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Collective Identity, Sexual Coercion, and Hegemonic Masculinity: Machine Learning and the Discourse of the Manosphere- [electronic resource]
Collective Identity, Sexual Coercion, and Hegemonic Masculinity: Machine Learning and the ...
Collective Identity, Sexual Coercion, and Hegemonic Masculinity: Machine Learning and the Discourse of the Manosphere- [electronic resource]

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자료유형  
 학위논문파일 국외
최종처리일시  
20240214101927
ISBN  
9798380723985
DDC  
396
저자명  
Julien, Chris.
서명/저자  
Collective Identity, Sexual Coercion, and Hegemonic Masculinity: Machine Learning and the Discourse of the Manosphere - [electronic resource]
발행사항  
[S.l.]: : The Pennsylvania State University., 2023
발행사항  
Ann Arbor : : ProQuest Dissertations & Theses,, 2023
형태사항  
1 online resource(131 p.)
주기사항  
Source: Dissertations Abstracts International, Volume: 85-05, Section: B.
주기사항  
Advisor: Felmlee, Diane.
학위논문주기  
Thesis (Ph.D.)--The Pennsylvania State University, 2023.
사용제한주기  
This item must not be sold to any third party vendors.
초록/해제  
요약In this project, I scrape and curate a novel dataset consisting of all posts and comments written in 58 subreddits belonging to the "Manosphere". This is a diffuse social movement concerned with what they believe is the rampant misandry of contemporary feminism. As such, it represents the first study to examine such a breadth of Manosphere discourse and analyze the themes therein, advancing our understanding of the prevalence and ubiquity of the themes of their discourse. This project uniquely captures the main forums in which the Manosphere congregated for over a decade and analyzes the texts therein with machine learning techniques. The aim of this project is to increase our knowledge about the Manosphere, with particular attention paid to: 1) the role of misogynist terrorist attacks and their influence on the Manosphere's discourse and collective identity, 2) the optimal machine learning techniques for analyzing nascent text-based communities, and 3) rhythms of sexual coercion that are normalized in a specific kind of Manosphere post, the "Field Report."In Chapter 2 of this project, I examine the discourse of the Manosphere on Reddit in the aftermath of violent misogynist attacks. Drawing on scholarship related to terrorist studies, collective identity, and precarious masculinity, I show how the main issues of the Manosphere, masculinity, and perceived misandry influence the beliefs and behaviors of the movement's members. I find that varying kinds of posts in the Manosphere forums are influenced differently by the violent attacks. Rather than one uniform pattern of influence following violent attacks, posts with relational topics, where members share interpersonal concerns, are stymied, while posts with ideological topics, where members refine and reiterate their core beliefs, receive a boost in frequency. I discuss the implications of this finding in light of the movement's collective identity.In Chapter 3, I evaluate several different machine learning techniques in the task of predicting popular and controversial content on Reddit. These data contain a class imbalance, common for many text datasets. As such, it advances our understanding of best practices for evaluating small textual datasets with a class imbalance. This is relevant as new communities take root in digital spaces and forums akin to subreddits. I also overview several common metrics for evaluating machine learners for class imbalance prediction tasks, finding balanced accuracy to be most successful.In Chapter 4, I analyze a specific kind of post from the Pick-Up Artist and Seduction communities within the Manosphere: "Field Reports". While many field reports detail consensual interactions, some describe an ebb and flow of resistance to sexual escalation and subsequent persistence even in spite of that resistance. These patterns that are legitimated through the encouraging comments of Manosphere members reify hegemonic masculinity and reproduce gender inequality in contemporary society. I analyze these findings in light of the Traditional Sexual Script, which undergirds the beliefs of these subsets of the Manosphere.
일반주제명  
Feminism.
일반주제명  
Scripts.
일반주제명  
Internet.
일반주제명  
Terrorism.
일반주제명  
Ideology.
일반주제명  
Verbal communication.
일반주제명  
Text analysis.
일반주제명  
Gender.
일반주제명  
Society.
일반주제명  
Violence.
일반주제명  
Community.
일반주제명  
Brainwashing.
일반주제명  
Misogyny.
일반주제명  
Women.
일반주제명  
Mass murders.
일반주제명  
Inequality.
일반주제명  
Social activism.
일반주제명  
Semantics.
일반주제명  
Masculinity.
일반주제명  
Suicides & suicide attempts.
일반주제명  
Womens studies.
일반주제명  
Social research.
일반주제명  
Gender studies.
일반주제명  
Sexuality.
키워드  
Manosphere
키워드  
Rampant misandry
키워드  
Machine learning
키워드  
Field Report
키워드  
Collective identity
기타저자  
The Pennsylvania State University.
기본자료저록  
Dissertations Abstracts International. 85-05B.
기본자료저록  
Dissertation Abstract International
전자적 위치 및 접속  
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■040    ▼aMiAaPQ▼cMiAaPQ
■0820  ▼a396
■1001  ▼aJulien,  Chris.
■24510▼aCollective  Identity,  Sexual  Coercion,  and  Hegemonic  Masculinity:  Machine  Learning  and  the  Discourse  of  the  Manosphere▼h[electronic  resource]
■260    ▼a[S.l.]:▼bThe  Pennsylvania  State  University.  ▼c2023
■260  1▼aAnn  Arbor  :▼bProQuest  Dissertations  &  Theses,  ▼c2023
■300    ▼a1  online  resource(131  p.)
■500    ▼aSource:  Dissertations  Abstracts  International,  Volume:  85-05,  Section:  B.
■500    ▼aAdvisor:  Felmlee,  Diane.
■5021  ▼aThesis  (Ph.D.)--The  Pennsylvania  State  University,  2023.
■506    ▼aThis  item  must  not  be  sold  to  any  third  party  vendors.
■520    ▼aIn  this  project,  I  scrape  and  curate  a  novel  dataset  consisting  of  all  posts  and  comments  written  in  58  subreddits  belonging  to  the  "Manosphere".  This  is  a  diffuse  social  movement  concerned  with  what  they  believe  is  the  rampant  misandry  of  contemporary  feminism.  As  such,  it  represents  the  first  study  to  examine  such  a  breadth  of  Manosphere  discourse  and  analyze  the  themes  therein,  advancing  our  understanding  of  the  prevalence  and  ubiquity  of  the  themes  of  their  discourse.  This  project  uniquely  captures  the  main  forums  in  which  the  Manosphere  congregated  for  over  a  decade  and  analyzes  the  texts  therein  with  machine  learning  techniques.  The  aim  of  this  project  is  to  increase  our  knowledge  about  the  Manosphere,  with  particular  attention  paid  to:  1)  the  role  of  misogynist  terrorist  attacks  and  their  influence  on  the  Manosphere's  discourse  and  collective  identity,  2)  the  optimal  machine  learning  techniques  for  analyzing  nascent  text-based  communities,  and  3)  rhythms  of  sexual  coercion  that  are  normalized  in  a  specific  kind  of  Manosphere  post,  the  "Field  Report."In  Chapter  2  of  this  project,  I  examine  the  discourse  of  the  Manosphere  on  Reddit  in  the  aftermath  of  violent  misogynist  attacks.  Drawing  on  scholarship  related  to  terrorist  studies,  collective  identity,  and  precarious  masculinity,  I  show  how  the  main  issues  of  the  Manosphere,  masculinity,  and  perceived  misandry  influence  the  beliefs  and  behaviors  of  the  movement's  members.  I  find  that  varying  kinds  of  posts  in  the  Manosphere  forums  are  influenced  differently  by  the  violent  attacks.  Rather  than  one  uniform  pattern  of  influence  following  violent  attacks,  posts  with  relational  topics,  where  members  share  interpersonal  concerns,  are  stymied,  while  posts  with  ideological  topics,  where  members  refine  and  reiterate  their  core  beliefs,  receive  a  boost  in  frequency.  I  discuss  the  implications  of  this  finding  in  light  of  the  movement's  collective  identity.In  Chapter  3,  I  evaluate  several  different  machine  learning  techniques  in  the  task  of  predicting  popular  and  controversial  content  on  Reddit.  These  data  contain  a  class  imbalance,  common  for  many  text  datasets.  As  such,  it  advances  our  understanding  of  best  practices  for  evaluating  small  textual  datasets  with  a  class  imbalance.  This  is  relevant  as  new  communities  take  root  in  digital  spaces  and  forums  akin  to  subreddits.  I  also  overview  several  common  metrics  for  evaluating  machine  learners  for  class  imbalance  prediction  tasks,  finding  balanced  accuracy  to  be  most  successful.In  Chapter  4,  I  analyze  a  specific  kind  of  post  from  the  Pick-Up  Artist  and  Seduction  communities  within  the  Manosphere:  "Field  Reports".  While  many  field  reports  detail  consensual  interactions,  some  describe  an  ebb  and  flow  of  resistance  to  sexual  escalation  and  subsequent  persistence  even  in  spite  of  that  resistance.  These  patterns  that  are  legitimated  through  the  encouraging  comments  of  Manosphere  members  reify  hegemonic  masculinity  and  reproduce  gender  inequality  in  contemporary  society.  I  analyze  these  findings  in  light  of  the  Traditional  Sexual  Script,  which  undergirds  the  beliefs  of  these  subsets  of  the  Manosphere.
■590    ▼aSchool  code:  0176.
■650  4▼aFeminism.
■650  4▼aScripts.
■650  4▼aInternet.
■650  4▼aTerrorism.
■650  4▼aIdeology.
■650  4▼aVerbal  communication.
■650  4▼aText  analysis.
■650  4▼aGender.
■650  4▼aSociety.
■650  4▼aViolence.
■650  4▼aCommunity.
■650  4▼aBrainwashing.
■650  4▼aMisogyny.
■650  4▼aWomen.
■650  4▼aMass  murders.
■650  4▼aInequality.
■650  4▼aSocial  activism.
■650  4▼aSemantics.
■650  4▼aMasculinity.
■650  4▼aSuicides  &  suicide  attempts.
■650  4▼aWomens  studies.
■650  4▼aSocial  research.
■650  4▼aGender  studies.
■650  4▼aSexuality.
■653    ▼aManosphere
■653    ▼aRampant  misandry
■653    ▼aMachine  learning
■653    ▼aField  Report
■653    ▼aCollective  identity
■690    ▼a0344
■690    ▼a0800
■690    ▼a0211
■690    ▼a0453
■690    ▼a0733
■71020▼aThe  Pennsylvania  State  University.
■7730  ▼tDissertations  Abstracts  International▼g85-05B.
■773    ▼tDissertation  Abstract  International
■790    ▼a0176
■791    ▼aPh.D.
■792    ▼a2023
■793    ▼aEnglish
■85640▼uhttp://www.riss.kr/pdu/ddodLink.do?id=T16935398▼nKERIS▼z이  자료의  원문은  한국교육학술정보원에서  제공합니다.
■980    ▼a202402▼f2024

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