Exploiting User Check-In Data for Geo-Friend Recommendations in Location-Based Social Networks

2020 ◽  
Vol 11 (2) ◽  
pp. 1-17
Author(s):  
Shudong Liu ◽  
Ke Zhang
Keyword(s):  
Social Networks ◽  
Online Social Networks ◽  
Time Slot ◽  
Empirical Studies ◽  
Real Life ◽  
Periodic Trajectories ◽  
The Public ◽  
Friend Recommendation ◽  
Location Based Social Networks ◽  
Friend Recommendations

The development of Web 2.0 technologies has meant that online social networks can both help the public facilitate sharing and communication and help them make new friends through their cyberspace social circles. Generating more accurate and geographically related results to help users find more friends in real life is gradually becoming a research hotspot. Recommending geographically related friends and alleviating check-in data sparsity problems in location-based social networks allows those to divide a day into different time slots and automatically collect user check-in data at each time slot over a certain period. Second, some important location points or regions are extracted from raw check-in trajectories, temporal periodic trajectories are constructed, and a geo-friend recommendation framework is proposed that can help users find geographically related friends. Finally, empirical studies from a real-world dataset demonstrate that this paper's method outperforms other existing methods for geo-friend recommendations in location-based social networks.

scholarly journals Exploring Trusted Relations among Virtual Interactions in Social Networks for Detecting Influence Diffusion

2019 ◽  
Vol 8 (9) ◽  
pp. 415
Author(s):  
Heba M. Wagih ◽  
Hoda M. O. Mokhtar ◽  
Samy S. Ghoniemy
Keyword(s):  
Social Networks ◽  
Peer Influence ◽  
Online Social Networks ◽  
Large Scale ◽  
Social Ties ◽  
Mutual Trust ◽  
Friend Recommendation ◽  
Influential Nodes ◽  
Location Based Social Networks ◽  
Virtual Interactions

Recently, social networks have shown huge potential in terms of collaborative web services and the study of peer influence as a result of the massive amount of data, datasets, and interrelations generated. These interrelations cannot guarantee the success of online social networks without ensuring the existence of trust between nodes. Detecting influential nodes improves collaborative filtering (CF) recommendations in which nodes with the highest influential capability are most likely to be the source of recommendations. Although CF-based recommendation systems are the most widely used approach for implementing recommender systems, this approach ignores the mutual trust between users. In this paper, a trust-based algorithm (TBA) is introduced to detect influential spreaders in social networks efficiently. In particular, the proposed TBA estimates the influence that each node has on the other connected nodes as well as on the whole network. Next, a Friend-of-Friend recommendation (FoF-SocialI) algorithm is addressed to detect the influence of social ties in the recommendation process. Finally, experimental results, performed on three large scale location-based social networks, namely, Brightkite, Gowalla, and Weeplaces, to test the efficiency of the proposed algorithm, are presented. The conducted experiments show a remarkable enhancement in predicting and recommending locations in various social networks.

Learning to Rank for Review Rating Prediction

2014 ◽  
Vol 556-562 ◽  
pp. 6286-6289
Author(s):  
Nian Li ◽  
Li Yin ◽  
Qing Xi Peng
Keyword(s):  
Learning To Rank ◽  
Empirical Studies ◽  
Real Life ◽  
Classification Problem ◽  
Machine Learning Method ◽  
Regression Problem ◽  
The Public ◽  
Rating Prediction ◽  
Multi Classification ◽  
New Machine

The Internet has experienced profound changes. Large amount of user-generated-contents provide valuable information to the public. Customers usually express their opinion in online shopping. After they finish the reviews, they give an overall rating to the product or service. In this paper, we focus on the review rating prediction problem. Previous studies usually regard this problem as a regression problem. We take a new machine learning method to solve the problem. Learning to rank method has been exploited to tackle the prediction. After feature selection, the maximum entropy classifier has been employed to solve the multi-classification problem. The real life dataset has been crawled to verify the proposed method. Empirical studies demonstrate the proposed method outperform the baseline methods.

scholarly journals Happiness Is Assortative in Online Social Networks

2011 ◽  
Vol 17 (3) ◽  
pp. 237-251 ◽  
Author(s):  
Johan Bollen ◽  
Bruno Gonçalves ◽  
Guangchen Ruan ◽  
Huina Mao
Keyword(s):  
Social Networks ◽  
Online Social Networks ◽  
Real Life ◽  
Well Being ◽  
Mood States ◽  
Physical Contact ◽  
Future Research ◽  
Human Society ◽  
Subjective Well Being ◽  
Collective Behaviors

Online social networking communities may exhibit highly complex and adaptive collective behaviors. Since emotions play such an important role in human decision making, how online networks modulate human collective mood states has become a matter of considerable interest. In spite of the increasing societal importance of online social networks, it is unknown whether assortative mixing of psychological states takes place in situations where social ties are mediated solely by online networking services in the absence of physical contact. Here, we show that the general happiness, or subjective well-being (SWB), of Twitter users, as measured from a 6-month record of their individual tweets, is indeed assortative across the Twitter social network. Our results imply that online social networks may be equally subject to the social mechanisms that cause assortative mixing in real social networks and that such assortative mixing takes place at the level of SWB. Given the increasing prevalence of online social networks, their propensity to connect users with similar levels of SWB may be an important factor in how positive and negative sentiments are maintained and spread through human society. Future research may focus on how event-specific mood states can propagate and influence user behavior in “real life.”

scholarly journals Exploring how Homophily and Accessibility can Facilitate Polarization in Social Networks

2018 ◽  
Author(s):  
Cameron Taylor ◽  
Alexander V. Mantzaris ◽  
Ivan Garibay
Keyword(s):  
Social Networks ◽  
Online Social Networks ◽  
Low Cost ◽  
Voter Model ◽  
The Public ◽  
Pairwise Interactions ◽  
Online Social Media ◽  
Complete Networks ◽  
Social Media Platforms ◽  
The Public Sphere

Polarization in online social networks has gathered a significant amount of attention in the research community and in the public sphere due to stark disagreements with millions of participants in topics surrounding politics, climate, the economy and other areas where an agreement is required. There are multiple approaches to investigating the scenarios in which polarization occurs and given that polarization is not a new phenomenon but that its virality may be supported by the low cost and latency messaging offered by online social media platforms; an investigation into the intrinsic dynamics of online opinion evolution is presented for complete networks. Extending a model which utilizes the Binary Voter Model (BVM) to examine the effect of the degree of freedom for selecting contacts based upon homophily, simulations show that different opinions are reinforced for a period of time when users have a greater range of choice for association. The facility of discussion threads and groups formed upon common views further delays the rate in which a consensus can form between all members of the network. This can temporarily incubate members from interacting with those who can present an alternative opinion where a voter model would then proceed to produce a homogeneous opinion based upon pairwise interactions.

scholarly journals User Classification on Online Social Networks by Post Frequency

2017 ◽  
Author(s):  
Gabriel Tavares ◽  
Saulo Mastelini ◽  
Sylvio Jr.
Keyword(s):  
Social Networks ◽  
Language Processing ◽  
Online Social Networks ◽  
Computational Cost ◽  
Real Life ◽  
Gradient Boosting ◽  
Support Vector ◽  
K Nearest Neighbors ◽  
User Classification ◽  
Extreme Gradient Boosting

This paper proposes a technique for classifying user accounts on social networks to detect fraud in Online Social Networks (OSN). The main purpose of our classification is to recognize the patterns of users from Human, Bots or Cyborgs. Classic and consolidated approaches of Text Mining employ textual features from Natural Language Processing (NLP) for classification, but some drawbacks as computational cost, the huge amount of data could rise in real-life scenarios. This work uses an approach based on statistical frequency parameters of the user posting to distinguish the types of users without textual content. We perform the experiment over a Twitter dataset and as learn-based algorithms in classification task we compared Random Forest (RF), Support Vector Machine (SVM), k-nearest Neighbors (k-NN), Gradient Boosting Machine (GBM) and Extreme Gradient Boosting (XGBoost). Using the standard parameters of each algorithm, we achieved accuracy results of 88% and 84% by RF and XGBoost, respectively

The Potential of Political Changes in the Information Age

2014 ◽  
pp. 37-59
Author(s):  
Anas Alahmed
Keyword(s):  
Social Networks ◽  
Saudi Arabia ◽  
New Media ◽  
Social Networking ◽  
Significant Role ◽  
Social Networking Sites ◽  
Real Life ◽  
The Public ◽  
Citizen Activism ◽  
The Public Sphere

In non-democratic societies new media social networks have played a significant role in changing political and social positions, not necessarily through real life but, instead, through cyber life. This chapter examines how Saudi activists challenge the political authority and how Saudi citizens took advantage of publicity by demanding political change. All of this happened due to social networks and new media, which allowed citizens to mobilize information for the sake of transparency. This was a new phenomenon in Saudi Arabia. The current young generation of Saudis, who use the Internet and social networking sites, played a significant role in the public sphere by making use of the space available to them within cyberspace. This chapter discusses the potential of political information to flourish in Saudi Arabia. It examines how and why citizen activism in Saudi Arabia can be effective. The chapter also shows that social networking activities have the power to change political decisions and society.

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