author(s):  
Amir Sheikhahmadi ◽  
Mohammad Ali Nematbakhsh ◽  
Ahmad Zareie
Keyword(s):  
Social Networks ◽  
Online Social Networks ◽  
Influential Users

scholarly journals How did Ebola Information Spread on Twitter: Broadcasting or Viral Spreading? (Preprint)

2018 ◽  
Author(s):  
Hai Liang ◽  
Isaac Chun-Hai Fung ◽  
Zion Tsz Ho Tse ◽  
Jingjing Yin ◽  
Chung-Hong Chan ◽  
...  
Keyword(s):  
Public Health ◽  
Social Networks ◽  
Health Information ◽  
Online Social Networks ◽  
Information Diffusion ◽  
Cost Effective ◽  
Communication Strategy ◽  
Public Health Information ◽  
Influential Users ◽  
Public Health Promotion

BACKGROUND It has been argued that information and emotions towards public health issues could spread widely through online social networks. Although aggregate metrics on the volume of information diffusion are available, we know little about how information spreads on online social networks. For example, health information could be transmitted from one to many (i.e. broadcasting), which is similar to how traditional mass media passes information to the general public. Health information could also be transmitted from many to many (i.e. viral spreading), which is analogous to the spread of infectious diseases. OBJECTIVE The aim of this study is to determine the spreading pattern of Ebola information on Twitter and identify influential users regarding Ebola messages. On Twitter, influential users are those whose tweets receive a large number of retweets. METHODS Our data was purchased from GNIP, the official Twitter data provider. We obtained all Ebola-related tweets (including retweets and replies) posted from March 23, 2014 to May 31, 2015. We reconstructed Ebola-related retweeting paths based on Twitter content and the follower-followee relationships (who follows whom on Twitter). Social network analysis was performed to investigate retweeting patterns. In addition to describing the diffusion structures, we classify users in the network into four categories (i.e., influential user, hidden influential user, disseminator, common user) based on following and retweeting patterns. Disseminators received fewer retweets than expected based on their number of followers, common users and influential users received as many or fewer retweets than expected, and hidden influential users received more retweets than expected. RESULTS On average, 91% of the retweets were directly retweeted from the initial message. Moreover, 47.5% of the retweeting paths of the original tweets had a depth of 1 (i.e., from the seed user to its immediate followers). These observations suggested that the broadcast model was more pervasive than viral spreading. Furthermore, we found that influential users and hidden influential users can trigger more retweets than disseminators and common users. Disseminators and common users relied more on the viral model for spreading information beyond their immediate followers via influential and hidden influential users. CONCLUSIONS The broadcast model was the dominant mechanism of information diffusion of a major health event on Twitter. It suggests that public health communicators can work with influential and hidden influential users to get the message across, because influential and hidden influential users can reach more people that are not following the public health Twitter accounts. Although both influential users and hidden influential users can trigger a lot of retweets, recognizing and using the hidden influential users as the source of information could potentially be a cost-effective communication strategy for public health promotion, because the hidden influential users can receive more retweets than expected based on their limited number of followers. However, challenges remain due to uncertain credibility of these hidden influential users.

Identification of Influential Online Social Network Users Based on Multi-Features

2016 ◽  
Vol 30 (06) ◽  
pp. 1659015 ◽  
Author(s):  
Qindong Sun ◽  
Nan Wang ◽  
Yadong Zhou ◽  
Zuomin Luo
Keyword(s):  
Social Networks ◽  
Online Social Networks ◽  
Online Social Network ◽  
Real Data ◽  
User Profiles ◽  
Sina Weibo ◽  
Pagerank Algorithm ◽  
Model Based ◽  
User Influence ◽  
Influential Users

The problem of discovering influential users is important to understand and analyze online social networks. The user profiles and interactions between users are significant features to evaluate the user influence. As these features are heterogeneous, it is challengeable to take all of them into a proper model for influence evaluation. In this paper, we propose a model based on personal user features and the adjacent factor to discover influential users in online social networks. Through taking the advantages of Bayesian network and chain principle of PageRank algorithm, the features of the user profiles and interactions are integratedly considered in our model. Based on real data from Sina Weibo data and multiple evaluation metrics of retweet count, tweet count, follower count, etc., the experimental results show that influential users identified by our model are more powerful than the ones identified by single indicator methods and PageRank-based methods.

scholarly journals Cross-site Prediction on Social Influence for Cold-start Users in Online Social Networks

2021 ◽  
Vol 15 (2) ◽  
pp. 1-23
Author(s):  
Qingyuan Gong ◽  
Yang Chen ◽  
Xinlei He ◽  
Yu Xiao ◽  
Pan Hui ◽  
...  
Keyword(s):  
Social Networks ◽  
Social Influence ◽  
Online Social Networks ◽  
Cold Start ◽  
Learning Technologies ◽  
Viral Marketing ◽  
Supervised Machine Learning ◽  
Activity Data ◽  
Public Data ◽  
Influential Users

Online social networks (OSNs) have become a commodity in our daily life. As an important concept in sociology and viral marketing, the study of social influence has received a lot of attentions in academia. Most of the existing proposals work well on dominant OSNs, such as Twitter, since these sites are mature and many users have generated a large amount of data for the calculation of social influence. Unfortunately, cold-start users on emerging OSNs generate much less activity data, which makes it challenging to identify potential influential users among them. In this work, we propose a practical solution to predict whether a cold-start user will become an influential user on an emerging OSN, by opportunistically leveraging the user’s information on dominant OSNs. A supervised machine learning-based approach is adopted, transferring the knowledge of both the descriptive information and dynamic activities on dominant OSNs. Descriptive features are extracted from the public data on a user’s homepage. In particular, to extract useful information from the fine-grained dynamic activities that cannot be represented by the statistical indices, we use deep learning technologies to deal with the sequential activity data. Using the real data of millions of users collected from Twitter (a dominant OSN) and Medium (an emerging OSN), we evaluate the performance of our proposed framework to predict prospective influential users. Our system achieves a high prediction performance based on different social influence definitions.

scholarly journals Exploring the construction and infiltration strategies of social bots in sina microblog

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Wenxian Wang ◽  
Xingshu Chen ◽  
Shuyu Jiang ◽  
Haizhou Wang ◽  
Mingyong Yin ◽  
...  
Keyword(s):  
Social Networks ◽  
Survival Rate ◽  
Data Collection ◽  
Online Social Networks ◽  
Defense Mechanism ◽  
Construction Technology ◽  
Critical Technology ◽  
Current Events ◽  
Influential Users

AbstractNowadays, millions of people use Online Social Networks (OSNs) like Twitter, Facebook and Sina Microblog, to express opinions on current events. The widespread use of these OSNs has also led to the emergence of social bots. What is more, the existence of social bots is so powerful that some of them can turn into influential users. In this paper, we studied the automated construction technology and infiltration strategies of social bots in Sina Microblog, aiming at building friendly and influential social bots to resist malicious interpretations. Firstly, we studied the critical technology of Sina Microblog data collection, which indicates that the defense mechanism of that is vulnerable. Then, we constructed 96 social bots in Sina Microblog and researched the influence of different infiltration strategies, like different attribute settings and various types of interactions. Finally, our social bots gained 5546 followers in the 42-day infiltration period with a 100% survival rate. The results show that the infiltration strategies we proposed are effective and can help social bots escape detection of Sina Microblog defense mechanism as well. The study in this paper sounds an alarm for Sina Microblog defense mechanism and provides a valuable reference for social bots detection.

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