A distributional semantics-based information retrieval framework for online social networks

2021 ◽  
pp. 1-11
Author(s):  
V.S. Anoop ◽  
P. Deepak ◽  
S. Asharaf
Keyword(s):  
Social Networks ◽  
Information Retrieval ◽  
Online Social Networks ◽  
Latent Dirichlet Allocation ◽  
Relevant Information ◽  
Distributional Semantics ◽  
Scalable Algorithms ◽  
Mobile Platforms ◽  
Cancer Support ◽  
Efficient Extraction

Online social networks are considered to be one of the most disruptive platforms where people communicate with each other on any topic ranging from funny cat videos to cancer support. The widespread diffusion of mobile platforms such as smart-phones causes the number of messages shared in such platforms to grow heavily, thus more intelligent and scalable algorithms are needed for efficient extraction of useful information. This paper proposes a method for retrieving relevant information from social network messages using a distributional semantics-based framework powered by topic modeling. The proposed framework combines the Latent Dirichlet Allocation and distributional representation of phrases (Phrase2Vec) for effective information retrieval from online social networks. Extensive and systematic experiments on messages collected from Twitter (tweets) show this approach outperforms some state-of-the-art approaches in terms of precision and accuracy and better information retrieval is possible using the proposed method.

scholarly journals Strengthening ties towards a highly-connected world

2022 ◽  
Author(s):  
Antonis Matakos ◽  
Aristides Gionis
Keyword(s):  
Social Networks ◽  
Online Social Networks ◽  
Constant Factor ◽  
Algorithmic Problem ◽  
Full Potential ◽  
User Engagement ◽  
Scalable Algorithms ◽  
Special Cases ◽  
Access Information ◽  
Flow Of Information

AbstractOnline social networks provide a forum where people make new connections, learn more about the world, get exposed to different points of view, and access information that were previously inaccessible. It is natural to assume that content-delivery algorithms in social networks should not only aim to maximize user engagement but also to offer opportunities for increasing connectivity and enabling social networks to achieve their full potential. Our motivation and aim is to develop methods that foster the creation of new connections, and subsequently, improve the flow of information in the network. To achieve our goal, we propose to leverage the strong triadic closure principle, and consider violations to this principle as opportunities for creating more social links. We formalize this idea as an algorithmic problem related to the densest k-subgraph problem. For this new problem, we establish hardness results and propose approximation algorithms. We identify two special cases of the problem that admit a constant-factor approximation. Finally, we experimentally evaluate our proposed algorithm on real-world social networks, and we additionally evaluate some simpler but more scalable algorithms.

scholarly journals Hot Topic Community Discovery on Cross Social Networks

2019 ◽  
Vol 11 (3) ◽  
pp. 60 ◽  
Author(s):  
Xuan Wang ◽  
Bofeng Zhang ◽  
Furong Chang
Keyword(s):  
Social Networks ◽  
Social Network ◽  
Online Social Networks ◽  
Latent Dirichlet Allocation ◽  
Rapid Development ◽  
Unified Model ◽  
Text Data ◽  
Community Discovery ◽  
Latent Topic ◽  
Discovery Method

The rapid development of online social networks has allowed users to obtain information, communicate with each other and express different opinions. Generally, in the same social network, users tend to be influenced by each other and have similar views. However, on another social network, users may have opposite views on the same event. Therefore, research undertaken on a single social network is unable to meet the needs of research on hot topic community discovery. “Cross social network” refers to multiple social networks. The integration of information from multiple social network platforms forms a new unified dataset. In the dataset, information from different platforms for the same event may contain similar or unique topics. This paper proposes a hot topic discovery method on cross social networks. Firstly, text data from different social networks are fused to build a unified model. Then, we obtain latent topic distributions from the unified model using the Labeled Biterm Latent Dirichlet Allocation (LB-LDA) model. Based on the distributions, similar topics are clustered to form several topic communities. Finally, we choose hot topic communities based on their scores. Experiment result on data from three social networks prove that our model is effective and has certain application value.

scholarly journals SentiFlow: An Information Diffusion Process Discovery Based on Topic and Sentiment from Online Social Networks

2018 ◽  
Vol 10 (8) ◽  
pp. 2731 ◽  
Author(s):  
Berny Carrera ◽  
Jae-Yoon Jung
Keyword(s):  
Social Networks ◽  
Social Media ◽  
Diffusion Process ◽  
Online Social Networks ◽  
Information Diffusion ◽  
Latent Dirichlet Allocation ◽  
Probabilistic Approach ◽  
Digital Era ◽  
Online Social Media ◽  
User Comments

In this digital era, people can become more interconnected as information spreads easily and quickly through online social media. The rapid growth of the social network services (SNS) increases the need for better methodologies for comprehending the semantics among the SNS users. This need motivated the proposal of a novel framework for understanding information diffusion process and the semantics of user comments, called SentiFlow. In this paper, we present a probabilistic approach to discover an information diffusion process based on an extended hidden Markov model (HMM) by analyzing the users and comments from posts on social media. A probabilistic dissemination of information among user communities is reflected after discovering topics and sentiments from the user comments. Specifically, the proposed method makes the groups of users based on their interaction on social networks using Louvain modularity from SNS logs. User comments are then analyzed to find different sentiments toward a subject such as news in social networks. Moreover, the proposed method is based on the latent Dirichlet allocation for topic discovery and the naïve Bayes classifier for sentiment analysis. Finally, an example using Facebook data demonstrates the practical value of SentiFlow in real world applications.

Analysis of User Interactions in Online Social Networks

2011 ◽  
Author(s):  
Seokchan Yun ◽  
Heungseok Do ◽  
Jinuk Jung ◽  
Song Mina ◽  
Namgoong Hyun ◽  
...  
Keyword(s):  
Social Networks ◽  
Online Social Networks ◽  
User Interactions
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