scholarly journals Near Real-Time Detection of Misinformation on Online Social Networks

2020 ◽  
pp. 246-260
Author(s):  
Lennart van de Guchte ◽  
Stephan Raaijmakers ◽  
Erik Meeuwissen ◽  
Jennifer Spenader
Keyword(s):  
Social Networks ◽  
Real Time ◽  
Online Social Networks ◽  
Real Time Detection

scholarly journals Real time enhanced random sampling of online social networks

2014 ◽  
Vol 41 ◽  
pp. 126-134 ◽  
Author(s):  
Giannis Haralabopoulos ◽  
Ioannis Anagnostopoulos
Keyword(s):  
Social Networks ◽  
Real Time ◽  
Online Social Networks ◽  
Random Sampling

scholarly journals Tracing You: How transparent surveillance reveals a desire for visibility

2017 ◽  
Vol 4 (1) ◽  
pp. 205395171769405
Author(s):  
Benjamin Grosser
Keyword(s):  
Social Networks ◽  
Real Time ◽  
Online Social Networks ◽  
Physical Environment ◽  
Surveillance System ◽  
Data Sources ◽  
Online Data ◽  
Ip Addresses ◽  
The Earth ◽  
The World

Tracing You is an artwork that presents a website's best attempt to see the world from its visitors’ viewpoints. By cross referencing visitor IP addresses with available online data sources, the work traces each visitor back through the network to its possible origin. The end of that trace is the closest available image that potentially shows the visitor’s physical environment. Sometimes what this image shows is eerily accurate; other times it is wildly dislocated. This computational surveillance system thus makes transparent the potential visibility of one’s present location on the Earth, while also giving each site visitor the ability to watch other visitor “traces” in real time. By making its surveillance capacity and intention overt, Tracing You provokes questions about the architecture of networks and how that architecture affects our own visibility both within and outside of the network. Further, reactions to the work reveal attitudes towards surveillance post-Snowden, including, in some cases, an angry desire for more visibility than Tracing You currently provides. This commentary describes how the artwork functions, presents and discusses visitor reactions, and briefly theorizes origins for these reactions within the contexts of surveillance, sousveillance, and transparency in the age of ubiquitous online social networks.

scholarly journals Real-Time Stream Processing in Social Networks with RAM3S

2019 ◽  
Vol 11 (12) ◽  
pp. 249 ◽  
Author(s):  
Ilaria Bartolini ◽  
Marco Patella
Keyword(s):  
Social Networks ◽  
Big Data ◽  
Data Analysis ◽  
Real Time ◽  
Online Social Networks ◽  
Distributed Processing ◽  
Stream Processing ◽  
Multimedia Data ◽  
Process Data ◽  
Real Time Processing

The avalanche of (both user- and device-generated) multimedia data published in online social networks poses serious challenges to researchers seeking to analyze such data for many different tasks, like recommendation, event recognition, and so on. For some such tasks, the classical “batch” approach of big data analysis is not suitable, due to constraints of real-time or near-real-time processing. This led to the rise of stream processing big data platforms, like Storm and Flink, that are able to process data with a very low latency. However, this complicates the task of data analysis since any implementation has to deal with the technicalities of such platforms, like distributed processing, synchronization, node faults, etc. In this paper, we show how the RAM 3 S framework could be profitably used to easily implement a variety of applications (such as clothing recommendations, job suggestions, and alert generation for dangerous events), being independent of the particular stream processing big data platforms used. Indeed, by using RAM 3 S, researchers can concentrate on the development of their data analysis application, completely ignoring the details of the underlying platform.

Analysing Twitter Data for Phishing Tweets Identification

2021 ◽  
Vol 17 (2) ◽  
pp. 96-106
Author(s):  
Falah Hassan Ali Al-Akashi
Keyword(s):  
Social Networks ◽  
Real Time ◽  
Online Social Networks ◽  
Crucial Issue ◽  
Word Level ◽  
Twitter Data ◽  
Preliminary Study ◽  
And Performance ◽  
High Level ◽  
Level Analysis

Detecting threats like adult, violent, and phishing tweets on online social networks is a crucial issue in recent years. The aim of the work is to identify phishing content from the users' perspective in real-time tweets. To outline such content comprehensively, lexicon analysis with sentiments are encapsulated to investigate tweets that yield phishing dynamic keywords, while some features and parameters are altered to optimize the performance. To support the preliminary study, the approach is rigorously designed to assemble users' opinions on completely different classes of phishing content. Each direct and indirect opinions as well as recently projected opinions are listed to characterize all sorts of phishing content. The authors use word level analysis with sentiments to build keyword blacklist lexicons. High promising results and high level of accuracy and performance are obtained experimentally if compared with the alternative algorithms.

Hybrid Real-Time Protection System for Online Social Networks

2019 ◽  
Vol 25 (4) ◽  
pp. 1095-1124 ◽  
Author(s):  
Muneer Bani Yassein ◽  
Shadi Aljawarneh ◽  
Yarub Wahsheh
Keyword(s):  
Social Networks ◽  
Real Time ◽  
Online Social Networks ◽  
Protection System
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