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.

scholarly journals Augmented Context-Based Conceptual User Modeling for Personalized Recommendation System in Online Social Networks

2020 ◽  
Vol 14 (3) ◽  
pp. 1-19
Author(s):  
Ammar Alnahhas ◽  
Bassel Alkhatib
Keyword(s):  
Social Networks ◽  
Online Social Networks ◽  
Recommendation System ◽  
Contextual Information ◽  
Detailed Comparison ◽  
Personalized Recommendation ◽  
User Models ◽  
Proposed Model ◽  
Recommendation Algorithms ◽  
Novel Method

As the data on the online social networks is getting larger, it is important to build personalized recommendation systems that recommend suitable content to users, there has been much research in this field that uses conceptual representations of text to match user models with best content. This article presents a novel method to build a user model that depends on conceptual representation of text by using ConceptNet concepts that exceed the named entities to include the common-sense meaning of words and phrases. The model includes the contextual information of concepts as well, the authors also show a novel method to exploit the semantic relations of the knowledge base to extend user models, the experiment shows that the proposed model and associated recommendation algorithms outperform all previous methods as a detailed comparison shows in this article.

scholarly journals Marketing and social networks: a criterion for detecting opinion leaders

2017 ◽  
Vol 26 (3) ◽  
pp. 347-366 ◽  
Author(s):  
Arnaldo Mario Litterio ◽  
Esteban Alberto Nantes ◽  
Juan Manuel Larrosa ◽  
Liliana Julia Gómez
Keyword(s):  
Social Networks ◽  
Social Network ◽  
Online Social Networks ◽  
Semantic Analysis ◽  
Point Of View ◽  
Eigenvector Centrality ◽  
Content Type ◽  
Sporting Event ◽  
Centrality Metrics ◽  
Proposed Model

Purpose The purpose of this paper is to use the practical application of tools provided by social network theory for the detection of potential influencers from the point of view of marketing within online communities. It proposes a method to detect significant actors based on centrality metrics. Design/methodology/approach A matrix is proposed for the classification of the individuals that integrate a social network based on the combination of eigenvector centrality and betweenness centrality. The model is tested on a Facebook fan page for a sporting event. NodeXL is used to extract and analyze information. Semantic analysis and agent-based simulation are used to test the model. Findings The proposed model is effective in detecting actors with the potential to efficiently spread a message in relation to the rest of the community, which is achieved from their position within the network. Social network analysis (SNA) and the proposed model, in particular, are useful to detect subgroups of components with particular characteristics that are not evident from other analysis methods. Originality/value This paper approaches the application of SNA to online social communities from an empirical and experimental perspective. Its originality lies in combining information from two individual metrics to understand the phenomenon of influence. Online social networks are gaining relevance and the literature that exists in relation to this subject is still fragmented and incipient. This paper contributes to a better understanding of this phenomenon of networks and the development of better tools to manage it through the proposal of a novel method.

Link prediction measures considering different neighbors’ effects and application in social networks

2017 ◽  
Vol 28 (03) ◽  
pp. 1750033 ◽  
Author(s):  
Peng Luo ◽  
Chong Wu ◽  
Yongli Li
Keyword(s):  
Social Networks ◽  
Online Social Networks ◽  
Link Prediction ◽  
Prediction Models ◽  
Network Evolution ◽  
Social Characteristics ◽  
Prediction Methods ◽  
Proposed Model ◽  
Social Network Evolution ◽  
Artificial Network

Link prediction measures have been attracted particular attention in the field of mathematical physics. In this paper, we consider the different effects of neighbors in link prediction and focus on four different situations: only consider the individual’s own effects; consider the effects of individual, neighbors and neighbors’ neighbors; consider the effects of individual, neighbors, neighbors’ neighbors, neighbors’ neighbors’ neighbors and neighbors’ neighbors’ neighbors’ neighbors; consider the whole network participants’ effects. Then, according to the four situations, we present our link prediction models which also take the effects of social characteristics into consideration. An artificial network is adopted to illustrate the parameter estimation based on logistic regression. Furthermore, we compare our methods with the some other link prediction methods (LPMs) to examine the validity of our proposed model in online social networks. The results show the superior of our proposed link prediction methods compared with others. In the application part, our models are applied to study the social network evolution and used to recommend friends and cooperators in social networks.

scholarly journals Modeling the Information Diffusion of Overlapped Nodes using SFA-ICBDM

2019 ◽  
Vol 8 (2) ◽  
pp. 709-713
Keyword(s):  
Social Networks ◽  
Online Social Networks ◽  
Information Diffusion ◽  
Cascade Model ◽  
Recent Time ◽  
Rumor Spreading ◽  
Overlapping Communities ◽  
Network Diversity ◽  
Proposed Model ◽  
Almost All

:In recent time, online social networks like, Facebook, Twitter, and other platforms, provide functionality that allows a chunk of information migrates from one user to another over a network. Almost all the actual networks exhibit the concept of community structure. Indeed overlapping communities are very common in a complex network such as online social networks since nodes could belong to multiple communities at once. The huge size of the real-world network, diversity in users profiles and, the uncertainty in their behaviors have made modeling the information diffusion in such networks to become more and more complex and tend to be less accurate. This work pays much attention on how we can accurately predicting information diffusion cascades over social networks taking into account the role played by the overlapping nodes in the diffusion process due to its belonging to more than one community. According to that, the information diffusion is modeled in communities in which these nodes have high membership for reasons that may relate to the applications such as market optimization and rumor spreading. Our experiment made on a real social data, Digg news aggregator network on 15% of overlapped nodes, using our proposed model SFA-ICBDM described in previous work. The experimental results show that the cascade model of the overlapped nodes whether represents seed or node within cascade achieves best prediction accuracy in the community which the node belongs at more

Collaborative Topic Prediction Model for User Interest Recommendation in Online Social Networks

2012 ◽  
Vol 6 (23) ◽  
pp. 62-73 ◽  
Author(s):  
Liang Guo ◽  
Qiumiao Chen ◽  
Wenwen Han ◽  
Ye Tian ◽  
Yidong Cui ◽  
...  
Keyword(s):  
Social Networks ◽  
Prediction Model ◽  
Online Social Networks ◽  
User Interest
Sign in / Sign up

Export Citation Format

Share Document