Fighting fake news spread in online social networks: Actual trends and future research directions

2017 ◽  
Author(s):  
Alina Campan ◽  
Alfredo Cuzzocrea ◽  
Traian Marius Truta
Keyword(s):  
Social Networks ◽  
Online Social Networks ◽  
Future Research ◽  
Fake News ◽  
Research Directions ◽  
Future Research Directions

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.

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.

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.

Deepfake videos: synthesis and detection techniques –a survey

2021 ◽  
pp. 1-21
Author(s):  
Shahela Saif ◽  
Samabia Tehseen
Keyword(s):  
Social Systems ◽  
Generative Models ◽  
Detection Methods ◽  
Future Research ◽  
Fake News ◽  
Research Directions ◽  
Detection Techniques ◽  
Future Research Directions ◽  
Video Creation ◽  
The One

Deep learning has been used in computer vision to accomplish many tasks that were previously considered too complex or resource-intensive to be feasible. One remarkable application is the creation of deepfakes. Deepfake images change or manipulate a person’s face to give a different expression or identity by using generative models. Deepfakes applied to videos can change the facial expressions in a manner to associate a different speech with a person than the one originally given. Deepfake videos pose a serious threat to legal, political, and social systems as they can destroy the integrity of a person. Research solutions are being designed for the detection of such deepfake content to preserve privacy and combat fake news. This study details the existing deepfake video creation techniques and provides an overview of the deepfake datasets that are publicly available. More importantly, we provide an overview of the deepfake detection methods, along with a discussion on the issues, challenges, and future research directions. The study aims to present an all-inclusive overview of deepfakes by providing insights into the deepfake creation techniques and the latest detection methods, facilitating the development of a robust and effective deepfake detection solution.

Play It Like Beckham!

2017 ◽  
pp. 43-61
Author(s):  
Sylvaine Castellano ◽  
Insaf Khelladi
Keyword(s):  
Social Networks ◽  
Social Media ◽  
Academic Research ◽  
Future Research ◽  
The Internet ◽  
Research Directions ◽  
Media Usage ◽  
Future Research Directions ◽  
The Impact

New opportunities and challenges are emerging thanks to the growing Internet importance and social media usage. Although practitioners have already recognized the strategic dimension of e-reputation and the power of social media, academic research is still in its infancy when it comes to e-reputation determinants in a social networks context. A study was conducted in the sports setting to explore the impact of social networks on the sportspeople's e-reputation. Whereas the study emphasized (1) the influence of social networks' perception on the sportspeople's e-reputation, and the neutral roles of (2) the motives for following sportspeople online, and (3) the negative content on the Internet, additional insights are formulated on maintaining, restoring and managing e-reputation on social networks. Finally, future research directions are suggested on the role of image to control e-reputation.

scholarly journals Role-Aware Information Spread in Online Social Networks

Entropy ◽  
2021 ◽  
Vol 23 (11) ◽  
pp. 1542
Author(s):  
Alon Bartal ◽  
Kathleen M. Jagodnik
Keyword(s):  
Social Networks ◽  
Online Social Networks ◽  
Future Research ◽  
Hybrid Features ◽  
Complex Process ◽  
Recommendation Algorithms ◽  
Information Spread ◽  
Comprehensive Survey ◽  
Content Recommendation ◽  
Future Research Directions

Understanding the complex process of information spread in online social networks (OSNs) enables the efficient maximization/minimization of the spread of useful/harmful information. Users assume various roles based on their behaviors while engaging with information in these OSNs. Recent reviews on information spread in OSNs have focused on algorithms and challenges for modeling the local node-to-node cascading paths of viral information. However, they neglected to analyze non-viral information with low reach size that can also spread globally beyond OSN edges (links) via non-neighbors through, for example, pushed information via content recommendation algorithms. Previous reviews have also not fully considered user roles in the spread of information. To address these gaps, we: (i) provide a comprehensive survey of the latest studies on role-aware information spread in OSNs, also addressing the different temporal spreading patterns of viral and non-viral information; (ii) survey modeling approaches that consider structural, non-structural, and hybrid features, and provide a taxonomy of these approaches; (iii) review software platforms for the analysis and visualization of role-aware information spread in OSNs; and (iv) describe how information spread models enable useful applications in OSNs such as detecting influential users. We conclude by highlighting future research directions for studying information spread in OSNs, accounting for dynamic user roles.

An Overview of Graph Indexing and Querying Techniques

2011 ◽  
pp. 71-88 ◽  
Author(s):  
Sherif Sakr ◽  
Ghazi Al-Naymat
Keyword(s):  
Social Networks ◽  
Query Language ◽  
Future Research ◽  
Protein Networks ◽  
Research Directions ◽  
Query Graph ◽  
Graph Indexing ◽  
Graph Query ◽  
Graph Query Language ◽  
Future Research Directions

Recently, there has been a lot of interest in the application of graphs in different domains. Graphs have been widely used for data modeling in different application domains such as: chemical compounds, protein networks, social networks and Semantic Web. Given a query graph, the task of retrieving related graphs as a result of the query from a large graph database is a key issue in any graph-based application. This has raised a crucial need for efficient graph indexing and querying techniques. In this chapter, we provide an overview of different techniques for indexing and querying graph databases. An overview of several proposals of graph query language is also given. Finally, we provide a set of guidelines for future research directions.

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