scholarly journals The Impact of Social Media Big Data on Organizations

◽  
2019 ◽  
Keyword(s):  
Social Media ◽  
Big Data ◽  
The Impact

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.

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 Mengajak Generasi Z SMA 1 Muhammadiyah Malang Berinternet Secara Bijak

2020 ◽  
Vol 4 (2) ◽  
Author(s):  
Hamdan Nafiatur Rosyida ◽  
Demeiati Nur Kusumaningrum ◽  
Palupi Anggraheni
Keyword(s):  
Social Media ◽  
Big Data ◽  
Cyber Security ◽  
Digital Literacy ◽  
Service Providers ◽  
Social Phenomena ◽  
Internet Service ◽  
Negative Impacts ◽  
Use Of Social Media ◽  
The Impact

ABSTRAKHasil survei oleh Asosiasi Penyelenggara Jasa Internet Indonesia (APJII) pada tahun 2016 menunjukkan bahwa 51,8 persen dari 256,2 juta penduduk Indonesia merupakan pengguna internet dan 47,6 persennya menggunakan internet melalui gawai pribadi. Meskipun demikian, fenomena sosial mencatat tidak semua individu dapat menggunakan media sosial secara bijak. Hal itulah yang menjadi semangat kemitraan tim UMM dengan SMA 1 Muhammadiyah Malang dalam program pelatihan literasi digital. Pemahaman siswa tentang literasi digital dalam penggunaan media sosial masih minim. Siswa menguasai penggunaan media sosial popular seperi Instagram, Twitter maupun Facebook namun dampak media sosial seperti munculnya hoax, fenomena ‘firehose falsehood’ maupun efek domino lainnya belum terlalu mendapat perhatian. Program literasi digital dilaksanakan melalui 2 (dua) format. Pertama, Seminar dan Talkshow Literasi Digital yang terdiri dari topik perkembangan terkini media social, pengenalan tentang logika big data yang menentukan tajuk komposisi berita, dan pengenalan keamanan digital (cyber security). Kedua, sosialisasi berinternet secara bijak menggunakan instrumen buku saku (booklet) yang bertujuan memberikan pemahaman bagi siswa mengenai bagaimana penggunaan sosial media dan dampak positif negatif dalam berbagai perspektif studi kasus.Kata Kunci: internet; literasi; millenial; pelatihan; remaja Invites Generation Z of Muhammadiyah Malang 1 High School to Internet WiselyABSTRACTThe results of a survey by the Indonesian Internet Service Providers Association (APJII) in 2016 showed that 51.8 percent of 256.2 million Indonesians were internet users and 47.6 percent used the internet through private devices. However, social phenomena noted that not all individuals can use social media wisely. That was the spirit of the partnership between UMM and SMA 1 Muhammadiyah Malang in the digital literacy training program. Students' understanding of digital literacy in the use of social media is still minimal. Students master the use of popular social media like Instagram, Twitter and Facebook but the impact of social media such as the emergence of hoaxes, the phenomenon of 'firehose falsehood' and other domino effects have not received much attention. Digital literacy program is carried out in 2 (two) formats. First, the Seminar and Digital Literacy Talkshow which consists of the latest developments in social media, the introduction of the logic of big data that determines the headline of news composition, and the introduction of digital security (cyber security). Second, internet socialization wisely uses a booklet instrument which aims to provide students with an understanding of how social media is used and its positive and negative impacts in a variety of case study perspectives.Keywords: internet; literacy; millennial; training; teenager

Big Data and Democracy

2020 ◽  
Keyword(s):  
Decision Making ◽  
Social Media ◽  
Big Data ◽  
Data Collection ◽  
Political Campaigns ◽  
Original Research ◽  
Targeted Advertising ◽  
Echo Chambers ◽  
Political Turmoil ◽  
The Impact

This edited collection tackles subjects that have arisen as a result of new capabilities to collect, analyse and use vast quantities of data using complex algorithms. Questions tackled include what is wrong with targeted advertising in political campaigns, whether echo chambers really are a matter of genuine concern, what is the impact of data collection through social media and other platforms on questions of trust in society and is there a problem of opacity as decision-making becomes increasingly automated? The contributors consider potential solutions to these challenges and discuss whether an ethical compass is available or even feasible in an ever more digitized and monitored world. The editors bring together original research on the philosophy of big data and democracy from leading international authors, with recent examples and case references – including the 2016 Brexit Referendum, the Leveson Inquiry and the Edward Snowden leaks – and combine them in one authoritative volume at time of great political turmoil.

scholarly journals Accounting, accountability, social media and big data: revolution or hype?

2017 ◽  
Vol 30 (4) ◽  
pp. 762-776 ◽  
Author(s):  
Michela Arnaboldi ◽  
Cristiano Busco ◽  
Suresh Cuganesan
Keyword(s):  
Social Media ◽  
Big Data ◽  
Future Research ◽  
Data Governance ◽  
Content Type ◽  
Knowledge Based ◽  
Decision Making Processes ◽  
Key Aspects ◽  
The Impact

Purpose The purpose of this paper is to outline an agenda for researching the relationship between technology-enabled networks – such as social media and big data – and the accounting function. In doing so, it links the contents of an unfolding area research with the papers published in this special issue of Accounting, Auditing and Accountability Journal. Design/methodology/approach The paper surveys the existing literature, which is still in its infancy, and proposes ways in which to frame early and future research. The intention is not to offer a comprehensive review, but to stimulate and conversation. Findings The authors review several existing studies exploring technology-enabled networks and highlight some of the key aspects featuring social media and big data, before offering a classification of existing research efforts, as well as opportunities for future research. Three areas of investigation are identified: new performance indicators based on social media and big data; governance of social media and big data information resources; and, finally, social media and big data’s alteration of information and decision-making processes. Originality/value The authors are currently experiencing a technological revolution that will fundamentally change the way in which organisations, as well as individuals, operate. It is claimed that many knowledge-based jobs are being automated, as well as others transformed with, for example, data scientists ready to replace even the most qualified accountants. But, of course, similar claims have been made before and therefore, as academics, the authors are called upon to explore the impact of these technology-enabled networks further. This paper contributes by starting a debate and speculating on the possible research agendas ahead.

scholarly journals How fear and collectivism influence public's preventive intention towards COVID-19 infection: A study based on big data from the social media

2020 ◽  
Author(s):  
Feng Huang ◽  
Huimin Ding ◽  
Zeyu Liu ◽  
Peijing Wu ◽  
Meng Zhu ◽  
...  
Keyword(s):  
Social Media ◽  
Big Data ◽  
Cultural Values ◽  
Multiple Regression Model ◽  
Positive Influence ◽  
Combined Effect ◽  
Slope Test ◽  
Simple Slope ◽  
Precautionary Measures ◽  
The Impact

Abstract Background: Despite worldwide calls for precautionary measures to combat COVID-19, the public's preventive intention still varies significantly among different regions. Exploring the influencing factors of the public's preventive intention is very important to curtail the spread of COVID-19. Previous studies have found that fear can effectively improve the public's preventive intention, but they ignore the impact of differences in cultural values. The present study examines the combined effect of fear and collectivism on the public's preventive intention towards COVID-19 through the analysis of social media big data.Methods: The Sina microblog posts of 108,914 active users from Chinese mainland 31 provinces were downloaded. The data was retrieved from January 11 to February 21 2020. Afterwards, we conducted a province-level analysis of the contents of downloaded posts. Three lexicons were applied to automatically recognise the scores of fear, collectivism, and preventive intention of 31 provinces. After that, a multiple regression model was established to examine the combined effect of fear and collectivism on the public's preventive intention towards COVID-19. The simple slope test and the Johnson-Neyman technique were used to test the interaction of fear and collectivism on preventive intention.Results: The study reveals that: (a) both fear and collectivism can positively predict people's preventive intention and (b) there is an interaction of fear and collectivism on people's preventive intention, where fear and collectivism reduce each other's positive influence on people's preventive intention.Conclusion: The promotion of fear on people's preventive intention may be limited and conditional, and values of collectivism can well compensate for the promotion of fear on preventive intention. These results provide scientific inspiration on how to enhance the public's preventive intention towards COVID-19 effectively.

The impact of digitalization on business models

2018 ◽  
Vol 20 (2) ◽  
pp. 105-124 ◽  
Author(s):  
Harry Bouwman ◽  
Shahrokh Nikou ◽  
Francisco J. Molina-Castillo ◽  
Mark de Reuver
Keyword(s):  
Social Media ◽  
Big Data ◽  
Case Studies ◽  
Business Performance ◽  
Business Models ◽  
Positive Impact ◽  
Organizational Factors ◽  
Content Type ◽  
Use Of Social Media ◽  
The Impact

Purpose This paper aims to explore how digital technologies have forced small- to medium-sized enterprises (SMEs) to reconsider and experiment with their business models (BMs) and how this contributes to their innovativeness and performance. Design/methodology/approach An empirical study has been conducted on 338 European SMEs actively using social media and big data to innovate their BMs. Four in-depth case studies of companies involved in BM innovation have also been carried out. Findings Findings show that the use of social media and big data in BMI is mainly driven by strategic and innovation-related internal motives. External technology turbulence plays a role too. BMI driven by social media and big data has a positive impact on business performance. Analysis of the case studies shows that BM is driven by big data rather than by social media. Research limitations/implications Research into big data- and social media-driven BMs needs more insight into how components are affected and how SMEs are experimenting with adjusting their BMs, specifically in terms of human and organizational factors. Practical implications Findings of this study can be used by managers and top-level executives to better understand how firms experiment with BMI, what affects business model components and how implementation might affect BMI performance. Originality/value This paper is one of the first research contributions to analyse the impact of digitalization, specifically the impact of social media and big data on a large number of European SMEs.

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.

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