scholarly journals Understanding Large-Scale Social Relationship Data by Combining Conceptual Graphs and Domain Ontologies

2021 ◽  
Vol 2021 ◽  
pp. 1-18
Author(s):  
Zhao Huang ◽  
Liu Yuan
Keyword(s):  
Social Media ◽  
Social Relationship ◽  
Large Scale ◽  
Conceptual Graphs ◽  
Formal Representation ◽  
Lehigh University ◽  
Large Scale Data ◽  
Conceptual Graph ◽  
Discovery Method ◽  
The Relationship

People worldwide communicate online and create a great amount of data on social media. The understanding of such large-scale data generated on social media and uncovering patterns from social relationship has received much attention from academics and practitioners. However, it still faces challenges to represent and manage the large-scale social relationship data in a formal manner. Therefore, this study proposes a social relationship representation model, which addresses both conceptual graph and domain ontology. Such a formal representation of a social relationship graph can provide a flexible and adaptive way to complete social relationship discovery. Using the term-define capability of ontologies and the graphical structure of the conceptual graph, this paper presents a social relationship description with formal syntax and semantics. The reasoning procedure working on this formal representation can exploit the capability of ontology reasoning and graph homomorphism-based reasoning. A social relationship graph constructed from the Lehigh University Benchmark (LUBM) is used to test the efficiency of the relationship discovery method.

scholarly journals Social Media User-Influencer Congruity

2022 ◽  
Vol 13 (1) ◽  
pp. 1-26
Author(s):  
Nida Tafheem ◽  
Hatem El-Gohary ◽  
Rana Sobh
Keyword(s):  
Social Media ◽  
Conceptual Framework ◽  
Social Relationships ◽  
Social Relationship ◽  
Substantial Impact ◽  
Social Media Platform ◽  
Brand Engagement ◽  
Social Media Platforms ◽  
Media Platform ◽  
The Relationship

This paper explores and inspects the effect of user-influencer congruence on social media platforms para-social relationships and consumer brand engagement (COBRA). In addition, the paper inspects the influence of para-social relationships on consumers brand in addition to the influence of social media platform type in moderating the effect of personality on para-social relationships and COBRA. A conceptual framework is developed to demonstrate the proposed relationships. Data was collected using online questionnaires, with 180 valid responses. The results suggest that user-influencer personality congruence is a salient predictor of para-social relationships and COBRA and that para-social relationship(s) have a substantial impact on customer brand engagement. Nevertheless, the results also indicated that social media platform type do not influence the relationship between congruity and para-social relationships or COBRA.

Online celebrity discourses on Facebook

2020 ◽  
Vol 8 (3) ◽  
pp. 305-319 ◽  
Author(s):  
Dániel Hegedűs
Keyword(s):  
Social Media ◽  
Large Scale ◽  
Modern Society ◽  
Large Scale Data ◽  
Cognitive Patterns ◽  
Social Media Platforms ◽  
Altered Form ◽  
The Individual ◽  
Facebook Pages ◽  
Scale Data

The web 2.0 phenomenon and social media – without question – have reshaped our everyday experiences. These changes that they have generated affect how we consume, communicate and present ourselves, just to name a few aspects of life, and moreover, opened up new perspectives for sociology. Though many social practices persist in a somewhat altered form, brand new types of entities have emerged on different social media platforms: one of them is the video blogger. These actors have gained great visibility through so-called micro-celebrity practices and have become potential large-scale distributors of ideas, values and knowledge. Celebrities, in this case micro-celebrities (video bloggers), may disseminate such cognitive patterns through their constructed discourse which is objectified in the online space through a peculiar digital face (a social media profile) where fans can react, share and comment according to the affordances of the digital space. Most importantly, all of these interactions are accessible for scholars to examine the fan and celebrity practices of our era. This research attempts to reconstruct these discursive interactions on the Facebook pages of ten top Hungarian video bloggers. All findings are based on a large-scale data collection using the Netvizz application. As part of the interpretation of the results, a further consideration was that celebrity discourses may be a sort of disciplinary force in (post)modern society, which normalizes the individual to some extent by providing adequate schemas of attitude, mentality and ways of consumption.

Fourier Transform Based GPU Acceleration

2013 ◽  
Vol 347-350 ◽  
pp. 2926-2929
Author(s):  
Jing Shen Li
Keyword(s):  
Fourier Transform ◽  
Parallel Computing ◽  
Large Scale ◽  
Gpu Acceleration ◽  
Image Transformation ◽  
Gpu Parallel Computing ◽  
Large Scale Data ◽  
Relationship Of ◽  
The Relationship ◽  
Scale Data

In digital image processing, Fourier transform is an important algorithm of image transformation. In order to improve the speed of Fourier transform, the paper proposes to deal with the image with GPU parallel computing through the method of GPU accelerating MATLB. The relationship of data scale and calculation speed is analyzed through the traditional CPU serial operation and GPU parallel computing. Computer simulations verify that the calculation speed can be improved by GPU about large scale data.

scholarly journals A Review of Models for Hydrating Large-scale Twitter Data of COVID-19-related Tweets for Transportation Research

2020 ◽  
Author(s):  
Mahmoud Arafat
Keyword(s):  
Data Mining ◽  
Social Media ◽  
Large Scale ◽  
Social Media Data ◽  
Work Related ◽  
Large Scale Data ◽  
Software Models ◽  
Starting Point ◽  
Transportation Research ◽  
Media Data

<p>In response to the Coronavirus disease (COVID-19) outbreak and the Transportation Research Board’s (TRB) urgent need for work related to transportation and pandemics, this paper contributes with a sense of urgency and provides a starting point for research on the topic. The main goal of this paper is to support transportation researchers and the TRB community during this COVID-19 pandemic by reviewing the performance of software models used for extracting large-scale data from Twitter streams related to COVID-19. The study extends the previous research efforts in social media data mining by providing a review of contemporary tools, including their computing maturity and their potential usefulness. The paper also includes an open repository for the processed data frames to facilitate the quick development of new transportation research studies. The output of this work is recommended to be used by the TRB community when deciding to further investigate topics related to COVID-19 and social media data mining tools.</p>

scholarly journals A Review of Models for Hydrating Large-scale Twitter Data of COVID-19-related Tweets for Transportation Research

2020 ◽  
Author(s):  
Mahmoud Arafat
Keyword(s):  
Data Mining ◽  
Social Media ◽  
Large Scale ◽  
Social Media Data ◽  
Work Related ◽  
Large Scale Data ◽  
Software Models ◽  
Starting Point ◽  
Transportation Research ◽  
Media Data

<p>In response to the Coronavirus disease (COVID-19) outbreak and the Transportation Research Board’s (TRB) urgent need for work related to transportation and pandemics, this paper contributes with a sense of urgency and provides a starting point for research on the topic. The main goal of this paper is to support transportation researchers and the TRB community during this COVID-19 pandemic by reviewing the performance of software models used for extracting large-scale data from Twitter streams related to COVID-19. The study extends the previous research efforts in social media data mining by providing a review of contemporary tools, including their computing maturity and their potential usefulness. The paper also includes an open repository for the processed data frames to facilitate the quick development of new transportation research studies. The output of this work is recommended to be used by the TRB community when deciding to further investigate topics related to COVID-19 and social media data mining tools.</p>

scholarly journals Dialoguing with Data and Data Reduction: An Observational, Narrowing-Down Approach to Social Media Network Analysis

2021 ◽  
Vol 2 (1) ◽  
pp. 14-29
Author(s):  
Jingrong Tong ◽  
Landong Zuo
Keyword(s):  
Social Media ◽  
Large Scale ◽  
Human Subjects ◽  
Computational Algorithms ◽  
Human Relationships ◽  
Social Media Networks ◽  
Large Scale Data ◽  
Social Media Network ◽  
Scale Data

In this article, we propose an observational, narrowing-down approach to analysing social media networks and developing research design by the joint use of computational algorithms and researchers’ inductive exploration and interpretive explanations. The Brexit referendum on Twitter study is used to illustrate how we applied this approach in practice. In this study, observation helped us combine the strengths of computational statistical analysis and modelling and of inductive inquiries. Computational algorithms and tools including Elasticsearch, Kibana and Gephi provided us with an “ethnographic field” where we were able to inductively observe the relationships among users and to reduce the amount of data down to a level in which we could intuitively understand these relationships. In traditional observational studies, talking to human subjects and observing their interactions in a research site are important to ethnographers. Likewise, it is useful for social science researchers to dialogue with data, observe human relationships embodied in the data and reconstructed by computational tools, and understand these relationships through closely examining a small batch of meaningful data that is extracted from large-scale data. In this case study, adopting the proposed approach, we found the importance of political disagreement leading to a tale of two politicians, in which pro-Brexit users denounced @David_Cameron but legitimised @Nigel_Farage.

Global Trends in Plastic Surgery on Social Media: Analysis of 2 Million Posts

2021 ◽  
Author(s):  
Sterling E Braun ◽  
Michaela K O’Connor ◽  
Margaret M Hornick ◽  
Melissa E Cullom ◽  
James A Butterworth
Keyword(s):  
Social Media ◽  
Plastic Surgery ◽  
Large Scale ◽  
Descriptive Analysis ◽  
The United States ◽  
Media Analysis ◽  
Classification Model ◽  
Global Trends ◽  
Large Scale Data ◽  
Textual Data

Abstract Background Plastic Surgeons and patients increasingly use social media. Despite evidence implicating its importance in Plastic Surgery, the large amount of data has made social media difficult to study. Objectives This study seeks to provide a comprehensive assessment of Plastic Surgery content throughout the world using techniques for analyzing large-scale data. Methods ‘#PlasticSurgery’ was used to search public Instagram posts. Metadata was collected from posts between December 2018 and August 2020. In addition to descriptive analysis, we created two instruments to characterize textual data: a multi-lingual dictionary of procedural hashtags and a rule-based text classification model to categorize the source of the post. Results Plastic Surgery content yielded more than 2 million posts, 369 million likes, and 6 billion views globally over the 21-month study. The United States had the most posts of 182 countries studied (26.8%, 566,206). Various other regions had substantial presence including Istanbul, Turkey, which led all cities (4.8%, 102,208). Our classification model achieved high accuracy (94.9%) and strong agreement with independent raters (κ= 0.88). Providers accounted for 40% of all posts (847,356) and included Physician (28%), Plastic Surgery (9%), Advanced-Practice-Practitioners and Nurses (1.6%), Facial Plastics (1.3%), and Oculoplastics (0.2%). Content between Plastics and non-Plastics groups demonstrated high textual similarity, and only 1.4% of posts had a verified source. Conclusions Plastic Surgery content has immense global reach in social media. Textual similarity between groups coupled with the lack of an effective verification mechanism presents challenges in discerning the source and veracity of information.

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