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.

scholarly journals Spam Diffusion in Social Networking Media using Latent Dirichlet Allocation

2019 ◽  
Vol 8 (12) ◽  
pp. 881-885
Keyword(s):  
Online Social Networks ◽  
Topic Modeling ◽  
Information Diffusion ◽  
Latent Dirichlet Allocation ◽  
Good Accuracy ◽  
Ground Truth ◽  
Online Social Media ◽  
Diffusion Dynamics ◽  
Dirichlet Allocation

Like web spam has been a major threat to almost every aspect of the current World Wide Web, similarly social spam especially in information diffusion has led a serious threat to the utilities of online social media. To combat this challenge the significance and impact of such entities and content should be analyzed critically. In order to address this issue, this work usedTwitter as a case study and modeled the contents of information through topic modeling and coupled it with the user oriented feature to deal it with a good accuracy. Latent Dirichlet Allocation (LDA) a widely used topic modeling technique is applied to capture the latent topics from the tweets’ documents. The major contribution of this work is twofold: constructing the dataset which serves as the ground-truth for analyzing the diffusion dynamics of spam/non-spam information and analyzing the effects of topics over the diffusibility. Exhaustive experiments clearly reveal the variation in topics shared by the spam and nonspam tweets. The rise in popularity of online social networks, not only attracts legitimate users but also the spammers. Legitimate users use the services of OSNs for a good purpose i.e., maintaining the relations with friends/colleagues, sharing the information of interest, increasing the reach of their business through advertisings

Working Adults' Buying Intention Through Online Social Network

2021 ◽  
Vol 17 (4) ◽  
pp. 92-116
Author(s):  
Syed Shah Alam ◽  
Chieh-Yu Lin ◽  
Mohd Helmi Ali ◽  
Nor Asiah Omar ◽  
Mohammad Masukujjaman
Keyword(s):  
Social Networks ◽  
Social Media ◽  
Social Network ◽  
Online Social Networks ◽  
Perceived Risk ◽  
Word Of Mouth ◽  
Online Social Network ◽  
Perceived Value ◽  
Online Social Media ◽  
Starting Point

Most businesses have online social media presence; therefore, understanding of working adult's perception on buying through online social networks is vital. The aim of this study is to examine the effect of perceived value, sociability, usability, perceived risk, trust, and e-word-of-mouth on buying intention through online social network sites. The research model for this study was developed based on the literature on information system research. This study adopted convenient sampling of non-probability sampling procedure. Data were collected through self-administered questionnaire, and PLS-based path analysis was used to analyse responses. The findings of the study shows that perceived value, sociability, usability, e-word-of-mouth, attitude, and subjective norm are significant constructs of buying intention through online social networks. This research can serve as a starting point for online shopping research through online social media while encouraging further exploration and integration addition adoption constructs.

Sentiment Based Information Diffusion in Online Social Networks

2018 ◽  
Vol 8 (1) ◽  
pp. 60-74
Author(s):  
Mohammad Ahsan ◽  
Madhu Kumari ◽  
Tajinder Singh ◽  
Triveni Lal Pal
Keyword(s):  
Social Networks ◽  
Social Media ◽  
Random Forests ◽  
Presidential Election ◽  
Online Social Networks ◽  
Regression Models ◽  
Information Diffusion ◽  
Comprehensive Understanding ◽  
Contextual Features ◽  
The Relationship

This article describes how social media has emerged as a main vehicle of information diffusion among people. They often share their experience, feelings and knowledge through these channels. Some pieces of information quickly reach a large number of people, while others not. The authors analyzed this variation by collecting tweets on 2016 U.S. presidential election. This article gives a comprehensive understanding of how sentiment encoded in the textual contents can affects the information diffusion, along with the effect of content features, i.e., URLs, hashtags, and contextual features, i.e., number of followers, followees, tweets generated by the user so far, account age, tweet age. In order to explore the relationship between sentiment content and information diffusion, the authors first checked the features' significance as an indicator of diffusibility by using random forests. Finally, support vectors and k-Neighbors regression models are used to capture the complete dynamics of information diffusion. Experiments and results clearly reveal that sentiment prominently helps in making a better prediction of information diffusion.

scholarly journals Detection of Malicious Data in Twitter Using Machine Learning Approaches

2021 ◽  
Vol 12 (3) ◽  
pp. 4951-4958
Author(s):  
B.Mukunthan Et. al.
Keyword(s):  
Social Networks ◽  
Social Media ◽  
Digital Media ◽  
Online Social Networks ◽  
Media Culture ◽  
Learning Approaches ◽  
Traditional Media ◽  
Misleading Information ◽  
Online Social Media ◽  
False News

: Unlike traditional media social media is populated by unknown individuals who can broadcast whatever they like. This online social media culture is dynamic in its nature and transition to digital media is becoming a trend among people. In upcoming years the use of traditional media will decline, and the increasing use of Online Social Networks(OSNs) blur the actual information of the traditional media. The information generated by the authentic users gives useful information to the general users, on the other hand,Spammers spread irrelevant or misleading information that makes social media a plot for false news. So unwanted text or vulnerable links can be distributed to specific users. These false texts are anonymous and sometimes linked with potential URLs. Due to data restrictions and communication categories, the current systems do not deserve an exact statistical classification for a piece of news. We will study different research papers using various techniques for master training in the prediction and detection of malicious data on social networks online. We tried to find spam tweets from the tweets collected by using Enhanced Random forest classifications and NaiveBayes in this research. To evaluate the work, different validation metrics such as F1-scoring, accurcy and precision values are calculated.

Big Data and Political Social Networks

2016 ◽  
Vol 35 (1) ◽  
pp. 126-141 ◽  
Author(s):  
Axel Maireder ◽  
Brian E. Weeks ◽  
Homero Gil de Zúñiga ◽  
Stephan Schlögl
Keyword(s):  
Social Networks ◽  
Social Media ◽  
Big Data ◽  
Political Communication ◽  
Online Social Networks ◽  
Information Diffusion ◽  
Political Issues ◽  
Transatlantic Trade ◽  
Twitter Data ◽  
Shed Light

Social media have changed the way citizens, journalists, institutions, and activists communicate about social and political issues. However, questions remain about how information is diffused through these networks and the degree to which each of these actors is influential in communicating information. In this study, we introduce two novel social network measures of connection and information diffusion that help shed light on patterns of political communication online. The Audience Diversity Score assesses the diversity of a particular actor’s followers and identifies which actors reach different publics with their messages. The Communication Connector Bridging Score highlights the most influential actors in the network who are potentially able to connect different spheres of communication through their information diffusion. We apply and discuss these measures using Twitter data from the discussion regarding the Transatlantic Trade Investment Partnership in Europe. Our results provide unique insights into the role various actors play in diffusing political information in online social networks.

Privacy Control of Photo Sharing in Online Social Media

2020 ◽  
Vol 17 (9) ◽  
pp. 4692-4697
Author(s):  
Radhika Goriparthi ◽  
Kanyakumari Jagadish Basani
Keyword(s):  
Social Networks ◽  
Social Media ◽  
Data Sharing ◽  
Online Social Networks ◽  
Facial Recognition ◽  
Recognition System ◽  
Online Social Media ◽  
Photo Sharing ◽  
Facial Recognition System ◽  
Prior Permission

Online Social Networks were famous for the data sharing etc., they have many features like chatting, photo sharing etc. A photo can be leaked without prior permission of the owner. A mechanism was developed to allow each individual in the group aware of the photo posting activity. An efficient facial recognition system that can recognize two different photos was designed. Only the owner of the photo can share the photo, this system is superior to other systems in terms of recognition ratio. This application is developed on the windows platform.

Sentiment Based Information Diffusion in Online Social Networks

2020 ◽  
pp. 807-822
Author(s):  
Mohammad Ahsan ◽  
Madhu Kumari ◽  
Tajinder Singh ◽  
Triveni Lal Pal
Keyword(s):  
Social Networks ◽  
Social Media ◽  
Random Forests ◽  
Presidential Election ◽  
Online Social Networks ◽  
Regression Models ◽  
Information Diffusion ◽  
Comprehensive Understanding ◽  
Support Vectors ◽  
The Relationship

This article describes how social media has emerged as a main vehicle of information diffusion among people. They often share their experience, feelings and knowledge through these channels. Some pieces of information quickly reach a large number of people, while others not. The authors analyzed this variation by collecting tweets on 2016 U.S. presidential election. This article gives a comprehensive understanding of how sentiment encoded in the textual contents can affects the information diffusion, along with the effect of content features, i.e., URLs, hashtags, and contextual features, i.e., number of followers, followees, tweets generated by the user so far, account age, tweet age. In order to explore the relationship between sentiment content and information diffusion, the authors first checked the features' significance as an indicator of diffusibility by using random forests. Finally, support vectors and k-Neighbors regression models are used to capture the complete dynamics of information diffusion. Experiments and results clearly reveal that sentiment prominently helps in making a better prediction of information diffusion.

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