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.

How do personality traits affect communication among users in online social networks?

2014 ◽  
Vol 38 (1) ◽  
pp. 136-153 ◽  
Author(s):  
Jose Maria Balmaceda ◽  
Silvia Schiaffino ◽  
Daniela Godoy
Keyword(s):  
Social Networks ◽  
Social Media ◽  
Personality Traits ◽  
Online Social Networks ◽  
Factor Model ◽  
Five Factor Model ◽  
Personality Dimensions ◽  
Content Type ◽  
Wide Range ◽  
Similarity Attraction

Purpose – The purpose of this work is to analyse the relationships between the personality traits of linked users in online social networks. First the authors tried to discover relation patterns between personality dimensions in conversations. They also wanted to verify some hypotheses: whether users' personality is stable throughout different conversation threads and whether the similarity-attraction paradigm can be verified in this context. They used the five factor model of personality or Big Five, which has been widely studied in psychology. Design/methodology/approach – One of the approaches to detect users' personalities is by analysing the language they use when they talk to others. Based on this assumption the authors computed users' personality from the conversations extracted from the MySpace social network. Then the authors analysed the relationships among personality traits of users to discover patterns. Findings – The authors found that there are patterns between some personality dimensions in conversation threads, for example, agreeable people tend to communicate with extroverted people. They confirmed that the personality stability theory can be verified in social networks. Finally the authors could verify the similarity-attraction paradigm for some values of personality traits, such as extroversion, agreeableness, and openness to experience. Originality/value – The results the authors found provide some clues about how people communicate within online social networks, particularly who they tend to communicate with depending on their personality. The discovered patterns can be used in a wide range of applications, such as suggesting contacts in online social networks. Although some studies have been made regarding the role of personality in social media, no similar analysis has been done to evaluate how users communicate in social media considering their personality.

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.

Birds of a feather: the geographic interconnection of Australian universities on Twitter

2016 ◽  
Vol 8 (1) ◽  
pp. 88-100 ◽  
Author(s):  
Stuart Palmer
Keyword(s):  
Social Media ◽  
State Level ◽  
Strong Relationship ◽  
Physical Geography ◽  
Research Literature ◽  
Content Type ◽  
Social Media Networks ◽  
Online Social Media ◽  
Use Of Social Media ◽  
Australian Universities

Purpose – Regardless of their virtual nature, research suggests that social media networks are still influenced by geography. The purpose of this paper is to investigate the connections between Australian universities on the Twitter social media system. Design/methodology/approach – This research employs network analysis and visualisation to characterise the connections between Australian universities on Twitter. Findings – A strong relationship to geography, both at the intra-state level and the inter-state level, was observed in the connections between Australian universities on Twitter. A relationship between number of followers and time since joining Twitter was also observed. Research limitations/implications – The research presented is limited to Australian universities only and represents a snapshot in time only. Practical implications – Australian universities have the opportunity to reach beyond the geographically restricted connections observed here, to actively seek new audiences, and to realise the cited benefits of online social media relating to increased connection across physical and digital frontiers. By capitalising on the strong “locality” observed in social media connections, a university could become a desirable source of information that is likely to be of interest to, and valued by, local constituents. Originality/value – This paper contributes to the research literature on university use of social media by addressing the so far largely silent area of inter-institutional connections via social media, and the influence of physical geography on the connections between universities on Twitter. It also offers a practical methodology for those interested in further research in this area.

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.

Social Media Lessons on the Nature of Political Decision Making

2020 ◽  
Author(s):  
Douglas R. Pierce
Keyword(s):  
Decision Making ◽  
Social Networks ◽  
Social Media ◽  
Media Use ◽  
Political Judgment ◽  
Political Decision ◽  
Social Media Use ◽  
Online Social Media ◽  
New Models ◽  
Political Decision Making

Traditional models of political decision making tend to focus on the subject’s information levels or information-processing strategy. One of the most common conceptions of political decision making assumes that voters who are informed by a store of factually accurate policy information make more optimal decisions—that is, decisions more in line with their supposed political interests—than those who lack such information. However, this traditional view of political decision making minimizes the roles of affect and social influence on judgment. No phenomenon underscores the primary place of these constructs more so than the meteoric rise of online social media use. Indeed, scholars working at the intersection of social media use and political judgment have made important revisions to the traditional model of political decision making. Specifically, the popularity of online social networks as a tool for exchanging information, connecting with others, and displaying affective reactions to stimuli suggest that new models of competent political decision making which take into account social, affective, and cognitive elements are replacing older, information-based and rational choice models. In this essay, I review some of the pertinent literature on social media use and decision-making and argue that motivation, emotion, and social networks are key components of political judgment and are in fact more relevant to understanding political decisions than political knowledge or political sophistication. I also propose that new models of political decision-making would do well to take into account automaticity, social approval, and the role of information in both rationalizing preferences and persuading others.

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.

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