Reinventing the Social Scientist and Humanist in the Era of Big Data: A Perspective from South African Scholars

Burgert A. Senekal, Eduan Kotzé, Susan Brokensha

Description

This book explores the big data evolution by interrogating the notion that big data is a disruptive innovation that appears to be challenging existing epistemologies inthe humanities and social sciences. Exploring various (controversial) facets of big data such as ethics, data power, and data justice, the book attempts to clarify the trajectoryof the epistemology of (big) data-driven science in the humanities and social sciences.

Sujets

• Littérature Sud-Africaine
• Littérature sud-africaine
• Statistics
• Social sciences
• Data acquisition
• Data storage
• Data analysis
• Digital Revolution
• Big data
• Data Science

Informations

• Publié par: UJ Press
• Date de parution: 01 décembre 2019
• Nombre de lectures: 0
• EAN13: 9781928424376
• Langue: English
• Poids de l'ouvrage: 10 Mo

Extrait

R ei nventi n g th e Social Sci entiSt an d h u man iSt i n the eRa of Big data

a PeRSPective fRom South afRican ScholaRS

SusanBROKENSHA EduanKOTZÉ BurgertASENEKAL

R einventing the Social ScientiSt and humaniSt in the eRa of Big data

a PeRSPective fRom South afRican ScholaRS

SusanBROKENSHA EduanKOTZÉ Burgert ASENEKAL

Reinventing the Social Scientist and Humanist in the Era of Big Data:

A Perspective from South African Scholars

Published by Sun Media Bloemfontein (Pty) Ltd.

Imprint: SunBonani Scholar

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First edition 2019

ISBN: 978-1-928424-36-9 (Print) ISBN: 978-1-928424-37-6 (e-book) DOI: https://doi.org/10.18820/9781928424376

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Research, academic and reference works are published under this imprint in print and electronic format.

This printed copy can be ordered directly from: [e-mail] The e-book is available at the following link: https://doi.org/10.18820/9781928424376

Contents Acknowledgements ................................................................................... Foreword ................................................................................................. Introducion.................................................................................................

he (fuzzy) orîgîns of bîg data and Chapter 1 | te dangers of îgnorîng îstory .................................................. 1.1 A messy afaîr ................................................................................................ 1.2 he îstory o (bîg) data storage .................................................................... 1.3 he emergence o statîstîca anaysîs ............................................................... 1.3.1 Bîg busîness, bîg data............................................................................ 1.4 he dîgîta revoutîon and events surroundîng bîg data .................................. 1.5 Bîg data’s îstory essons ................................................................................ 1.5.1 Revolutîon versus evolutîon.................................................................... 1.5.2 he past în bîg data.............................................................................. 1.5.3 Two oundatîonal narratîves ..................................................................

Chapter 2 |Locatîng bîg data în te (dîgîtal) umanîtîes and (computatîonal) socîal scîences ........................................... 2.1 How bîg data îs ramed .................................................................................. 2.1.1 Two domînant metapors...................................................................... 2.1.2 A collaboratîve efort............................................................................. 2.2 Dîgîta umanîsts and computatîona socîa scîentîsts ....................................

yts debunked and lessons learned ...... Chapter 3 |Bîg Data, bîg despaîr: M 3.1 Epîc aîs ........................................................................................................ 3.2 Bîg data essons ............................................................................................. 3.2.1 Lesson 1............................................................................................... 3.2.2 Lesson 2 ............................................................................................... 3.2.3 Lesson 3 ............................................................................................... 3.2.4 Lesson 4 ............................................................................................... 3.2.5 Lesson 5 ............................................................................................... 3.2.6 Lesson 6 ............................................................................................... 3.2.7 Lesson 7 ...............................................................................................

 î îîî 1

6 6 7 10 12 13 16 16 16 18

19 20 21 23 24

29 29 30 30 34 36 37 40 42 43

Bîg Data needs bîg etîcs ........................................................... Chapter 4 | 4.1 Bîg dataaux paso te mîennîum ............................................................... 4.2 A revîew o te îterature ............................................................................... 4.2.1 Current callenges ................................................................................. 4.2.2 he controversy surroundîng uman subjects ........................................... 4.2.3 he publîc-prîvate space conundrum ...................................................... 4.2.4 he culture o înormed consent on te Internet and anonymîsatîon .......... 4.2.5 Etîcs and te problem o representatîveness ............................................ 4.2.6 A new dîgîtal ecosystem ......................................................................... 4.3 Data justîce ...................................................................................................

g data vîsualîsa Chapter 5 |Does bî tîon make our endeavours less umanîstîc? ....................................................... 5.1 Data vîsuaîsatîon, uman cognîtîon, and uman perceptîon ........................ 5.2 Tecnoogy and te umanîtîes ..................................................................... 5.3 Data as capta ................................................................................................. 5.4 he emotîona and socîa pîtas o vîsuaîsatîon ............................................ Vîsuaîsatîon and te probem o data power ............................................................

Chapter 6 |Data power în te era of bîg data: Frîend or foe? ....................... 6.1 Bîg data’s sadow sîde .................................................................................... 6.2 Engaged umanîsts and socîa scîentîsts ......................................................... 6.3 Bîg data as an obstace/brîdge to umanîtarîan projects ................................. 6.4 Sîze revîsîted ..................................................................................................

Chapter 7 |he place of qualîtatîve data analysîs software (QDAS) programmes în a bîg data world .................... 7.1 Sotware programmes and te quaîtatîve researcer ...................................... 7.2 Two ways o tînkîng about QDAS ............................................................... 7.3 QDAS and bended readîng .......................................................................... 7.4 QDAS, quaîtatîve content anaysîs, and bîg data .......................................... 7.5 Beyond tradîtîona databases ..........................................................................

45 45 47 47 49 50 52 53 54 55

57 58 64 66 69 71

72 72 75 83 84

87 87 88 92 95 96

-Chapter 8 |he nîtty grîtty: Bîg data înfrastructure .................................... 8.1 Managîng te unmanageabe ......................................................................... 8.2 Bîg data systems ............................................................................................ 8.3 Data generatîon ............................................................................................. 8.4 Data acquîsîtîon ............................................................................................ 8.5 Data storage .................................................................................................. 8.5.1 Dîstrîbuted ile systems .......................................................................... 8.5.2 NoSQL databases ................................................................................. 8.5.3 Programmîng models ............................................................................. 8.6 Data anaysîs ................................................................................................. 8.7 he Hadoop ecosystem .................................................................................. 8.7.1 Hadoop’s core components ...................................................................... 8.8 he Hadoop sotware stack ............................................................................ 8.9 An exampe o a Hadoop bîg data system ..................................................... 8.10 Commercîa bîg data systems and coud bîg data ........................................... 8.11 A remînder ....................................................................................................

98 98 100 101 103 104 104 105 107 107 110 110 112 115 116 117

Leveragîng socîal scîentîic and umanîstîc Chapter 9 | expertîse în te world of (bîg) data scîence ................................ 118 9.1 Wat îs data scîence? ..................................................................................... 119 9.2 Marryîng (bîg) data scîence and te umanîtîes and socîa scîences ............... 124

An example: Bîg data analysîs în te Chapter 10 | umanîtîes în Sout Afrîca ........................................................ 126 10.1 ïntroductîon .................................................................................................. 126 10. 2 ïdentîyîng te probem ................................................................................. 126 10. 3 Data gaterîng ............................................................................................... 127 10. 4 Text pre-processîng ........................................................................................ 131 10.5 Perormîng anaytîcs on te data .................................................................... 132 10.5.1 Introductîon to sentîment analysîs ........................................................132 10.5.2 Applyîng sentîment analysîs .................................................................133 10.5.3 Sentîment analysîs results .....................................................................134 10.6 Vîsuaîsîng te resuts .................................................................................... 135 10.7 Concusîon ................................................................................................... 140

 141 The last word ................................................................................................. References ................................................................................................. 142 Index ................................................................................................. 187

List of figures

Fîgure 1.1: Mîestones în te era o bîg data sînce te întroductîon o te îrst commercîa mîcroprocessor .............................. 15 Fîgure 3.1: Caî and Zu’s (2015:7) quaîty assessment process ................................ 33 Fîgure 5.1: Cape Town’s average maxîmum temperatures în degrees Cesîus .................................................................................. 60 Fîgure 5.2: Semantîcay încongruent coour coîces to represent temperatures ...................................................................... 60 Fîgure 5.3: Te Stroop eect ................................................................................... 60 Fîgure 5.4: Gestat prîncîpe o sîmîarîty ................................................................ 62 Fîgure 5.5: Gestat prîncîpe o proxîmîty ............................................................... 62 Fîgure 5.6: Gestat prîncîpe o encosure ................................................................ 62 Fîgure 5.7: Exampe o anaogîca reasonîng (adapted rom Daugerty & Mentzer 2008:10) ..................................... 64 Fîgure 5.8: Jon Snow’s map o coera outbreaks în London (drawn by Snow cîrca 1854, and taken rom Stamp’s (1964)Te geograpy o lîe and deat) ...................................... 68 Fîgure 5.9: Jon Snow’s map reînvîsaged (Drucker 2011:19, wît credît to Xárene Eskandar or te grapîc) ..................................... 68 Fîgure 7.1: An exampe o a word coud rom Sout Arîca’s Lîe Esîdîmenî Arbîtratîon Hearîngs, 24 and 25 January 2018 .............. 90 Fîgure 8.1: Te Hadoop ecosystem (adapted rom Hu, Wen, Cau & Lî 2014:678) ........................................................................... 112 Fîgure 8.2: A bîg data system (adapted rom ïbarra 2012:3) .................................... 115 Fîgure 9.1: Te skîs and knowedge o a data scîentîst ........................................... 122 Fîgure 9.2: Te varîous tasks perormed by a data scîentîst ..................................... 122 Fîgure 10.1: A comparîson o sentîment cassîîers .................................................... 135 Fîgure 10.2: Tweets about te Arîkaner by country ................................................. 136 Fîgure 10.3: Exampes o negatîve tweets .................................................................. 137 Fîgure 10.4: Googe ïmage searc o ‘Sout Arîcan squatter camps’ ........................ 138 Fîgure 10.5: Negatîve tweets rom wîtîn Sout Arîca ............................................. 139

List of tables

Tabe 2.1: Two approaces to bîg data anaysîs (O’Suîvan 2017:15) ..................... 28 Tabe 3.1: Caî and Zu’s (2015:5) bîg data quaîty ramework ............................... 32 Tabe 4.1: Measurîng etîcs varîatîon în dîgîta researc (Jang & Caîngam 2012:75) ............................................................... 51 Tabe 10.1: Te use o te word ‘Arîkaner’ în some sampe anguages ...................... 129 Tabe 10.2: Language dîstrîbutîon o tweets ............................................................. 130 Tabe 10.3: Resuts o te sentîment anaysîs (wîtout re-tweets, n = 4505) ............. 134

Acknowledgements

We woud îke to express a specîa tanks o gratîtude to te Unîversîty o te Free State or an ïnterdîscîpînary Researc Grant tat enabed us to undertake te bîg data project. We are aso îndebted to Teo du Pessîs and to Lanî de Lange or teîr unwaverîng support over te ast ew years. Tank you too to our amîy members and rîends or teîr steadast encouragement.

I

Foreword

Te mantra o “Bîg Data” – te two capîta etters în tîs expressîon epîtomîse te massîve nature o te data învoved – as încreasîngy gaîned tractîon în recent years. Wen tîs mantra îrst appeared, ît ad te aura o academîc dîscîpînes, and amost every spere o busîness began dîppîng înto te sea o bîg data. Durîng tat traî-bazîng perîod, most scoars în te socîa and uman scîences, owîng argey to teîr academîc traînîng, et îmmobîîsed and tecnoogîcay apess at te prospect o bîg data. Tîs poînt îs poîgnanty contextuaîsed în te current book at te begînnîng o Capter 2: “Te very notîon o bîg data creepîng înto teîr researc spaces casts an întîmîdatîng sadow over tradîtîona umanîsts and socîa scîentîsts, wo may ear uman beavîour beîng reduced to mere matematîca modes” (p. 19). Muc înormatîon about te atter woud emerge rom te ïnternet and rom bîg commercîa pubîsîng ouses as îs stî te case even now. Agaîn, durîng tat perîod, tere was not te sîgtest cance tat any scoar rom tese two cognate scîences, east o a scoars rom te Sout Arîcan context, woud ever dabbe în te nascent îed o bîg data. But not anymore. Tîs îs te background agaînst wîc te current book, apty tîted, “Reînventîng te Socîa Scîentîst and Humanîst în te Era o Bîg Data: A Perspectîve rom Sout Arîcan Scoars”, soud be vîewed. Not ony îs tîs book’s tîte apposîte, but te puttîng togeter o te book îtse îs a îttîng and wecome scoary event în te Sout Arîcan îger educatîon ecosystem, and more so gîven te reated mantra o te Fourt ïndustrîa Revoutîon (4ïR) – o wîc bîg data îs an întegra part (see Caka 2019; Caka ortcomîng) – wîc îs gaînîng currency în te Sout Arîcan îger educatîon system. Gîven te poînts îgîgted above, a pertînent questîon to pose îs: wat roe does bîg data ave to pay în te socîa and uman scîences? Beore devîng înto and provîdîng a bespoke answer tat te book attempts to oer, ï venture to say tat bîg data as a roe to pay în every aspect o te socîa and uman scîences. ïn te maîn, te book îs “about bîg data aîmed specîîcay at academîcs în te umanîtîes and socîa scîences” (p. 1) and îs wrîtten by tree scoars rom Sout Arîca, and dare ï add, by tree scoars rom te goba Sout. Overa, te book boasts ten capters wose owdown as been eoquenty captured în te ïntroductîon. One o te teîng poînts o te book îs îts bruta onesty about te act tat despîte concerted eorts by Sout Arîcan scoars to create a nexus between bîg data and te dîgîta umanîtîes (DH), te appîcatîons o bîg data în te “tradîtîona umanîtîes and socîa scîences” (p. 3) are, at best, ew and ar between, and at worst, “an aîen penomenon at oca unîversîtîes” (p. 3). ïn act, argues te book, at tese unîversîtîes, bîg data anaytîcs îs amost te excusîve preserve o computer scîence dîscîpînes.

III