scholarly journals Identification of Influencers in eWord-of-Mouth communities using their Online Participation Features

2016 ◽  
Author(s):  
Sergio L. Toral ◽  
Maria Olmedilla ◽  
Francisco José Arenas-Márquez ◽  
M. Rocio Martinez-Torres
Keyword(s):  
Social Network ◽  
Online Social Network ◽  
Experimental Results ◽  
Purchasing Decisions ◽  
Trust Network ◽  
Online Participation ◽  
Meaningful Information

The identification of influencers in any type of online social network is of paramount importance, as they can significantly affect consumers’ purchasing decisions. This paper proposes the utilization of a self-designed web scraper to extract meaningful information for the identification of influencers and the analysis of how this new set of variables can be used to predict them. The experimental results from the Ciao UK website will be used to illustrate the proposed approach and to provide new insights in the identification of influencers. Obtained results show the importance of the trust network, but considering the intensity and the quality of both trustors and trustees.

Measure of the Content Creation Score on Social Network Using Sentiment Score and Passion Point

2020 ◽  
Author(s):  
Hien D. Nguyen ◽  
Tai Huynh ◽  
Son T. Luu ◽  
Suong N. Hoang ◽  
Vuong T. Pham ◽  
...  
Keyword(s):  
Social Network ◽  
Experimental Results ◽  
Information Propagation ◽  
Content Creation ◽  
Sentiment Score

Social network is one of efficient tools for spreading information. The evaluation of the content creation of a user is a useful feature to improve the ability of information propagation on social network. In this paper, the measures for evaluating the user’s content creation are proposed. They include the passion point of a user with a brand and the quality of the user’s posts. The passion point is computed based on the sentiment score of posting and the activity of the user. The quality of the user’s posts is computed through the analyzing of the post’s content. Those measures are combined to analyze the interesting of posts. The proposed method has been tested and get the positive experimental results.

scholarly journals Research on Relationship Strength under Personalized Recommendation Service

2020 ◽  
Vol 12 (4) ◽  
pp. 1459 ◽  
Author(s):  
Wanqiong Tao ◽  
Chunhua Ju ◽  
Chonghuan Xu
Keyword(s):  
Social Network ◽  
Online Social Network ◽  
Interaction Strength ◽  
Experimental Results ◽  
Strength Calculation ◽  
Personalized Recommendation ◽  
Relationship Strength ◽  
Interactive Behavior ◽  
Relationship Of ◽  
Feature Attribute

Relationship of users in an online social network can be applied to promote personalized recommendation services. The measurement of relationship strength between user pairs is crucial to analyze the user relationship, which has been developed by many methods. An issue that has not been fully addressed is that the interaction behavior of individuals subjected to the activity field preference and interactive habits will affect interactive behavior. In this paper, the three-way representation of the activity field is given firstly, the contribution weight of the activity filed preferences is measured based on the interactions in the positive and boundary regions. Then, the interaction strength is calculated, integrating the contribution weight of the activity field preference and interactive habit. Finally, user relationship strength is calculated by fusing the interaction strength, common friend rate and similarity of feature attribute. The experimental results show that the proposed method can effectively improve the accuracy of relationship strength calculation.

scholarly journals TDSRC: A Task-Distributing System of Crowdsourcing Based on Social Relation Cognition

2019 ◽  
Vol 2019 ◽  
pp. 1-12 ◽  
Author(s):  
Zhenlong Peng ◽  
Xiaolin Gui ◽  
Jian An ◽  
Ruowei Gui ◽  
Yali Ji
Keyword(s):  
Social Network ◽  
Problem Solving ◽  
Social Relation ◽  
Experimental Results ◽  
The Public ◽  
Trust Relationship ◽  
Main Challenge ◽  
The Social

Crowdsourcing significantly augments the creativity of the public and has become an indispensable component of many problem-solving pipelines. The main challenge, however, is the effective identification of malicious participators while distributing crowdsourcing tasks. In this paper, we propose a novel task-distributing system named Task-Distributing system of crowdsourcing based on Social Relation Cognition (TDSRC) to select qualified participators. First, we divided the tasks into categories according to task themes. Then, we constructed and calculated the Abilities Set (AS), Abilities Values (AVs), and the Friends’ Abilities Matrix (FAM) by using the historical interactive texts between a given task publisher (requester) and its friends. When a requester distributes a task, TDSRC can generate the candidate participators’ sequence based on the task needs and FAM. Finally, the best-matched friends in the sequence are selected as the task receivers (solvers), thus producing a personal FAM to disseminate the tasks. The experimental results indicate that (1) the proposed system can accurately and effectively discover the requester’s friends’ abilities and select appropriate solvers and (2) the natural trust relationship in the social network reduces fraudsters and enhances the quality of crowdsourcing services.

scholarly journals An Analysis of Participation, Identity Conversations, and Social Networking Affordances on an Online Social Network for Children

2016 ◽  
Vol 3 (1) ◽  
pp. 41-62
Author(s):  
Stephanie Valentine ◽  
Tracy Hammond
Keyword(s):  
Social Network ◽  
Social Networking ◽  
Online Social Network ◽  
Reward System ◽  
Daily Basis ◽  
Identity Exploration ◽  
Online Participation ◽  
Positive Correlation ◽  
Shared Experiences ◽  
Internal Focus

In this study, we explore the behaviors of children ages 7 to 12 years on our custom social network, KidGab, to understand the activities that increase participation and identity-related conversation. We specifically study the effects of two social networking affordances provided by KidGab: a suite of personality quizzes and a badge-based participatory reward system. Both affordances harness the preadolescent’s internal focus on identity exploration. We analyzed KidGab’s activity logs in an attempt to understand the relationships between activities on these affordances and other engagement measured on the site (e.g., the total posts authored on KidGab per day, total comments authored per day, likes per day, etc.). We also investigate the amount of conversation relating to identity that accompanies quizzes and badges. We found that taking quizzes and posting results had a higher positive correlation with online participation on KidGab than attempting to earn badges. Our results suggest that, though youth are interested in self-reflecting via textual compositions, on a daily basis they partake more in personality-quiz-style activities that provide them instant feedback and shared experiences with other users.

scholarly journals How Does User Social Network Improve Innovation Outcomes on a Virtual Innovation Platform?

2021 ◽  
Vol 29 (3) ◽  
pp. 188-211
Author(s):  
Guijie Qi ◽  
Linke Hou ◽  
Jiali Chen ◽  
Yikai Liang ◽  
Qi Zhang
Keyword(s):  
Social Network ◽  
Emotional Support ◽  
Positive Emotions ◽  
Online Social Network ◽  
User Innovation ◽  
Positive Effects ◽  
Quality Dimensions ◽  
Innovation Platform ◽  
Innovation Outcomes

Previous studies demonstrate that online interactive relations can help improve users' innovation outcomes, yet few studies have investigated how they influence user innovation. This paper builds a social network based on users' online interactive relations in one virtual innovation platform (LEGO Ideas). It characterizes the online social network relations from both quantity and quality dimensions and examines their influencing paths on users' innovation outcomes (i.e., emotional support and information flow). The empirical results show that both the quantity and quality of online relations impose positive effects on innovation, yet in different ways. The quantity of online relations could bring users more positive emotions, whereas the quality of online relations could bring them with more useful information and knowledge. By examining the influencing paths, this paper contributes to the literature on how online relations influence innovation outcomes as well as provides practical suggestions for innovation platforms.

scholarly journals Recognition on Online Social Network by user's writing style

2015 ◽  
Vol 8 (3) ◽  
pp. 64-85 ◽  
Author(s):  
Rodrigo Augusto Igawa ◽  
Alex Almeida ◽  
Bruno Zarpelão ◽  
Sylvio Barbon Jr
Keyword(s):  
Social Networks ◽  
Social Network ◽  
Online Social Networks ◽  
Online Social Network ◽  
Experimental Results ◽  
Writing Style ◽  
Authorship Verification

Compromising legitimate accounts is the most popular way of disseminating fraudulent content in Online Social Networks (OSN). To address this issue, we propose an approach for recognition of compromised Twitter accounts based on Authorship Verification. Our solution can detect accounts that became compromised by analysing their user writing styles. This way, when an account content does not match its user writing style, we affirm that the account has been compromised, similar to Authorship Verification. Our approach follows the profile-based paradigm and uses N-grams as its kernel. Then, a threshold is found to represent the boundary of an account writing style. Experiments were performed using two subsampled datasets from Twitter. Experimental results showed the developed model is very suitable for compromised recognition of Online Social Networks accounts due to the capacity of recognizing user styles over 95% accuracy for both datasets.

Extraverts Have Larger Social Network Layers

2011 ◽  
Vol 32 (3) ◽  
pp. 161-169 ◽  
Author(s):  
Thomas V. Pollet ◽  
Sam G. B. Roberts ◽  
Robin I. M. Dunbar
Keyword(s):  
Social Network ◽  
Social Relationships ◽  
Outer Layer ◽  
Network Size ◽  
Emotional Intensity ◽  
Emotional Closeness ◽  
Network Layer ◽  
Network Layers ◽  
Social Network Size

Previous studies showed that extraversion influences social network size. However, it is unclear how extraversion affects the size of different layers of the network, and how extraversion relates to the emotional intensity of social relationships. We examined the relationships between extraversion, network size, and emotional closeness for 117 individuals. The results demonstrated that extraverts had larger networks at every layer (support clique, sympathy group, outer layer). The results were robust and were not attributable to potential confounds such as sex, though they were modest in size (raw correlations between extraversion and size of network layer, .20 < r < .23). However, extraverts were not emotionally closer to individuals in their network, even after controlling for network size. These results highlight the importance of considering not just social network size in relation to personality, but also the quality of relationships with network members.

Sign in / Sign up

Export Citation Format

Share Document