Location-Based Online Social Networks: Location-Based Online Social Media, Location-Based Online Social Services

2018 ◽  
pp. 1213-1213
Keyword(s):  
Social Networks ◽  
Social Media ◽  
Social Services ◽  
Online Social Networks ◽  
Online Social Media

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.

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.

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.

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.

Grey social media engagement analysis

2016 ◽  
Vol 6 (2) ◽  
pp. 233-245 ◽  
Author(s):  
Camelia Delcea
Keyword(s):  
Decision Making ◽  
Social Networks ◽  
Social Media ◽  
Online Social Networks ◽  
Online Advertising ◽  
Strong Relationship ◽  
Content Type ◽  
Online Social Media ◽  
Social Media Engagement ◽  
The Right

Purpose – Recent studies have shown that customers are more likely to buy a certain product or service from a company they can follow or contact on social media. Moreover, the customers feel a stronger relationship with the companies they are interacting in the virtual networks. But have the companies succeed in getting everything from this strong relationship? Are their online advertising campaigns getting to the right customers? Is there any connection between the social media engagement and the decisions users are taking? This is going to be shaped in this paper through a grey analysis applied to the selected variables. The paper aims to discuss these issues. Design/methodology/approach – As the nature of the relationships created in the online social networks is characterized by incomplete information, the analysis will make used of the concepts and means offered by the grey systems theory, a theory that have obtained good results over the time when used on uncertain situations. Findings – By applying a grey relational analysis (GRA) to the considered variables, a strong relationship between the decision easiness and both time spent on social media platforms and number of the accessed websites has been identified. Moreover, it has been determined that the decision happiness is closely related to the companies’ websites and their commercials. Research limitations/implications – The present paper shapes the relationship between the usage of online social media and consumers’ decision-making process and decision-making happiness. Due to the fact that the online social media includes billions of users worldwide, the study has some limitations due to the users’ number. Originality/value – The paper uses GRA for drawing the connections between the online social networks reality and its influence to users’ every-day life. Considering the present findings, it can be underlined that the identification of the persons which are influential becomes important in order to get to the proper targeted customers.

scholarly journals Social influence and unfollowing accelerate the emergence of echo chambers

2020 ◽  
Author(s):  
Kazutoshi Sasahara ◽  
Wen Chen ◽  
Hao Peng ◽  
Giovanni Luca Ciampaglia ◽  
Alessandro Flammini ◽  
...  
Keyword(s):  
Social Networks ◽  
Social Media ◽  
Online Social Networks ◽  
Mitigation Strategies ◽  
Social Connections ◽  
Online Social Media ◽  
The Social ◽  
Model Dynamics ◽  
Echo Chambers ◽  
Access Information

Abstract While social media make it easy to connect with and access information from anyone, they also facilitate basic influence and unfriending mechanisms that may lead to segregated and polarized clusters known as “echo chambers.” Here we study the conditions in which such echo chambers emerge by introducing a simple model of information sharing in online social networks with the two ingredients of influence and unfriending. Users can change both their opinions and social connections based on the information to which they are exposed through sharing. The model dynamics show that even with minimal amounts of influence and unfriending, the social network rapidly devolves into segregated, homogeneous communities. These predictions are consistent with empirical data from Twitter. Although our findings suggest that echo chambers are somewhat inevitable given the mechanisms at play in online social media, they also provide insights into possible mitigation strategies.

Fuzzy based feature engineering architecture for sentiment analysis of medical discussion over online social networks

2021 ◽  
pp. 1-13
Author(s):  
C S Pavan Kumar ◽  
L D Dhinesh Babu
Keyword(s):  
Machine Learning ◽  
Social Networks ◽  
Social Media ◽  
Sentiment Analysis ◽  
Membership Function ◽  
Online Social Networks ◽  
Learning Model ◽  
Feature Engineering ◽  
Machine Learning Model ◽  
Social Media Platforms

Sentiment analysis is widely used to retrieve the hidden sentiments in medical discussions over Online Social Networking platforms such as Twitter, Facebook, Instagram. People often tend to convey their feelings concerning their medical problems over social media platforms. Practitioners and health care workers have started to observe these discussions to assess the impact of health-related issues among the people. This helps in providing better care to improve the quality of life. Dementia is a serious disease in western countries like the United States of America and the United Kingdom, and the respective governments are providing facilities to the affected people. There is much chatter over social media platforms concerning the patients’ care, healthy measures to be followed to avoid disease, check early indications. These chatters have to be carefully monitored to help the officials take necessary precautions for the betterment of the affected. A novel Feature engineering architecture that involves feature-split for sentiment analysis of medical chatter over online social networks with the pipeline is proposed that can be used on any Machine Learning model. The proposed model used the fuzzy membership function in refining the outputs. The machine learning model has obtained sentiment score is subjected to fuzzification and defuzzification by using the trapezoid membership function and center of sums method, respectively. Three datasets are considered for comparison of the proposed and the regular model. The proposed approach delivered better results than the normal approach and is proved to be an effective approach for sentiment analysis of medical discussions over online social networks.

Limitation of degree information for analyzing the interaction evolution in online social networks

2014 ◽  
Vol 25 (10) ◽  
pp. 1450056 ◽  
Author(s):  
Ke-Ke Shang ◽  
Wei-Sheng Yan ◽  
Xiao-Ke Xu
Keyword(s):  
Social Networks ◽  
Social Network ◽  
Social Services ◽  
Online Social Networks ◽  
Dynamic Network ◽  
Interaction Network ◽  
Strength Distribution ◽  
Customer Churn ◽  
Dynamic Social Network ◽  
Temporal Interactions

Previously many studies on online social networks simply analyze the static topology in which the friend relationship once established, then the links and nodes will not disappear, but this kind of static topology may not accurately reflect temporal interactions on online social services. In this study, we define four types of users and interactions in the interaction (dynamic) network. We found that active, disappeared, new and super nodes (users) have obviously different strength distribution properties and this result also can be revealed by the degree characteristics of the unweighted interaction and friendship (static) networks. However, the active, disappeared, new and super links (interactions) only can be reflected by the strength distribution in the weighted interaction network. This result indicates the limitation of the static topology data on analyzing social network evolutions. In addition, our study uncovers the approximately stable statistics for the dynamic social network in which there are a large variation for users and interaction intensity. Our findings not only verify the correctness of our definitions, but also helped to study the customer churn and evaluate the commercial value of valuable customers in online social networks.

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