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SUMMARY:Jessica Pidoux's Public Defense PhD Thesis “Online Dating Quanti
 fication Practices: A Human-Machine Learning Process”
DTSTART:20210916T170000
DTEND:20210916T190000
DTSTAMP:20260405T002111Z
UID:77187d434078aef5fbd90d5603eb208e99de6632335d112c39d1f731
CATEGORIES:Conferences - Seminars
DESCRIPTION:Jessica Pidoux\n**** Event limited to 150 attendees on-site. T
 o register to the on-site event or to receive the zoom link please fill in
  this form: https://bit.ly/3CZSnRV or click on the right link "Registratio
 n"\n\nThe phenomenon of online dating via web and mobile phone application
 s involves several actors: graphical interfaces\, developers\, algorithmic
  systems for user matching\, and users. These actors have been studied in 
 parallel by the social sciences and by computer science. While computer sc
 ience research focuses on the design of more efficient recommendation syst
 ems and on the analysis of behavioral patterns\, research in the humanitie
 s and social sciences is interested in the effects of platforms and their 
 computational practices on the formation of affective relationships.\n\nTh
 is thesis explores how the mediation relations between the different actor
 s of dating help to commonly establish the human-machine communication. Ev
 ery actor learns in his own way a new conception of dating\, mirroring the
  machine’s algorithmic efficiency. The study of these new dynamics of co
 mmunication is bound to become key to software and algorithmic studies\, f
 or which the human-machine relationship has hitherto remained invisible in
  the study of dating platforms\, or incomplete when it comes to analyzing 
 developers' practices or algorithmic systems without taking into account t
 he final uses of the platforms.\n\nUsing a combination of qualitative and 
 quantitative concepts and methodologies from sociology and data science\, 
 this thesis contributes to the comparative analysis of four actors who hav
 e a profound impact on the experience of online dating: graphical interfac
 es\, developers\, algorithmic matchmaking systems\, and users. This is a s
 tudy of more than 320 variables collected in more than 20 dating applicati
 ons\, both English and French\, used in Switzerland and around the world. 
 These variables mediate between the design of profile recommendation syste
 ms and their final use for dating. A case study is conducted on the massiv
 ely-adopted dating application Tinder\, which acts as an industry innovato
 r\, capable of establishing design conventions (e.g.\, swiping). This thes
 is further investigates\, on the one hand\, the development practices of n
 ine founders and developers working in the industry\, and on the other han
 d\, the practices of 40 users of 26 different dating platforms.\n\nThe res
 ults show that the interaction with dating platforms involves a collective
  learning process of experi-mentation that allows for communication betwee
 n humans and machines. This fast-paced communication favors trial-and-erro
 r practices: actors either learn to adapt to the machine\, or on the contr
 ary to overcome it by accepting the mutual tolerance stemming from the unc
 ertainty of actions. Learning is possible through the supposed social conv
 entions of the actors\, some of which are made explicit and quantifiable t
 hrough graphical interfaces. First\, the interfaces present a conceptual m
 odel of use that makes explicit how to pre-sent the attractiveness of ones
 elf\, how to capture attention\, and how to measure the performance of thi
 s attractiveness that is constantly updated according to the dynamics of t
 he reputation economy. Secondly\, the practices of developers are guided b
 y the economic interests of dating companies. Dating is made quan-tifiable
  by imitating other successful applications\, exploiting the developers’
  personal experiences\, and by taking into account the machine’s capabil
 ities. This produces a reduction and a heteronormative standardi-zation of
  the conceptual models of dating. Thirdly\, the Tinder application case st
 udy shows how it favors an average ideal match modeled on sociodemographic
  factors through the swipe-imposed reduction of individ-ual preferences. F
 inally\, users learn to systematize their actions by mirroring the machine
  in order to fit into the reactive and competitive view they have of an ap
 plication. However\, there is also a personal reclaiming of the dating exp
 erience that allows individuals to step outside the machine-like framework
  of the applica-tion to foster other practices inherent in human capacitie
 s.\n\nThis thesis contributes to the described research field with a theor
 etical framework and empirical data which can guide future research on onl
 ine sociability at the intersection of computer and social sciences\, like
  in the digital humanities.
LOCATION:BCH 2201 https://plan.epfl.ch/?room==BCH%202201 https://epfl.zoom
 .us/j/63580996437
STATUS:CONFIRMED
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