An ISmSnR Marketing Information Spreading Model in Online Social Networks Considering Spammer Existence

2016 ◽  
Author(s):  
Special Issues Editor
Keyword(s):  
Social Networks ◽  
Online Social Networks ◽  
Spreading Model ◽  
Information Spreading ◽  
Marketing Information

Mining Information Spreading Based on Users' Retweet Behavior in Twitter

2013 ◽  
Vol 380-384 ◽  
pp. 2866-2870 ◽  
Author(s):  
Rong Ze Xia ◽  
Yan Jia ◽  
Wang Qun Lin ◽  
Hu Li
Keyword(s):  
Social Networks ◽  
Power Law ◽  
Power Law Distribution ◽  
Spreading Process ◽  
Spreading Model ◽  
The World ◽  
Information Spread ◽  
Information Spreading

Twitter is one of the largest social networks in the world. People could share contents on it. When we interact with each other, the information spreads. And its users retweet behavior that makes information spread so fast. So there comes an important question: Whats about users retweet behavior? Could we simulate information spreading in twitter by retweeting behavior? We crawl twitter and mine information spreading based on users retweet behavior in it. Through our dateset, we verify the power-law distribution of the retweet-width and retweet-depth. At the same time, we study the correlation between retweet-width and retweet-depth. Finally, we propose an information spreading model to simulate the information spreading process in twitter and get a good result.

scholarly journals Topic Enhanced Sentiment Spreading Model in Social Networks Considering User Interest

2020 ◽  
Vol 34 (01) ◽  
pp. 989-996
Author(s):  
Xiaobao Wang ◽  
Di Jin ◽  
Katarzyna Musial ◽  
Jianwu Dang
Keyword(s):  
Social Networks ◽  
Online Social Networks ◽  
Emotional State ◽  
Underlying Mechanism ◽  
Alternative Methods ◽  
Behavior Changes ◽  
User Interest ◽  
Spreading Model ◽  
Proposed Model ◽  
Negative Sentiment

Emotion is a complex emotional state, which can affect our physiology and psychology and lead to behavior changes. The spreading process of emotions in the text-based social networks is referred to as sentiment spreading. In this paper, we study an interesting problem of sentiment spreading in social networks. In particular, by employing a text-based social network (Twitter) , we try to unveil the correlation between users' sentimental statuses and topic distributions embedded in the tweets, then to automatically learn the influence strength between linked users. Furthermore, we introduce user interest to refine the influence strength. We develop a unified probabilistic framework to formalize the problem into a topic-enhanced sentiment spreading model. The model can predict users' sentimental statuses based on their historical emotional status, topic distributions in tweets and social structures. Experiments on the Twitter dataset show that the proposed model significantly outperforms several alternative methods in predicting users' sentimental status. We also discover an intriguing phenomenon that positive and negative sentiment is more relevant to user interest than neutral ones. Our method offers a new opportunity to understand the underlying mechanism of sentimental spreading in online social networks.

The influence of positive news on rumor spreading in social networks with scale-free characteristics

2018 ◽  
Vol 29 (09) ◽  
pp. 1850078 ◽  
Author(s):  
Yongcong Luo ◽  
Jing Ma
Keyword(s):  
Social Networks ◽  
Online Social Networks ◽  
Transition Probability ◽  
Probability Method ◽  
Spreading Process ◽  
Rumor Spreading ◽  
Scale Free ◽  
Spreading Model ◽  
Text Model ◽  
The Impact

We explore the impact of positive news on rumor spreading in this paper. It is a fact that most of the rumors related to hot events or emergencies can be propagated rapidly on the hotbed of online social networks. In Chinese words, it is better to divert rather than block. Therefore, we propose the spreading model [Formula: see text] in which positive news is a good factor to guide rumor spreading. Based on transition probability method, we have got the spreading parameters of the [Formula: see text] model by running the rumor spreading process in online social networks with scale-free characteristics. The results give a good proof that improving the activity of the positive news spreader [Formula: see text] derived from the [Formula: see text] model can guide and restrain the spreading speed of rumor smoothly.

scholarly journals The Spreading of Information in Online Social Networks through Cellular Automata

Complexity ◽  
2018 ◽  
Vol 2018 ◽  
pp. 1-9 ◽  
Author(s):  
Yuda Wang ◽  
Gang Li
Keyword(s):  
Social Networks ◽  
Cellular Automata ◽  
Infection Rate ◽  
Online Social Networks ◽  
Information Dissemination ◽  
Epidemic Models ◽  
Computational Method ◽  
Online Information ◽  
Information Dynamics ◽  
Information Spreading

Epidemic dynamics in complex networks have been extensively studied. Due to the similarity between information and disease spreading, most studies on information dynamics use epidemic models and merely consider the characteristics of online social networks and individual’s cognitive. In this paper, we propose an online social networks information spreading (OSIS) model combining epidemic models and individual’s cognitive psychology. Then we design a cellular automata (CA) method to provide a computational method for OSIS. Finally, we use OSIS and CA to simulate the spreading and evolution of information in online social networks. The experimental results indicate that OSIS is effective. Firstly, individual’s cognition affects online information spreading. When infection rate is low, it prevents the spreading, whereas when infection rate is sufficiently high, it promotes transmission. Secondly, the explosion of online social network scale and the convenience of we-media greatly increase the ability of information dissemination. Lastly, the demise of information is affected by both time and heat decay rather than probability. We believe that these findings are in the right direction for perceiving information spreading in online social networks and useful for public management policymakers seeking to design efficient programs.

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