scholarly journals Truth Discovery in Big Data Social Media Sensing Applications

2020 ◽  
Vol 9 (8) ◽  
pp. 999-1004
Keyword(s):  
Social Media ◽  
Big Data ◽  
False Information ◽  
Distributed Structure ◽  
Sensing Applications ◽  
Online Social Media ◽  
Truth Discovery ◽  
Novel Approach ◽  
Work Queue ◽  
The Impact

The detection of truthful information amid data provided by online social media platforms (e.g., Twitter, Facebook, Instagram) is a critical task in the trend of big data. Truth Discovery is nothing but the extraction of true information or facts from unwanted and raw data, which has become a difficult task nowadays in today's day and age due to the rampant spread of rumors and false information. Before posting anything on the social media platform, people do not consider fact-checking and the source authenticity and frantically spread them by re-posting them which has made the detection of truthful claims more difficult than ever. So, this problem needs to be addressed soon since the impact of false information and misunderstanding can be very powerful and misleading. This mission, truth discovery, is targeted at establishing the authenticity of the sources and therefore the truthfulness of the statements that they create without knowing whether it is true or not. We propose a Big Data Truth Discovery Scheme (BDTD) to overcome the major problems. We have three major problems, the main one being "False information spread" where a large number of sources lead to false or fake statements, making it difficult to distinguish true statements, now this problem is solved by our scheme by studying the various behaviors of sources. On Twitter for example rumormongering is common. The second problem is "lack of claims" where most outlets contribute only a tiny small number of claims, giving very few pieces of evidence and making it not sufficient to analyze the trustworthiness of such sources, this problem is addressed by our scheme where it uses an algorithm that evaluates the claim’s truthfulness and historic contributions of the source regarding the claim. Thirdly the scalability challenge, due to the clustered design of their existing truth discovery algorithms, many existing approaches don't apply to Big-scale social media sensing cases so this challenge is managed by our scheme by making use of frameworks HTCondor and Work Queue. This scheme computes both the reliability of the sources and, ultimately, the legitimacy of statements using a novel approach. A distributed structure is also developed for the implementation of the proposed scheme by making use of the Work Queue (platform) in the HTCondor method (maybe distributed). Findings of the test on a real-world dataset indicate that the BDTD system greatly outperforms the existing methods of Discovery of Truth both in terms of performance and efficiency.

scholarly journals Detection of Truth Discovery in Big Data Social Media Sensing Applications

2019 ◽  
Vol 9 (2) ◽  
pp. 1502-1507
Keyword(s):  
Social Media ◽  
Big Data ◽  
Sensing Applications ◽  
Truth Discovery

With ithe irapid igrowth iof ionline isocial imedia iand iubiquitous iInternet iconnectivity, isocial isensing ihas iemerged ias ia inew icrowd isourcing iapplication iparadigm iof icollecting iobservations i(often icalled iclaims) iabout ithe iphysical ienvironment ifrom ihumans ior idevices ion itheir ibehalf. iA ifundamental iproblem iin isocial isensing iapplications iliesiin ieffectively iascertaining ithe icorrectness iof iclaims iand ithe ireliability iof idata isources iwithout iknowing ieither iof ithem ia ipriori, iwhich iis ireferred ito ias itruth idiscovery. iWhile isignificant iprogress ihas ibeen imade ito isolve ithe itruth idiscovery iproblem, isome iimportant ichallenges ihave inot ibeen iwell iaddressed iyet. iFirst, iexisting itruth idiscovery isolutions idid inot ifully isolve ithe idynamic itruth idiscovery iproblem iwhere ithe iground itruth iof iclaims ichanges iover itime. iSecond, imany icurrent isolutions iare inot iscalable ito ilarge-scale isocial isensing ievents ibecause iof ithe icentralized inature iof itheir itruth idiscovery ialgorithms. iThird, ithe iheterogeneity iand iunpredictability iof ithe isocial isensing idata itraffic ipose iadditional ichallenges ito ithe iresource iallocation iand isystem iresponsiveness. iIn ithis ipaper, iwe idevelop ia iScalable iand iRobust iTruth iDiscovery i(SRTD) ischeme ito iaddress ithe iabove ithree ichallenges. iIn iparticular, ithe iSRTD ischeme ijointly iquantifies iboth ithe ireliability iof isources iand ithe icredibility iof iclaims iusing ia iprincipled iapproach. iThe ievaluation iresults ion ithree ireal-world idata itraces i(i.e., iBoston iBombing, iParis iShooting iand iCollege iFootball) ishow ithat ithe iSSTD ischeme iis iscalable iand ioutperforms ithe istate-of-the- iart itruth idiscovery imethods iin iterms iof iboth ieffectiveness iand iefficiency.

scholarly journals On Scalable and Robust Truth Discovery in Big Data Social Media Sensing Applications

2019 ◽  
Vol 5 (2) ◽  
pp. 195-208 ◽  
Author(s):  
Daniel Zhang ◽  
Dong Wang ◽  
Nathan Vance ◽  
Yang Zhang ◽  
Steven Mike
Keyword(s):  
Social Media ◽  
Big Data ◽  
Sensing Applications ◽  
Truth Discovery

The Impact of Big Data on Security

Big Data ◽  
2016 ◽  
pp. 1495-1518
Author(s):  
Mohammad Alaa Hussain Al-Hamami
Keyword(s):  
Social Media ◽  
Big Data ◽  
Management System ◽  
Database Management ◽  
Database Systems ◽  
Structured Data ◽  
Database Management System ◽  
Unstructured Data ◽  
And Behavior ◽  
The Impact

Big Data is comprised systems, to remain competitive by techniques emerging due to Big Data. Big Data includes structured data, semi-structured and unstructured. Structured data are those data formatted for use in a database management system. Semi-structured and unstructured data include all types of unformatted data including multimedia and social media content. Among practitioners and applied researchers, the reaction to data available through blogs, Twitter, Facebook, or other social media can be described as a “data rush” promising new insights about consumers' choices and behavior and many other issues. In the past Big Data has been used just by very large organizations, governments and large enterprises that have the ability to create its own infrastructure for hosting and mining large amounts of data. This chapter will show the requirements for the Big Data environments to be protected using the same rigorous security strategies applied to traditional database systems.

Rumors on the Net: A Brackish Suspension of Speech and Hate

2019 ◽  
pp. 174387211988012 ◽  
Author(s):  
Anne Wagner ◽  
Sarah Marusek
Keyword(s):  
United States ◽  
Social Media ◽  
Hate Speech ◽  
Public Memory ◽  
The United States ◽  
Online Social Media ◽  
Cyber Culture ◽  
Social Media Platforms ◽  
The Impact ◽  
Mob Mentality

The legitimacy of public memory and socially normative standards of civility is questioned through rumors that abound on online social media platforms. On the Net, the proclivity of rumors is particularly prone to acts of bullying and frameworks of hate speech. Legislative attempts to limit rumors operate differently in France and throughout Europe from the United States. This article examines the impact of online rumors, the mob mentality, and the politicization of bullying critics within a cyber culture that operates within the limitations of law.

scholarly journals A Hybrid Deep Learning Model to Predict the Impact of COVID-19 on Mental Health form Social Media Big Data

2021 ◽  
Author(s):  
Tapotosh Ghosh ◽  
Md. Hasan Al Banna ◽  
Md. Jaber Al Nahian ◽  
Kazi Abu Taher ◽  
M Shamim Kaiser ◽  
...  
Keyword(s):  
Neural Network ◽  
Mental Health ◽  
Social Media ◽  
Big Data ◽  
Language Processing ◽  
Short Term Memory ◽  
Daily Basis ◽  
Significant Rise ◽  
Special Focus ◽  
The Impact

The novel coronavirus disease (COVID-19) pandemic is provoking a prevalent consequence on mental health because of less interaction among people, economic collapse, negativity, fear of losing jobs, and death of the near and dear ones. To express their mental state, people often are using social media as one of the preferred means. Due to reduced outdoor activities, people are spending more time on social media than usual and expressing their emotion of anxiety, fear, and depression. On a daily basis, about 2.5 quintillion bytes of data are generated on social media, analyzing this big data can become an excellent means to evaluate the effect of COVID-19 on mental health. In this work, we have analyzed data from Twitter microblog (tweets) to find out the effect of COVID-19 on peoples mental health with a special focus on depression. We propose a novel pipeline, based on recurrent neural network (in the form of long-short term memory or LSTM) and convolutional neural network, capable of identifying depressive tweets with an accuracy of 99.42%. Preprocessed using various natural language processing techniques, the aim was to find out depressive emotion from these tweets. Analyzing over 571 thousand tweets posted between October 2019 and May 2020 by 482 users, a significant rise in depressing tweets was observed between February and May of 2020, which indicates as an impact of the long ongoing COVID-19 pandemic situation.

scholarly journals Analysis of People Response on Twitter Towards Tidal Wave Disaster in the Southern Coast of Yogyakarta Special Province (Case Studies: Parangtritis Beach, Bantul Regency)

2019 ◽  
Author(s):  
Satria Indratmoko ◽  
Inayah Bastin Al Hakim ◽  
Wahyu Satrio Guntoro
Keyword(s):  
Social Media ◽  
Indian Ocean ◽  
Tidal Wave ◽  
Media Usage ◽  
Online Social Media ◽  
Analysis Theory ◽  
The Indian Ocean ◽  
Tourism Activity ◽  
The Impact ◽  
Dangerous Condition

In July 2018 the movement of the wind from Australia to the Indian Ocean gives the impact on season transition from rainy to dry season. As the result, the wave becomes so much higher than normal condition as it hits the coastal area as well as in the southern part of Yogyakarta Special Province where is directly bordered with the Indian Ocean. Some impacted areas are popular tourism spots like Parangtritis Beach. The wave wrecks several shops along the beach owned by the local people. The majority of damaged objects are semi-permanent buildings constructed by traditional bamboo and timber. Moreover the tourism activity has been warned due to the dangerous condition. The advancement of technology becomes one of popular issues including the increasing of online social media usage. Internet and gadgets such as smartphone are recently the part of people lifestyle. The nowadays people prefer to access anything online through their smartphone including to find the news on the website or social media such as Twitter. One of interested news is about disaster particularly in recognizable places as well as about tidal wave disaster in Parangtritis Beach. This study aims to investigate the advantages of Twitter contents related to the tidal wave in Parangtritis Beach on people response about the disaster and the beach. The analysis applies sentiment analysis theory. Furthermore the data being collected in this research is online from Twitter accounts that has divided into three phases of disaster (before tidal wave, during tidal wave, and after tidal wave).

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