Analysis of Online Social Networks for the Identification of Sarcasm

2018 ◽  
pp. 92-105 ◽  
Author(s):  
Pulkit Mehndiratta
Keyword(s):  
Social Networks ◽  
Social Network ◽  
Social Network Analysis ◽  
Network Analysis ◽  
Sentiment Analysis ◽  
Online Social Networks ◽  
Future Research ◽  
Research Directions ◽  
Textual Data ◽  
Future Research Directions

With the ever-increasing acceptance of online social networks (OSNs), a new dimension has evolved for communication amongst humans. OSNs have given us the opportunity to monitor and mine the opinions of a large number of online active populations in real time. Many diverse approaches have been proposed, various datasets have been generated, but there is a need of collective understanding of this area. Researchers are working around the globe to find a pattern to judge the mood of the user; the still serious problem of detection of irony and sarcasm in textual data poses a threat to the accuracy of the techniques evolved till date. This chapter aims to help the reader to think and learn more clearly about the aspects of sentiment analysis, social network analysis, and detection of irony or sarcasm in textual data generated via online social networks. It argues and discusses various techniques and solutions available in literature currently. In the end, the chapter provides some answers to the open-ended question and future research directions related to the analysis of textual data.

scholarly journals A Survey on Computational Propaganda Detection

2020 ◽  
Author(s):  
Giovanni Da San Martino ◽  
Stefano Cresci ◽  
Alberto Barrón-Cedeño ◽  
Seunghak Yu ◽  
Roberto Di Pietro ◽  
...  
Keyword(s):  
Social Networks ◽  
Social Network ◽  
Network Analysis ◽  
Language Processing ◽  
State Of The Art ◽  
Future Research ◽  
The Internet ◽  
Persuasive Messages ◽  
Research Directions ◽  
Future Research Directions

Propaganda campaigns aim at influencing people's mindset with the purpose of advancing a specific agenda. They exploit the anonymity of the Internet, the micro-profiling ability of social networks, and the ease of automatically creating and managing coordinated networks of accounts, to reach millions of social network users with persuasive messages, specifically targeted to topics each individual user is sensitive to, and ultimately influencing the outcome on a targeted issue. In this survey, we review the state of the art on computational propaganda detection from the perspective of Natural Language Processing and Network Analysis, arguing about the need for combined efforts between these communities. We further discuss current challenges and future research directions.

scholarly journals Social Network Analysis Visualization

2015 ◽  
pp. 257-270
Author(s):  
Mohana Shanmugam ◽  
Yusmadi Yah Jusoh ◽  
Rozi Nor Haizan Nor ◽  
Marzanah A. Jabar
Keyword(s):  
Social Networks ◽  
Social Network ◽  
Social Network Analysis ◽  
Network Analysis ◽  
Online Social Network ◽  
Future Research ◽  
Research Gaps ◽  
The Social ◽  
Network Studies ◽  
Academic Study

The social network surge has become a mainstream subject of academic study in a myriad of disciplines. This chapter posits the social network literature by highlighting the terminologies of social networks and details the types of tools and methodologies used in prior studies. The list is supplemented by identifying the research gaps for future research of interest to both academics and practitioners. Additionally, the case of Facebook is used to study the elements of a social network analysis. This chapter also highlights past validated models with regards to social networks which are deemed significant for online social network studies. Furthermore, this chapter seeks to enlighten our knowledge on social network analysis and tap into the social network capabilities.

An Experimental Evaluation of Link Prediction for Movie Suggestions Using Social Media Content

2018 ◽  
pp. 203-230
Author(s):  
Anu Taneja ◽  
Bhawna Gupta ◽  
Anuja Arora
Keyword(s):  
Social Media ◽  
Social Network ◽  
Social Network Analysis ◽  
Network Analysis ◽  
Online Social Networks ◽  
Experimental Evaluation ◽  
Link Prediction ◽  
Common Neighbor ◽  
Research Directions ◽  
Definition Of

The enormous growth and dynamic nature of online social networks have emerged to new research directions that examine the social network analysis mechanisms. In this chapter, the authors have explored a novel technique of recommendation for social media and used well known social network analysis (SNA) mechanisms-link prediction. The initial impetus of this chapter is to provide general description, formal definition of the problem, its applications, state-of-art of various link prediction approaches in social media networks. Further, an experimental evaluation has been made to inspect the role of link prediction in real environment by employing basic common neighbor link prediction approach on IMDb data. To improve performance, weighted common neighbor link prediction (WCNLP) approach has been proposed. This exploits the prediction features to predict new links among users of IMDb. The evaluation shows how the inclusion of weight among the nodes offers high link prediction performance and opens further research directions.

New Developments in Social Network Analysis

2021 ◽  
Vol 9 (1) ◽  
Author(s):  
Daniel J. Brass
Keyword(s):  
Social Networks ◽  
Social Network ◽  
Social Network Analysis ◽  
Network Analysis ◽  
Organizational Behavior ◽  
Annual Review ◽  
Publication Date ◽  
Research Directions ◽  
New Research ◽  
Conceptual Foundations

This review of social network analysis focuses on identifying recent trends in interpersonal social networks research in organizations, and generating new research directions, with an emphasis on conceptual foundations. It is organized around two broad social network topics: structural holes and brokerage and the nature of ties. New research directions include adding affect, behavior, and cognition to the traditional structural analysis of social networks, adopting an alter-centric perspective including a relational approach to ego and alters, moving beyond the triad in structural hole and brokerage research to consider alters as brokers, expanding the nature of ties to include negative, multiplex/dissonant, and dormant ties, and exploring the value of redundant ties. The challenge is to answer the question “What's next in social network analysis?” Expected final online publication date for the Annual Review of Organizational Psychology and Organizational Behavior, Volume 9 is January 2022. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.

scholarly journals A Review on Epidemiological Methods to Detect Untrue Information

2021 ◽  
Vol 10 (11) ◽  
pp. 16-19
Author(s):  
Akanksha Mathur ◽  
◽  
Prof. C. P. Gupta ◽  
Keyword(s):  
Social Networks ◽  
Online Social Networks ◽  
Epidemiological Methods ◽  
Future Research ◽  
Epidemiological Models ◽  
Research Directions ◽  
Diffusion Of Information ◽  
Prime Concern ◽  
History Of ◽  
Future Research Directions

Online propagation of untrue information has been and is becoming an increasing problem. Understanding and modeling the diffusion of information on Online Social Networks (OSN's) of voluminous data is the prime concern. The paper provides the history of the epidemic spread and its analogy with untrue information. This paper provides a review of untrue information on online social networks and methods of detection of untrue information based on epidemiological models. Open research challenges and potential future research directions are also highlighted. The paper aimed at aiding research for the identification of untrue information on OSNs.

An Exploratory Analysis Using Co-Authorship Network

2019 ◽  
pp. 166-200
Author(s):  
Burçin Güçlü ◽  
Miguel Ángel Canela ◽  
Inés Alegre
Keyword(s):  
Social Networks ◽  
Social Network ◽  
Social Network Analysis ◽  
Network Analysis ◽  
Organizational Behavior ◽  
Human Behavior ◽  
Online Social Networks ◽  
World Wide ◽  
Academic Research ◽  
E Mail

Social network analysis has been widely used by organizational behavior researchers to stress the importance of the context, social connections, and social structure on human behavior. In the last decade, social network analysis has emerged as one of the most useful techniques for exploring online social networks, world wide web, e-mail traffic, and logistic operations. In this chapter, the authors present an application of social network analysis techniques for academic research. The authors choose Kahneman and Tversky's prospect theory as the focus of their analysis and, based on that, develop a co-authorship structure that depicts in a clear manner the key authors and/or the researchers that dominate and bridge different sub-fields in the field of management. The authors discuss the implications of this study for academic research and management discipline.

Online Social Network Analysis

2019 ◽  
pp. 50-63
Author(s):  
Praveen Kumar Bhanodia ◽  
Aditya Khamparia ◽  
Babita Pandey ◽  
Shaligram Prajapat
Keyword(s):  
Social Networks ◽  
Social Network ◽  
Social Network Analysis ◽  
Network Analysis ◽  
Online Social Networks ◽  
Online Social Network ◽  
Structural Features ◽  
Network Properties ◽  
Prediction Systems ◽  
And Behavior

Expansion of online social networks is rapid and furious. Millions of users are appending to it and enriching the nature and behavior, and the information generated has various dimensional properties providing new opportunities and perspective for computation of network properties. The structure of social networks is comprised of nodes and edges whereas users are entities represented by node and relationships designated by edges. Processing of online social networks structural features yields fair knowledge which can be used in many of recommendation and prediction systems. This is referred to as social network analysis, and the features exploited usually are local and global both plays significant role in processing and computation. Local features include properties of nodes like degree of the node (in-degree, out-degree) while global feature process the path between nodes in the entire network. The chapter is an effort in the direction of online social network analysis that explores the basic methods that can be process and analyze the network with a suitable approach to yield knowledge.

Games and Social Networks

2013 ◽  
pp. 204-219
Author(s):  
Yulia Bachvarova ◽  
Stefano Bocconi
Keyword(s):  
Social Networks ◽  
Social Media ◽  
Social Network ◽  
Future Research ◽  
Network Games ◽  
Research Directions ◽  
Game Mechanics ◽  
Social Tv ◽  
Future Research Directions ◽  
Game Industry

Social media and social networks have gained an unprecedented role in connecting people, knowledge, and experiences. Game industry is using the power of social networks by creating Social Network Games, which can be even more engaging than traditional games. In this chapter, the main characteristics of Social Network Games and their potential are discussed. This potentiality can also be used for serious games (i.e. games with purposes beyond entertainment) and especially games related to learning and behavioural changes. This leads to introducing the emerging field of Serious Social Network Games and their unique characteristics that make them suitable for serious applications. Finally, the rising phenomenon of Social TV is discussed, which combines the power of TV and social media. Based on a project by the authors, preliminary findings on the most engaging techniques of Social TV Games are presented, together with initial suggestions on what constitutes good game mechanics for such games. The chapter concludes with future research directions for Social Network Games to become even more engaging and effective for purposes beyond pure entertainment.

scholarly journals Trends and perspectives on the use of social network analysis in behavioural ecology: a bibliometric approach

2018 ◽  
Author(s):  
Quinn M.R. Webber ◽  
Eric Vander Wal
Keyword(s):  
Social Networks ◽  
Social Network ◽  
Social Network Analysis ◽  
Network Analysis ◽  
Temporal Trends ◽  
Future Research ◽  
Network Research ◽  
Emerging Trends ◽  
Primary Finding ◽  
Over Time

AbstractThe increased popularity and improved accessibility of social network analysis has improved our ability to test hypotheses about the complexity of animal social structure. To gain a deeper understanding of the use and application of social network analysis, we systematically surveyed the literature and extracted information on publication trends from articles using social network analysis. We synthesize trends in social network research over time and highlight variation in the use of different aspects of social network analysis. Our primary finding highlights the increase in use of social network analysis over time and from this finding, we observed an increase in the number of review and methods of social network analysis. We also found that most studies included a relatively small number (median = 15, range = 4–1406) of individuals to generate social networks, while the number and type of social network metrics calculated in a given study varied zero to nine (median = 2, range 0–9). The type of data collection or the software programs used to analyze social network data have changed; SOCPROG and UCINET have been replaced by various R packages over time. Finally, we found strong taxonomic and conservation bias in the species studied using social network analysis. Most species studied using social networks are mammals (111/201, 55%) or birds (47/201, 23%) and the majority tend to be species of least concern (119/201, 59%). We highlight emerging trends in social network research that may be valuable for distinct groups of social network researchers: students new to social network analysis, experienced behavioural ecologists interested in using social network analysis, and advanced social network users interested in trends of social network research. In summary we address the temporal trends in social network publication practices, highlight potential bias in some of the ways we employ social network analysis, and provide recommendations for future research based on our findings.

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